You Can Do It in SQL

•

0 j'aime•313 vues

Just because you can, doesn’t mean you should. But in this case, you definitely should! Learn how this one weird trick (Jinja templating) will supercharge your analytics workflows and help you do more, better, faster with SQL.

Données & analyses

Drew Banin (he/him)
Chief Product Ofﬁcer
2
mo’ data mo’ problems
@drewbanin on Twitter

● Open core data modeling software
● dbt provides a SQL-ﬁrst workﬂow for:
- Data modeling
- Data testing
- Data documentation
- And more!
You can learn more at getdbt.com
We’re the makers of dbt

Confessions & disclaimers
● I have never written a line of production Scala code in my life
● I have written a whole lot of SQL in my life
● People who tolerate Python packaging don’t get to complain about the JVM

I ❤ SQL
, Expressive
, Accessible
, Universal
, Declarative
, Thought provoking….?

I ❤ SQL
, Expressive - SQL reads more or less like a real sentence
, Accessible
, Universal
, Declarative
, Thought provoking….?

I ❤ SQL
, Expressive
, Accessible - Lots of people can learn in pretty quickly
, Universal
, Declarative
, Thought provoking….?

I ❤ SQL
, Expressive
, Accessible
, Universal - Most databases speak SQL with pretty consistent support
, Declarative
, Thought provoking….?

I ❤ SQL
, Expressive
, Accessible
, Universal
, Declarative - No moving parts, limited opportunities for the bad kind of magic
, Thought provoking….?

What: convert rows of data into columns of data
Why: to derive new metrics from existing rows in a table
Pivoting columns

What: convert rows of data into columns of data
Why: to derive new metrics from existing rows in a table
Pivoting columns
user_id event_name happened_at
1 signup 2021-01-01
1 purchase 2021-01-02
2 signup 2021-01-03
3 signup 2021-01-04
3 purchase 2021-01-05
1 purchase 2021-01-05

Unions
What: append tables together
Why: combine tables into a taller table

Unions
What: append tables together
Why: combine tables into a taller table
user_id event_name happened_at
1 signup 2021-01-01
1 purchase 2021-01-02
2 signup 2021-01-03
user_id event_name happened_at
1 app_install 2021-01-01
1 screen_view 2021-01-02
2 app_install 2021-01-03
web_events mobile_events

Unions
What: append tables together
Why: combine tables into a taller table
source user_id event_name happened_at
mobile 1 app_install 2021-01-01
mobile 1 screen_view 2021-01-02
mobile 2 app_install 2021-01-03
web 1 signup 2021-01-01
web 1 purchase 2021-01-02
web 2 signup 2021-01-03
all_events

Window functions
What: apply transformations over part of a table
Why: use cases abound!

Window functions
What: apply transformations over part of a table
Why: use cases abound!
user_id happened_at page_path
1 2021-01-01 /product
1 2021-01-02 /signup
2 2021-01-03 /app
pageviews

Helpful window functions
● ﬁrst_value
● last_value
● lag
● lead
● row_number
● rank
● sum
● min
● max
● avg
● count

SQL: The bad parts
● No “functions” for code reuse
● Lots of typing → lots of typos
● It might be the most copy/pasted language of all time!
○ Citation needed
● But…. what if we add templating?

● Yes!
● Accessibility is 🔑
● What happens when you teach people SQL?
○ Data (engineers|scientists|analysts) can collaborate in code
○ Non-engineers are empowered to understand business logic
○ More people can contribute!
You can do it in SQL… but should you?

What does the future hold?
● More data workloads will converge around SQL
● Databases will improve support for sophisticated SQL operations
- AI & ML?
- Streaming?
- ….?
● More roles will require SQL knowledge and aptitude
● More & better tools will exist to aid in writing and managing SQL

Contenu connexe

Tendances

Comcast is one of the leading providers of communications, entertainment, and cable products and services. At the heart of it is Comcast RDK providing the backbone of telemetry to the industry. RDK (Reference Design Kit) is pre-bundled opensource firmware for a complete home platform covering video, broadband and IoT devices. RDK team at Comcast analyzes petabytes of data, collected every 15 minutes from 70 million devices (video and broadband and IoT devices) installed in customer homes. They run ETL and aggregation pipelines and publish analytical dashboards on a daily basis to reduce customer calls and firmware rollout. The analysis is also used to calculate WIFI happiness index which is a critical KPI for Comcast customer experience. In addition to this, RDK team also does release tracking by analyzing the RDK firmware quality. SQL Analytics allows customers to operate a lakehouse architecture that provides data warehousing performance at data lake economics for up to 4x better price/performance for SQL workloads than traditional cloud data warehouses. We present the results of the “Test and Learn” with SQL Analytics and the delta engine that we worked in partnership with the Databricks team. We present a quick demo introducing the SQL native interface, the challenges we faced with migration, The results of the execution and our journey of productionizing this at scale.

SQL Analytics Powering Telemetry Analysis at Comcast

Databricks

"Combining Databricks, the unified analytics platform with Snowflake, the data warehouse built for the cloud is a powerful combo. Databricks offers the ability to process large amounts of data reliably, including developing scalable AI projects. Snowflake offers the elasticity of a cloud-based data warehouse that centralizes the access to data. Databricks brings the unparalleled utility of being based on a mature distributed big data processing and AI-enabled tool to the table, capable of integrating with nearly every technology, from message queues (e.g. Kafka) to databases (e.g. Snowflake) to object stores (e.g. S3) and AI tools (e.g. Tensorflow). Key Takeaways: How Databricks & Snowflake work; Why they're so powerful; How Databricks + Snowflake symbiotically catalyze analytics and AI initiatives"

Databricks + Snowflake: Catalyzing Data and AI Initiatives

Databricks

Quby, an Amsterdam-based technology company, offers solutions to empower homeowners to stay in control of their electricity, gas and water usage. Using Europe’s largest energy dataset, consisting of petabytes of IoT data, the company has developed AI powered products that are used by hundreds of thousands of users on a daily basis. Delta Lake ensures the quality of incoming records though schema enforcement and evolution. But it is the Data Engineers role to check whether the expected data is ingested in to the Delta Lake at the right time with expected metrics so that downstream processes will function their duties. Re-training models and serving on the fly might go wrong unless we put the right monitoring infrastructure too.

Monitoring Half a Million ML Models, IoT Streaming Data, and Automated Qualit...

Databricks

Modern Machine Learning (ML) represents everything as vectors, from documents, to videos, to user behavior. This representation makes it possible to accurately search, retrieve, rank, and classify different items by similarity and relevance. Running real-time applications that rely on large numbers of such high dimensional vectors requires a new kind of data infrastructure. In this talk we will discuss the need for such infrastructure, the algorithmic and engineering challenges in working with vector data at scale, and open problems we still have no adequate solutions for. Time permitting, I will introduce Pinecone as a solution to some of these challenges.

The Rise of Vector Data

Databricks

Accelerating Data Science Initiatives with Databricks’ Rapid SQL Analytics and Privacera’s Centralized Data Access Governance. Databricks’ SQL Analytics helps data teams consolidate and simplify their data architectures. With SQL Analytics, data teams can perform BI and SQL workloads on the same multi-cloud lakehouse architecture enabling data scientists to perform advanced analytics on unstructured and large-scale data. This session will explore how Privacera’s advanced security, privacy, and governance capabilities seamlessly integrate with Databricks’ unified SQL Analytics approach to provide single pane visibility of data analytics from a centralized location. Attendees will learn how to: Rapidly access data to run high-fidelity analytics Implement a fully secure solution that ensures productivity, while controlling data access at fine-grained levels (row, column, and file) Easily enable consistent access policies across all systems and applications Support true data transparency across enterprises Comply with stringent industry and privacy regulations like GDPR, LGPD, HIPAA, CCPA, PCI DSS, RTBF, and more with rich auditing and reporting

Accelerate Data Science Initiatives: Databricks & Privacera

Databricks

Here at T-Mobile when a new account is opened, there are fraud checks that occur both pre- and post-activation. Fraud that is missed has a tendency of falling into first payment default, looking like a delinquent new account. The objective of this project was to investigate newly created accounts headed towards delinquency to find additional fraud. For the longevity of this project we wanted to implement it as an end to end automated solution for building and productionizing models that included multiple modeling techniques and hyper parameter tuning. We wanted to utilize MLflow for model comparison, graduation to production, and parallel hyper parameter tuning using Hyperopt. To achieve this goal, we created multiple machine learning notebooks where a variety of models could be tuned with their specific parameters. These models were saved into a training MLflow experiment, after which the best performing model for each model notebook was saved to a model comparison MLflow experiment. In the second experiment the newly built models would be compared with each other as well as the models currently and previously in production. After the best performing model was identified it was then saved to the MLflow Model Registry to be graduated to production. We were able to execute the multiple notebook solution above as part of an Azure Data Factory pipeline to be regularly scheduled, making the model building and selection a completely hand off implementation. Every data science project has its nuances; the key is to leverage available tools in a customized approach that fit your needs. We are hoping to provide the audience with a view into our advanced and custom approach of utilizing the MLflow infrastructure and leveraging these tools through automation.

Advanced Model Comparison and Automated Deployment Using ML

Databricks

At Schiphol airport we run a lot of mission critical machine learning models in production, ranging from models that predict passenger flow to computer vision models that analyze what is happening around the aircraft. Especially now in times of Covid it is paramount for us to be able to quickly iterate on these models by implementing new features, retraining them to match the new dynamics and above all to monitor them actively to see if they still fit the current state of affairs. To achieve those needs we rely on MLFlow but have also integrated that with many of our other systems. So have we written Airflow operators for MLFlow to ease the retraining of our models, have we integrated MLFlow deeply with our CI pipelines and have we integrated it with our model monitoring tooling. In this talk we will take you through the way we rely on MLFlow and how that enables us to release (sometimes) multiple versions of a model per week in a controlled fashion. With this set-up we are achieving the same benefits and speed as you have with a traditional software CI pipeline.

Consolidating MLOps at One of Europe’s Biggest Airports

Databricks

Leveraging Apache Spark to Develop AI-Enabled Products and Services at Bosch

Databricks

It has long been said that business intelligence needs a relational warehouse, but that view is changing. With the Lakehouse architecture being shouted from the rooftops, Databricks have released SQL Analytics, an alternative workspace for SQL-savvy users to interact with an analytics-tuned cluster. But how does it work? Where do you start? What does a typical Data Analyst’s user journey look like with the tool? This session will introduce the new workspace and walk through the various key features – how you set up a SQL Endpoint, the query workspace, creating rich dashboards and connecting up BI tools such as Microsoft Power BI. If you’re truly trying to create a Lakehouse experience that satisfies your SQL-loving Data Analysts, this is a tool you’ll need to be familiar with and include in your design patterns, and this session will set you on the right path.

Getting Started with Databricks SQL Analytics

Databricks

At Adobe Experience Platform, we ingest TBs of data every day and manage PBs of data for our customers as part of the Unified Profile Offering. At the heart of this is a bunch of complex ingestion of a mix of normalized and denormalized data with various linkage scenarios power by a central Identity Linking Graph. This helps power various marketing scenarios that are activated in multiple platforms and channels like email, advertisements etc. We will go over how we built a cost effective and scalable data pipeline using Apache Spark and Delta Lake and share our experiences. What are we storing? Multi Source – Multi Channel Problem Data Representation and Nested Schema Evolution Performance Trade Offs with Various formats Go over anti-patterns used (String FTW) Data Manipulation using UDFs Writer Worries and How to Wipe them Away Staging Tables FTW Datalake Replication Lag Tracking Performance Time!

Massive Data Processing in Adobe Using Delta Lake

Databricks

Building ML models is a time consuming endeavor that requires a thorough understanding of feature engineering, selecting useful features, choosing an appropriate algorithm, and performing hyper-parameter tuning. Extensive experimentation is required to arrive at a robust and performant model. Additionally, keeping track of the models that have been developed and deployed may be complex. Solving these challenges is key for successfully implementing end-to-end ML pipelines at scale. In this talk, we will present a seamless integration of automated machine learning within a Databricks notebook, thus providing a truly unified analytics lifecycle for data scientists and business users with improved speed and efficiency. Specifically, we will show an app that generates and executes a Databricks notebook to train an ML model with H2O’s Driverless AI automatically. The resulting model will be automatically tracked and managed with MLflow. Furthermore, we will show several deployment options to score new data on a Databricks cluster or with an external REST server, all within the app.

Accelerate Your ML Pipeline with AutoML and MLflow

Databricks

Many had dubbed 2020 as the decade of data. This is indeed an era of data zeitgeist. From code-centric software development 1.0, we are entering software development 2.0, a data-centric and data-driven approach, where data plays a central theme in our everyday lives. As the volume and variety of data garnered from myriad data sources continue to grow at an astronomical scale and as cloud computing offers cheap computing and data storage resources at scale, the data platforms have to match in their abilities to process, analyze, and visualize at scale and speed and with ease — this involves data paradigm shifts in processing and storing and in providing programming frameworks to developers to access and work with these data platforms. In this talk, we will survey some emerging technologies that address the challenges of data at scale, how these tools help data scientists and machine learning developers with their data tasks, why they scale, and how they facilitate the future data scientists to start quickly. In particular, we will examine in detail two open-source tools MLflow (for machine learning life cycle development) and Delta Lake (for reliable storage for structured and unstructured data). Other emerging tools such as Koalas help data scientists to do exploratory data analysis at scale in a language and framework they are familiar with as well as emerging data + AI trends in 2021. You will understand the challenges of machine learning model development at scale, why you need reliable and scalable storage, and what other open source tools are at your disposal to do data science and machine learning at scale.

The Future of Data Science and Machine Learning at Scale: A Look at MLflow, D...

Databricks

"We all know the unprecedented potential impact for Machine Learning. But how do you take advantage of the myriad of data and ML tools now available? How do you streamline processes, speed up discovery, share knowledge, and scale up implementations for real-life scenarios? In this talk, we'll cover some of the latest innovations brought into the Databricks Unified Analytics Platform for Machine Learning. In particular we will show you how to: - Get started quickly using the Databricks Runtime for Machine Learning, that provides pre-configured Databricks clusters including the most popular ML frameworks and libraries, Conda support, performance optimizations, and more. - Get started with most popular Deep Learning frameworks within a few minutes and go deep with state of the art model DL diagnostics tools. - Scale up Deep Learning training workloads from a single machine to large clusters for the most demanding applications using the new HorovodRunner with ease. - How all of these ML frameworks get exposed to large and distributed data using Databricks Runtime for Machine Learning."

Accelerating Machine Learning on Databricks Runtime

Databricks

The volume of available data is growing by the second (to an estimated 175 zetabytes by 2025), and it is becoming increasingly granular in its information. With that change every organization is moving towards building a data driven culture. We at Northwestern Mutual share similar story of driving towards making data driven decisions to improve both efficiency and effectiveness. Legacy system analysis revealed bottlenecks, excesses, duplications etc. Based on ever growing need to analyze more data our BI Team decided to make a move to more modern, scalable, cost effective data platform. As a financial company, data security is as important as ingestion of data. In addition to fast ingestion and compute we would need a solution that can support column level encryption, Role based access to different teams from our datalake. In this talk we describe our journey to move 100’s of ELT jobs from current MSBI stack to Databricks and building a datalake (using Lakehouse). How we reduced our daily data load time from 7 hours to 2 hours with capability to ingest more data. Share our experience, challenges, learning, architecture and design patterns used while undertaking this huge migration effort. Different sets of tools/frameworks built by our engineers to help ease the learning curve that our non-Apache Spark engineers would have to go through during this migration. You will leave this session with more understand on what it would mean for you and your organization if you are thinking about migrating to Apache Spark/Databricks.

Northwestern Mutual Journey – Transform BI Space to Cloud

Databricks

The BigDL framework scales deep learning for large data sets using Apache Spark. However there is significant scheduling overhead from Spark when running BigDL at large scale. In this talk we propose a new parameter manager implementation that along with coarse-grained scheduling can provide significant speedups for deep learning models like Inception, VGG etc. Aggregation functions like reduce or treeReduce that are used for parameter aggregation in Apache Spark (and the original MapReduce) are slow as the centralized scheduling and driver network bandwidth become a bottleneck especially in large clusters. To reduce the overhead of parameter aggregation and allow for near-linear scaling, we introduce a new AllReduce operation, a part of the parameter manager in BigDL which is built directly on top of the BlockManager in Apache Spark. AllReduce in BigDL uses a peer-to-peer mechanism to synchronize and aggregate parameters. During parameter synchronization and aggregation, all nodes in the cluster play the same role and driver’s overhead is eliminated thus enabling near-linear scaling. To address the scheduling overhead we use Drizzle, a recently proposed scheduling framework for Apache Spark. Currently, Spark uses a BSP computation model, and notifies the scheduler at the end of each task. Invoking the scheduler at the end of each task adds overheads and results in decreased throughput and increased latency. Drizzle introduces group scheduling, where multiple iterations (or a group) of iterations are scheduled at once. This helps decouple the granularity of task execution from scheduling and amortizes the costs of task serialization and launch. Finally we will present results from using the new AllReduce operation and Drizzle on a number of common deep learning models including VGG and Inception. Our benchmarks run on Amazon EC2 and Google DataProc will show the speedups and scalability of our implementation.

Accelerating Deep Learning Training with BigDL and Drizzle on Apache Spark wi...

Databricks

Databricks Overview for MLOps

Databricks

Democratizing Data

Databricks

In Salesforce, our customers are using High Velocity Sales to intelligently convert leads and create new opportunities. To support it, we built the engagement activity platform to automatically capture and store user engagement activities using delta lake, which is one of the key components supporting Einstein Analytics for creating powerful reports and dashboards and Sales Cloud Einstein for training machine learning models. To convert leads and create new opportunities requires our engagement activity delta lake to handle data mutations at scale. In this presentation, we will share the challenges and learnings from building a high performance mutable data lake using delta lake which will include: Independent Stream Process to Support Engagement Data Life cycle Downstream Incremental Read High Throughput Transactions in Engagement ID Mutation Detect Cascading ID Mutation with Graph Data Skipping and Z-Order with I/O Pruning High Data Consistency and Integrity Exact Once Write Across Tables Global Synchronization and Ordering

A High Performance Mutable Engagement Activity Delta Lake

Databricks

As organizations launch complex multi-modal models into human-facing applications, data governance becomes both increasingly important, and difficult. Specifically, monitoring the underlying ML models for accuracy and reliability becomes a critical component of any data governance system. When complex data, such as image, text and video, is involved, monitoring model performance is particularly problematic given the lack of semantic information. In industries such as health care and automotive, fail-safes are needed for compliant performance and safety but access to validation data is in short supply, or in some cases, completely absent. However, to date, there have been no widely accessible approaches for monitoring semantic information in a performant manner. In this talk, we will provide an overview of approximate statistical methods, how they can be used for monitoring, along with debugging data pipelines for detecting concept drift and out-of-distribution data in semantic-full data, such as images. We will walk through an open source library, whylogs, which combines Apache Spark and novel approaches to semantic data sketching. We will conclude with practical examples equipping ML practitioners with monitoring tools for computer vision, and semantic-full models.

Semantic Image Logging Using Approximate Statistics & MLflow

Databricks

Simplifying Disaster Recovery with Delta Lake

Databricks

Tendances (20)

SQL Analytics Powering Telemetry Analysis at Comcast

Databricks + Snowflake: Catalyzing Data and AI Initiatives

Monitoring Half a Million ML Models, IoT Streaming Data, and Automated Qualit...

The Rise of Vector Data

Accelerate Data Science Initiatives: Databricks & Privacera

Advanced Model Comparison and Automated Deployment Using ML

Consolidating MLOps at One of Europe’s Biggest Airports

Leveraging Apache Spark to Develop AI-Enabled Products and Services at Bosch

Getting Started with Databricks SQL Analytics

Massive Data Processing in Adobe Using Delta Lake

Accelerate Your ML Pipeline with AutoML and MLflow

The Future of Data Science and Machine Learning at Scale: A Look at MLflow, D...

Accelerating Machine Learning on Databricks Runtime

Northwestern Mutual Journey – Transform BI Space to Cloud

Accelerating Deep Learning Training with BigDL and Drizzle on Apache Spark wi...

Databricks Overview for MLOps

Democratizing Data

A High Performance Mutable Engagement Activity Delta Lake

Semantic Image Logging Using Approximate Statistics & MLflow

Simplifying Disaster Recovery with Delta Lake

Similaire à You Can Do It in SQL

** This workshop was conducted in the Hong Kong Open Source Conference 2017 ** Excel formulas can be quite slow when you're processing data files with thousands of rows. It's also especially difficult to maintain the files when you have some messy mixture of VLOOKUPs, Pivot Tables, Macros and VBAs. In this interactive workshop targeted for non-coders, we will make use of SQLite, a very lightweight and portable open source database library, to perform some simple and repeatable data analysis on large datasets that are publicly available. We will also explore what you can further do with the data by using some powerful extensions of SQLite. While SQLite may not totally replace Excel in many ways, after the workshop you will find that it can improve your work efficiency and make your life much easier in so many use cases! Who should attend this workshop? - If you're frustrated with the slow performance of Excel formulas when dealing with large datasets in your daily work - No coding experience is required

Waiting too long for Excel's VLOOKUP? Use SQLite for simple data analysis!

Amanda Lam

This fourth meetup will present good practices and tips about deploying a recommender system in production. We will cover a wide range of the day-to-day of machine learning engineers and devops: from test-driven development to continuous integration and cloud architecture design. We will see how machine learning and recommender system in particular differ from traditional software development, and how this impacts deployment pipelines, and what tools you can use to solve this problem.

Recommender Systems from A to Z – Real-Time Deployment

Crossing Minds

Move a successful onpremise oltp application to the cloud

Ike Ellis

Daniel Egan Msdn Tech Days Oc Day2

Daniel Egan

Shant Hovsepian, CTO of Arcadia Data and a panel of experts details the trade-offs between a number of architectures that provide self-service access to data, and industry researcher Mark Madsen discusses the pros and cons of architectures, deployment strategies, and customer examples of BI on big data. Topics include: - Traditional BI platforms based on semantic layers and SQL/MDX generation - Server and desktop BI tools based on direct mapping of data - Distributed BI platforms (e.g., MPP and data native) - OLAP- and SQL-on-Hadoop engines

BI on Big Data Presentation

Arcadia Data

E-Commerce Product Rating Based on Customer Review

IRJET Journal

Mr bi amrp

renjan131

What's new in VisibleThread 3.0

VisibleThread

Project report

MansiKulkarni18

Evolutionary db development

Open Party

Dax & sql in power bi

Berkovich Consulting

A case for teaching SQL to scientists

dhalperi

Map Reduce amrp presentation

renjan131

Library management system project

AJAY KUMAR

CV_Anantha_Krishnan_Senior_Oracle_ETL_PLSQL

Anantha TMS

Agile Data Warehousing

Davide Mauri

Ganesh profile

Ganesh Kamble

Learn how to leverage new workflow management tools to simplify complex data pipelines and ETL jobs spanning multiple systems. In this technical deep dive from Treasure Data, company founder and chief architect walks through the codebase of DigDag, our recently open-sourced workflow management project. He shows how workflows can break large, error-prone SQL statements into smaller blocks that are easier to maintain and reuse. He also demonstrates how a system using ‘last good’ checkpoints can save hours of computation when restarting failed jobs and how to use standard version control systems like Github to automate data lifecycle management across Amazon S3, Amazon EMR, Amazon Redshift, and Amazon Aurora. Finally, you see a few examples where SQL-as-pipeline-code gives data scientists both the right level of ownership over production processes and a comfortable abstraction from the underlying execution engines. This session is sponsored by Treasure Data. AWS Competency Partner

AWS re:Invent 2016: Automating Workflows for Analytics Pipelines (DEV401)

Amazon Web Services

Real-life Customer Cases using Data Vault and Data Warehouse Automation

Patrick Van Renterghem

Ebook9

kaashiv1

Similaire à You Can Do It in SQL (20)

Waiting too long for Excel's VLOOKUP? Use SQLite for simple data analysis!

Recommender Systems from A to Z – Real-Time Deployment

Move a successful onpremise oltp application to the cloud

Daniel Egan Msdn Tech Days Oc Day2

BI on Big Data Presentation

E-Commerce Product Rating Based on Customer Review

Mr bi amrp

What's new in VisibleThread 3.0

Project report

Evolutionary db development

Dax & sql in power bi

A case for teaching SQL to scientists

Map Reduce amrp presentation

Library management system project

CV_Anantha_Krishnan_Senior_Oracle_ETL_PLSQL

Agile Data Warehousing

Ganesh profile

AWS re:Invent 2016: Automating Workflows for Analytics Pipelines (DEV401)

Real-life Customer Cases using Data Vault and Data Warehouse Automation

Ebook9

Plus de Databricks

DW Migration Webinar-March 2022.pptx

Databricks

The world of data architecture began with applications. Next came data warehouses. Then text was organized into a data warehouse. Then one day the world discovered a whole new kind of data that was being generated by organizations. The world found that machines generated data that could be transformed into valuable insights. This was the origin of what is today called the data lakehouse. The evolution of data architecture continues today. Come listen to industry experts describe this transformation of ordinary data into a data architecture that is invaluable to business. Simply put, organizations that take data architecture seriously are going to be at the forefront of business tomorrow. This is an educational event. Several of the authors of the book Building the Data Lakehouse will be presenting at this symposium.

Data Lakehouse Symposium | Day 1 | Part 1

Databricks

Data Lakehouse Symposium | Day 1 | Part 2

Databricks

Data Lakehouse Symposium | Day 2

Databricks

Data Lakehouse Symposium | Day 4

Databricks

In this session, learn how to quickly supplement your on-premises Hadoop environment with a simple, open, and collaborative cloud architecture that enables you to generate greater value with scaled application of analytics and AI on all your data. You will also learn five critical steps for a successful migration to the Databricks Lakehouse Platform along with the resources available to help you begin to re-skill your data teams.

5 Critical Steps to Clean Your Data Swamp When Migrating Off of Hadoop

Databricks

Bad data leads to bad decisions and broken customer experiences. Organizations depend on complete and accurate data to power their business, maintain efficiency, and uphold customer trust. With thousands of datasets and pipelines running, how do we ensure that all data meets quality standards, and that expectations are clear between producers and consumers? Investing in shared, flexible components and practices for monitoring data health is crucial for a complex data organization to rapidly and effectively scale. At Zillow, we built a centralized platform to meet our data quality needs across stakeholders. The platform is accessible to engineers, scientists, and analysts, and seamlessly integrates with existing data pipelines and data discovery tools. In this presentation, we will provide an overview of our platform’s capabilities, including: Giving producers and consumers the ability to define and view data quality expectations using a self-service onboarding portal Performing data quality validations using libraries built to work with spark Dynamically generating pipelines that can be abstracted away from users Flagging data that doesn’t meet quality standards at the earliest stage and giving producers the opportunity to resolve issues before use by downstream consumers Exposing data quality metrics alongside each dataset to provide producers and consumers with a comprehensive picture of health over time

Democratizing Data Quality Through a Centralized Platform

Databricks

Data scientists face numerous challenges throughout the data science workflow that hinder productivity. As organizations continue to become more data-driven, a collaborative environment is more critical than ever — one that provides easier access and visibility into the data, reports and dashboards built against the data, reproducibility, and insights uncovered within the data.. Join us to hear how Databricks’ open and collaborative platform simplifies data science by enabling you to run all types of analytics workloads, from data preparation to exploratory analysis and predictive analytics, at scale — all on one unified platform.

Learn to Use Databricks for Data Science

Databricks

Application performance monitoring (APM) has become the cornerstone of software engineering allowing engineering teams to quickly identify and remedy production issues. However, as the world moves to intelligent software applications that are built using machine learning, traditional APM quickly becomes insufficient to identify and remedy production issues encountered in these modern software applications. As a lead software engineer at NewRelic, my team built high-performance monitoring systems including Insights, Mobile, and SixthSense. As I transitioned to building ML Monitoring software, I found the architectural principles and design choices underlying APM to not be a good fit for this brand new world. In fact, blindly following APM designs led us down paths that would have been better left unexplored. In this talk, I draw upon my (and my team’s) experience building an ML Monitoring system from the ground up and deploying it on customer workloads running large-scale ML training with Spark as well as real-time inference systems. I will highlight how the key principles and architectural choices of APM don’t apply to ML monitoring. You’ll learn why, understand what ML Monitoring can successfully borrow from APM, and hear what is required to build a scalable, robust ML Monitoring architecture.

Why APM Is Not the Same As ML Monitoring

Databricks

Autonomy and ownership are core to working at Stitch Fix, particularly on the Algorithms team. We enable data scientists to deploy and operate their models independently, with minimal need for handoffs or gatekeeping. By writing a simple function and calling out to an intuitive API, data scientists can harness a suite of platform-provided tooling meant to make ML operations easy. In this talk, we will dive into the abstractions the Data Platform team has built to enable this. We will go over the interface data scientists use to specify a model and what that hooks into, including online deployment, batch execution on Spark, and metrics tracking and visualization.

The Function, the Context, and the Data—Enabling ML Ops at Stitch Fix

Databricks

In this talk, I will dive into the stage level scheduling feature added to Apache Spark 3.1. Stage level scheduling extends upon Project Hydrogen by improving big data ETL and AI integration and also enables multiple other use cases. It is beneficial any time the user wants to change container resources between stages in a single Apache Spark application, whether those resources are CPU, Memory or GPUs. One of the most popular use cases is enabling end-to-end scalable Deep Learning and AI to efficiently use GPU resources. In this type of use case, users read from a distributed file system, do data manipulation and filtering to get the data into a format that the Deep Learning algorithm needs for training or inference and then sends the data into a Deep Learning algorithm. Using stage level scheduling combined with accelerator aware scheduling enables users to seamlessly go from ETL to Deep Learning running on the GPU by adjusting the container requirements for different stages in Spark within the same application. This makes writing these applications easier and can help with hardware utilization and costs. There are other ETL use cases where users want to change CPU and memory resources between stages, for instance there is data skew or perhaps the data size is much larger in certain stages of the application. In this talk, I will go over the feature details, cluster requirements, the API and use cases. I will demo how the stage level scheduling API can be used by Horovod to seamlessly go from data preparation to training using the Tensorflow Keras API using GPUs. The talk will also touch on other new Apache Spark 3.1 functionality, such as pluggable caching, which can be used to enable faster dataframe access when operating from GPUs.

Stage Level Scheduling Improving Big Data and AI Integration

Databricks

In this talk, I would like to introduce an open-source tool built by our team that simplifies the data conversion from Apache Spark to deep learning frameworks. Imagine you have a large dataset, say 20 GBs, and you want to use it to train a TensorFlow model. Before feeding the data to the model, you need to clean and preprocess your data using Spark. Now you have your dataset in a Spark DataFrame. When it comes to the training part, you may have the problem: How can I convert my Spark DataFrame to some format recognized by my TensorFlow model? The existing data conversion process can be tedious. For example, to convert an Apache Spark DataFrame to a TensorFlow Dataset file format, you need to either save the Apache Spark DataFrame on a distributed filesystem in parquet format and load the converted data with third-party tools such as Petastorm, or save it directly in TFRecord files with spark-tensorflow-connector and load it back using TFRecordDataset. Both approaches take more than 20 lines of code to manage the intermediate data files, rely on different parsing syntax, and require extra attention for handling vector columns in the Spark DataFrames. In short, all these engineering frictions greatly reduced the data scientists’ productivity. The Databricks Machine Learning team contributed a new Spark Dataset Converter API to Petastorm to simplify these tedious data conversion process steps. With the new API, it takes a few lines of code to convert a Spark DataFrame to a TensorFlow Dataset or a PyTorch DataLoader with default parameters. In the talk, I will use an example to show how to use the Spark Dataset Converter to train a Tensorflow model and how simple it is to go from single-node training to distributed training on Databricks.

Simplify Data Conversion from Spark to TensorFlow and PyTorch

Databricks

There is no doubt Kubernetes has emerged as the next generation of cloud native infrastructure to support a wide variety of distributed workloads. Apache Spark has evolved to run both Machine Learning and large scale analytics workloads. There is growing interest in running Apache Spark natively on Kubernetes. By combining the flexibility of Kubernetes and scalable data processing with Apache Spark, you can run any data and machine pipelines on this infrastructure while effectively utilizing resources at disposal. In this talk, Rajesh Thallam and Sougata Biswas will share how to effectively run your Apache Spark applications on Google Kubernetes Engine (GKE) and Google Cloud Dataproc, orchestrate the data and machine learning pipelines with managed Apache Airflow on GKE (Google Cloud Composer). Following topics will be covered: – Understanding key traits of Apache Spark on Kubernetes- Things to know when running Apache Spark on Kubernetes such as autoscaling- Demonstrate running analytics pipelines on Apache Spark orchestrated with Apache Airflow on Kubernetes cluster.

Scaling your Data Pipelines with Apache Spark on Kubernetes

Databricks

Pipelines have become ubiquitous, as the need for stringing multiple functions to compose applications has gained adoption and popularity. Common pipeline abstractions such as “fit” and “transform” are even shared across divergent platforms such as Python Scikit-Learn and Apache Spark. Scaling pipelines at the level of simple functions is desirable for many AI applications, however is not directly supported by Ray’s parallelism primitives. In this talk, Raghu will describe a pipeline abstraction that takes advantage of Ray’s compute model to efficiently scale arbitrarily complex pipeline workflows. He will demonstrate how this abstraction cleanly unifies pipeline workflows across multiple platforms such as Scikit-Learn and Spark, and achieves nearly optimal scale-out parallelism on pipelined computations. Attendees will learn how pipelined workflows can be mapped to Ray’s compute model and how they can both unify and accelerate their pipelines with Ray.

Scaling and Unifying SciKit Learn and Apache Spark Pipelines

Databricks

In this talk about zipline, we will introduce a new type of windowing construct called a sawtooth window. We will describe various properties about sawtooth windows that we utilize to achieve online-offline consistency, while still maintaining high-throughput, low-read latency and tunable write latency for serving machine learning features.We will also talk about a simple deployment strategy for correcting feature drift – due operations that are not “abelian groups”, that operate over change data.

Sawtooth Windows for Feature Aggregations

Databricks

We want to present multiple anti patterns utilizing Redis in unconventional ways to get the maximum out of Apache Spark.All examples presented are tried and tested in production at Scale at Adobe. The most common integration is spark-redis which interfaces with Redis as a Dataframe backing Store or as an upstream for Structured Streaming. We deviate from the common use cases to explore where Redis can plug gaps while scaling out high throughput applications in Spark. Niche 1 : Long Running Spark Batch Job – Dispatch New Jobs by polling a Redis Queue · Why? o Custom queries on top a table; We load the data once and query N times · Why not Structured Streaming · Working Solution using Redis Niche 2 : Distributed Counters · Problems with Spark Accumulators · Utilize Redis Hashes as distributed counters · Precautions for retries and speculative execution · Pipelining to improve performance

Redis + Apache Spark = Swiss Army Knife Meets Kitchen Sink

Databricks

In the era of microservices, decentralized ML architectures and complex data pipelines, data quality has become a bigger challenge than ever. When data is involved in complex business processes and decisions, bad data can, and will, affect the bottom line. As a result, ensuring data quality across the entire ML pipeline is both costly, and cumbersome while data monitoring is often fragmented and performed ad hoc. To address these challenges, we built whylogs, an open source standard for data logging. It is a lightweight data profiling library that enables end-to-end data profiling across the entire software stack. The library implements a language and platform agnostic approach to data quality and data monitoring. It can work with different modes of data operations, including streaming, batch and IoT data. In this talk, we will provide an overview of the whylogs architecture, including its lightweight statistical data collection approach and various integrations. We will demonstrate how the whylogs integration with Apache Spark achieves large scale data profiling, and we will show how users can apply this integration into existing data and ML pipelines.

Re-imagine Data Monitoring with whylogs and Spark

Databricks

Machine learning (ML) models are typically part of prediction queries that consist of a data processing part (e.g., for joining, filtering, cleaning, featurization) and an ML part invoking one or more trained models. In this presentation, we identify significant and unexplored opportunities for optimization. To the best of our knowledge, this is the first effort to look at prediction queries holistically, optimizing across both the ML and SQL components. We will present Raven, an end-to-end optimizer for prediction queries. Raven relies on a unified intermediate representation that captures both data processing and ML operators in a single graph structure. This allows us to introduce optimization rules that (i) reduce unnecessary computations by passing information between the data processing and ML operators (ii) leverage operator transformations (e.g., turning a decision tree to a SQL expression or an equivalent neural network) to map operators to the right execution engine, and (iii) integrate compiler techniques to take advantage of the most efficient hardware backend (e.g., CPU, GPU) for each operator. We have implemented Raven as an extension to Spark’s Catalyst optimizer to enable the optimization of SparkSQL prediction queries. Our implementation also allows the optimization of prediction queries in SQL Server. As we will show, Raven is capable of improving prediction query performance on Apache Spark and SQL Server by up to 13.1x and 330x, respectively. For complex models, where GPU acceleration is beneficial, Raven provides up to 8x speedup compared to state-of-the-art systems. As part of the presentation, we will also give a demo showcasing Raven in action.

Raven: End-to-end Optimization of ML Prediction Queries

Databricks

Semantic segmentation is the classification of every pixel in an image/video. The segmentation partitions a digital image into multiple objects to simplify/change the representation of the image into something that is more meaningful and easier to analyze [1][2]. The technique has a wide variety of applications ranging from perception in autonomous driving scenarios to cancer cell segmentation for medical diagnosis. Exponential growth in the datasets that require such segmentation is driven by improvements in the accuracy and quality of the sensors generating the data extending to 3D point cloud data. This growth is further compounded by exponential advances in cloud technologies enabling the storage and compute available for such applications. The need for semantically segmented datasets is a key requirement to improve the accuracy of inference engines that are built upon them. Streamlining the accuracy and efficiency of these systems directly affects the value of the business outcome for organizations that are developing such functionalities as a part of their AI strategy. This presentation details workflows for labeling, preprocessing, modeling, and evaluating performance/accuracy. Scientists and engineers leverage domain-specific features/tools that support the entire workflow from labeling the ground truth, handling data from a wide variety of sources/formats, developing models and finally deploying these models. Users can scale their deployments optimally on GPU-based cloud infrastructure to build accelerated training and inference pipelines while working with big datasets. These environments are optimized for engineers to develop such functionality with ease and then scale against large datasets with Spark-based clusters on the cloud.

Processing Large Datasets for ADAS Applications using Apache Spark

Databricks

Detecting advanced email attacks at scale is a challenging ML problem, particularly due to the rarity of attacks, adversarial nature of the problem, and scale of data. In order to move quickly and adapt to the newest threat we needed to build a Continuous Integration / Continuous Delivery pipeline for the entire ML detection stack. Our goal is to enable detection engineers and data scientists to make changes to any part of the stack including joined datasets for hydration, feature extraction code, detection logic, and develop/train ML models. In this talk, we discuss why we decided to build this pipeline, how it is used to accelerate development and ensure quality, and dive into the nitty-gritty details of building such a system on top of an Apache Spark + Databricks stack.

Machine Learning CI/CD for Email Attack Detection

Databricks

Plus de Databricks (20)

DW Migration Webinar-March 2022.pptx

Data Lakehouse Symposium | Day 1 | Part 1

Data Lakehouse Symposium | Day 1 | Part 2

Data Lakehouse Symposium | Day 2

Data Lakehouse Symposium | Day 4

5 Critical Steps to Clean Your Data Swamp When Migrating Off of Hadoop

Democratizing Data Quality Through a Centralized Platform

Learn to Use Databricks for Data Science

Why APM Is Not the Same As ML Monitoring

The Function, the Context, and the Data—Enabling ML Ops at Stitch Fix

Stage Level Scheduling Improving Big Data and AI Integration

Simplify Data Conversion from Spark to TensorFlow and PyTorch

Scaling your Data Pipelines with Apache Spark on Kubernetes

Scaling and Unifying SciKit Learn and Apache Spark Pipelines

Sawtooth Windows for Feature Aggregations

Redis + Apache Spark = Swiss Army Knife Meets Kitchen Sink

Re-imagine Data Monitoring with whylogs and Spark

Raven: End-to-end Optimization of ML Prediction Queries

Processing Large Datasets for ADAS Applications using Apache Spark

Machine Learning CI/CD for Email Attack Detection

Dernier

Anomaly detection and data imputation within time series

Paris Women in Machine Learning and Data Science

Call Girls Jalahalli Just Call 👗 7737669865 👗 Top Class Call Girl Service Bangalore Booking Contact Details :- WhatsApp Chat :- +91-7737669865 2-May-2024(SMW) Call Girls In Model Towh Bangalore +91-7737669865 !! Best Woman Seeking Man Call Girls Service, Escorts Service in Home Hotel in Bangalore NCR 24 Hours Available Service Call Girls, Contact Us +91-7737669865 (Any Time. Any Where) Call Girls in Bangalore, Noida, Gurgaon, Ghaziabad,Sexy Indian Female Escorts Service Bangalore NCRWelcome To Bangalore Escorts Service – An All Over New Bangalore Very Sexy Hot Call Girls Agency Service Escorts In South BangaloreNCRBangalore’s No. 1 High Profile Independent Female Escorts Service. We Provide Good Quality Educated Profile At Very Regnebal Price 100% Safe And Original.We Are Provide Escorts Service All OYO Hotels ,3*,4*,5* Star Hotel And Home Flat, Apartment. Guest-House. Services In -Call And Out – Call Both Are Services Available. 24Hrs. Any Time Any Where. In All Over Bangalore Noida Gurgaon Ghaziabad Faridabad.More Information And Contact Profile Real Pic Visit Our Website City Wise Escorts Service Agency.Good Looking Cheap And Best Models Girls U Can Get Best Click On Link……Night Call Girls Now In Hotel Le Meridien Gurgaon Near Female Escort One Shot — 5000/in call (time 1 hour), 6000/out call Two shot with one girl — 8000/in call (time 2 hour), 10000/out call Body to body massage with sex- 8000/in call (time 1 hour) Full night Service for one person– 12000/in call, 13000/out call (shot limit 3-4 shots) Full night Service for more than 1 person — please contact Us —7737669865 We are available 24*7 all days of the year. Call us — 7737669865 Thank you for Visiting.

Call Girls Jalahalli Just Call 👗 7737669865 👗 Top Class Call Girl Service Ban...

amitlee9823

BigBuy dropshipping via API with DroFx.pptx

olyaivanovalion

CebaBaby dropshipping via API with DroFX.pptx

olyaivanovalion

Invezz.com - Grow your wealth with trading signals

Invezz1

Capstone Project on IBM Data Analytics Program

MoniSankarHazra

Call Girls Hsr Layout Just Call 👗 7737669865 👗 Top Class Call Girl Service Bangalore Booking Contact Details :- WhatsApp Chat :- +91-7737669865 2-May-2024(SMW) Call Girls In Model Towh Bangalore +91-7737669865 !! Best Woman Seeking Man Call Girls Service, Escorts Service in Home Hotel in Bangalore NCR 24 Hours Available Service Call Girls, Contact Us +91-7737669865 (Any Time. Any Where) Call Girls in Bangalore, Noida, Gurgaon, Ghaziabad,Sexy Indian Female Escorts Service Bangalore NCRWelcome To Bangalore Escorts Service – An All Over New Bangalore Very Sexy Hot Call Girls Agency Service Escorts In South BangaloreNCRBangalore’s No. 1 High Profile Independent Female Escorts Service. We Provide Good Quality Educated Profile At Very Regnebal Price 100% Safe And Original.We Are Provide Escorts Service All OYO Hotels ,3*,4*,5* Star Hotel And Home Flat, Apartment. Guest-House. Services In -Call And Out – Call Both Are Services Available. 24Hrs. Any Time Any Where. In All Over Bangalore Noida Gurgaon Ghaziabad Faridabad.More Information And Contact Profile Real Pic Visit Our Website City Wise Escorts Service Agency.Good Looking Cheap And Best Models Girls U Can Get Best Click On Link……Night Call Girls Now In Hotel Le Meridien Gurgaon Near Female Escort One Shot — 5000/in call (time 1 hour), 6000/out call Two shot with one girl — 8000/in call (time 2 hour), 10000/out call Body to body massage with sex- 8000/in call (time 1 hour) Full night Service for one person– 12000/in call, 13000/out call (shot limit 3-4 shots) Full night Service for more than 1 person — please contact Us —7737669865 We are available 24*7 all days of the year. Call us — 7737669865 Thank you for Visiting.

Call Girls Hsr Layout Just Call 👗 7737669865 👗 Top Class Call Girl Service Ba...

amitlee9823

5CL-ADBA,5cladba, Chinese supplier, safety is guaranteed

amy56318795

Midocean dropshipping via API with DroFx

olyaivanovalion

Digital advertising, or paid media, encompasses the strategic deployment of online advertisements to reach target audiences efficiently and effectively. This includes any digital platform that supports advertising to deliver unique messages for any objective. Understanding the mechanics of digital advertising platforms, along with insights into audience behaviors and preferences, allows marketers to optimize their ad spend and achieve significant engagement and conversion rates. This lecture is for Advanced Digital & Social Media Strategy (MGMTX 466.05) at UCLA Extension.

Digital Advertising Lecture for Advanced Digital & Social Media Strategy at U...

Valters Lauzums

➥🔝 7737669865 🔝▻ malwa Call-girls in Women Seeking Men 🔝malwa🔝 Escorts Service Booking Contact Details :- WhatsApp Chat :- +91-7737669865 Call Girls In Model Towh +91-7737669865 !! Best Woman Seeking Man Call Girls Service, Escorts Service in Home Hotel in NCR 24 Hours Available Service Call Girls, Contact Us +91-7737669865 (Any Time. Any Where) Call Girls in , India,Sexy Indian Female Escorts Service NCRWelcome To Escorts Service – An All Over New Very Sexy Hot Call Girls Agency Service Escorts In South NCR’s No. 1 High Profile Independent Female Escorts Service. We Provide Good Quality Educated Profile At #K09 Very Regnebal Price 100% Safe And Original.We Are Provide Escorts Service All OYO Hotels ,3*,4*,5* Star Hotel And Home Flat, Apartment. Guest-House. Services In -Call And Out – Call Both Are Services Available. 24Hrs. Any Time Any Where. In All Over Noida Gurgaon Ghaziabad Faridabad.More Information And Contact Profile Real Pic Visit Our Website City Wise Escorts Service Agency.Good Looking Cheap And Best Models Girls U Can Get Best Click On Link……Night Call Girls Now In Hotel Le Meridien Gurgaon Near Female Escort One Shot — 5000/in call (time 1 hour), 6000/out call Two shot with one girl — 8000/in call (time 2 hour), 10000/out call Body to body massage with sex- 8000/in call (time 1 hour) Full night Service for one person– 12000/in call, 13000/out call (shot limit 3-4 shots) Full night Service for more than 1 person — please contact Us —7737669865 We are available 24*7 all days of the year. Call us — 7737669865 Thank you for Visiting.

➥🔝 7737669865 🔝▻ malwa Call-girls in Women Seeking Men 🔝malwa🔝 Escorts Ser...

amitlee9823

Discover Why Less is More in B2B Research

michael115558

Probability Grade 10 Third Quarter Lessons

JoseMangaJr1

Mature dropshipping via API with DroFx.pptx

olyaivanovalion

Model Call Girl Services in Delhi reach out to us at 🔝 9953056974 🔝✔️✔️ Our agency presents a selection of young, charming call girls available for bookings at Oyo Hotels. Experience high-class escort services at pocket-friendly rates, with our female escorts exuding both beauty and a delightful personality, ready to meet your desires. Whether it's Housewives, College girls, Russian girls, Muslim girls, or any other preference, we offer a diverse range of options to cater to your tastes. We provide both in-call and out-call services for your convenience. Our in-call location in Delhi ensures cleanliness, hygiene, and 100% safety, while our out-call services offer doorstep delivery for added ease. We value your time and money, hence we kindly request pic collectors, time-passers, and bargain hunters to refrain from contacting us. Our services feature various packages at competitive rates: One shot: ₹2000/in-call, ₹5000/out-call Two shots with one girl: ₹3500/in-call, ₹6000/out-call Body to body massage with sex: ₹3000/in-call Full night for one person: ₹7000/in-call, ₹10000/out-call Full night for more than 1 person: Contact us at 🔝 9953056974 🔝. for details Operating 24/7, we serve various locations in Delhi, including Green Park, Lajpat Nagar, Saket, and Hauz Khas near metro stations. For premium call girl services in Delhi 🔝 9953056974 🔝. Thank you for considering us!

CHEAP Call Girls in Saket (-DELHI )🔝 9953056974🔝(=)/CALL GIRLS SERVICE

9953056974 Low Rate Call Girls In Saket, Delhi NCR

This project aims to predict whether a loan application will be approved or denied based on various factors such as applicant's income, credit score, loan amount, etc. Using a dataset containing historical loan application data, we employed machine learning algorithms to build a predictive model. The model was trained on features such as applicant's income, credit history, loan amount, loan term, and others. After training the model, we evaluated its performance using metrics like accuracy, precision, recall, and F1 score. The insights from this project can help financial institutions streamline their loan approval process and make informed decisions. Visit for more information: https://bostoninstituteofanalytics.org/data-science-and-artificial-intelligence/

Predicting Loan Approval: A Data Science Project

Boston Institute of Analytics

VIP Model Call Girls Hinjewadi ( Pune ) Call ON 8005736733 Starting From 5K to 25K High Profile Escorts In Pune Booking Now open +91- 8005736733 Why you Choose Us- +91- 8005736733 HOT⇄ 8005736733 Mr ashu ji Call Mr ashu Ji +91- 8005736733 (V030524]N) 𝐇𝐨𝐭𝐞𝐥 𝐑𝐨𝐨𝐦𝐬 𝐈𝐧𝐜𝐥𝐮𝐝𝐢𝐧𝐠 𝐑𝐚𝐭𝐞 𝐒𝐡𝐨𝐭𝐬/𝐇𝐨𝐮𝐫𝐲🆓 .█▬█⓿▀█▀ 𝐈𝐍𝐃𝐄𝐏𝐄𝐍𝐃𝐄𝐍𝐓 𝐆𝐈𝐑𝐋 𝐕𝐈𝐏 𝐄𝐒𝐂𝐎𝐑𝐓 Hello Guys ! High Profiles young Beauties and Good Looking standard Profiles Available , Enquire Now if you are interested in Hifi Service and want to get connect with someone who can understand your needs. Service offers you the most beautiful High Profile sexy independent female Escorts in genuine ✔✔✔ To enjoy with hot and sexy girls ✔✔✔ ★providing:- • Models • vip Models • Russian Models • Foreigner Models • TV Actress and Celebrities • Receptionist • Air Hostess • Call Center Working Girls/Women • Hi-Tech Co. Girls/Women • Housewife

VIP Model Call Girls Hinjewadi ( Pune ) Call ON 8005736733 Starting From 5K t...

SUHANI PANDEY

Call Girls Indiranagar Just Call 👗 7737669865 👗 Top Class Call Girl Service Bangalore Booking Contact Details :- WhatsApp Chat :- +91-7737669865 2-May-2024(SMW) Call Girls In Model Towh Bangalore +91-7737669865 !! Best Woman Seeking Man Call Girls Service, Escorts Service in Home Hotel in Bangalore NCR 24 Hours Available Service Call Girls, Contact Us +91-7737669865 (Any Time. Any Where) Call Girls in Bangalore, Noida, Gurgaon, Ghaziabad,Sexy Indian Female Escorts Service Bangalore NCRWelcome To Bangalore Escorts Service – An All Over New Bangalore Very Sexy Hot Call Girls Agency Service Escorts In South BangaloreNCRBangalore’s No. 1 High Profile Independent Female Escorts Service. We Provide Good Quality Educated Profile At Very Regnebal Price 100% Safe And Original.We Are Provide Escorts Service All OYO Hotels ,3*,4*,5* Star Hotel And Home Flat, Apartment. Guest-House. Services In -Call And Out – Call Both Are Services Available. 24Hrs. Any Time Any Where. In All Over Bangalore Noida Gurgaon Ghaziabad Faridabad.More Information And Contact Profile Real Pic Visit Our Website City Wise Escorts Service Agency.Good Looking Cheap And Best Models Girls U Can Get Best Click On Link……Night Call Girls Now In Hotel Le Meridien Gurgaon Near Female Escort One Shot — 5000/in call (time 1 hour), 6000/out call Two shot with one girl — 8000/in call (time 2 hour), 10000/out call Body to body massage with sex- 8000/in call (time 1 hour) Full night Service for one person– 12000/in call, 13000/out call (shot limit 3-4 shots) Full night Service for more than 1 person — please contact Us —7737669865 We are available 24*7 all days of the year. Call us — 7737669865 Thank you for Visiting.

Call Girls Indiranagar Just Call 👗 7737669865 👗 Top Class Call Girl Service B...

amitlee9823

Call Girls Begur Just Call 👗 7737669865 👗 Top Class Call Girl Service Bangalore Booking Contact Details :- WhatsApp Chat :- +91-7737669865 2-May-2024(SMW) Call Girls In Model Towh Bangalore +91-7737669865 !! Best Woman Seeking Man Call Girls Service, Escorts Service in Home Hotel in Bangalore NCR 24 Hours Available Service Call Girls, Contact Us +91-7737669865 (Any Time. Any Where) Call Girls in Bangalore, Noida, Gurgaon, Ghaziabad,Sexy Indian Female Escorts Service Bangalore NCRWelcome To Bangalore Escorts Service – An All Over New Bangalore Very Sexy Hot Call Girls Agency Service Escorts In South BangaloreNCRBangalore’s No. 1 High Profile Independent Female Escorts Service. We Provide Good Quality Educated Profile At Very Regnebal Price 100% Safe And Original.We Are Provide Escorts Service All OYO Hotels ,3*,4*,5* Star Hotel And Home Flat, Apartment. Guest-House. Services In -Call And Out – Call Both Are Services Available. 24Hrs. Any Time Any Where. In All Over Bangalore Noida Gurgaon Ghaziabad Faridabad.More Information And Contact Profile Real Pic Visit Our Website City Wise Escorts Service Agency.Good Looking Cheap And Best Models Girls U Can Get Best Click On Link……Night Call Girls Now In Hotel Le Meridien Gurgaon Near Female Escort One Shot — 5000/in call (time 1 hour), 6000/out call Two shot with one girl — 8000/in call (time 2 hour), 10000/out call Body to body massage with sex- 8000/in call (time 1 hour) Full night Service for one person– 12000/in call, 13000/out call (shot limit 3-4 shots) Full night Service for more than 1 person — please contact Us —7737669865 We are available 24*7 all days of the year. Call us — 7737669865 Thank you for Visiting.

Call Girls Begur Just Call 👗 7737669865 👗 Top Class Call Girl Service Bangalore

amitlee9823

Ashok Vihar Call Girls in Delhi (–9953330565) Escort Service In Delhi NCR PROVIDE 100% REAL GIRLS ALL ARE GIRLS LOOKING MODELS AND RAM MODELS ALL GIRLS” INDIAN , RUSSIAN ,KASMARI ,PUNJABI HOT GIRLS AND MATURED HOUSE WIFE BOOKING ONLY DECENT GUYS AND GENTLEMAN NO FAKE PERSON FREE HOME SERVICE IN CALL FULL AC ROOM SERVICE IN SOUTH DELHI Ultimate Destination for finding a High Profile Independent Escorts in Delhi.Gurgaon.Noida..!.Like You Feel 100% Real Girl Friend Experience. We are High Class Delhi Escort Agency offering quality services with discretion. We only offer services to gentlemen people. We have lots of girls working with us like students, Russian, models, house wife, and much More We Provide Short Time and Full Night Service Call ☎☎+91–9953330565 ❤꧂ • In Call and Out Call Service in Delhi NCR • 3* 5* 7* Hotels Service in Delhi NCR • 24 Hours Available in Delhi NCR • Indian, Russian, Punjabi, Kashmiri Escorts • Real Models, College Girls, House Wife, Also Available • Short Time and Full Time Service Available • Hygienic Full AC Neat and Clean Rooms Avail. In Hotel 24 hours • Daily New Escorts Staff Available • Minimum to Maximum Range Available. Location;- Delhi, Gurgaon, NCR, Noida, and All Over in Delhi Hotel and Home Services HOTEL SERVICE AVAILABLE :-REDDISSON BLU,ITC WELCOM DWARKA,HOTEL-JW MERRIOTT,HOLIDAY INN MAHIPALPUR AIROCTY,CROWNE PLAZA OKHALA,EROSH NEHRU PLACE,SURYAA KALKAJI,CROWEN PLAZA ROHINI,SHERATON PAHARGANJ,THE AMBIENC,VIVANTA,SURAJKUND,ASHOKA CONTINENTAL , LEELA CHANKYAPURI,_ALL 3* 5* 7* STARTS HOTEL SERVICE BOOKING CALL Call WHATSAPP Call ☎+91–9953330565❤꧂ NIGHT SHORT TIME BOTH ARE AVAILABLE

Call Girls In Shalimar Bagh ( Delhi) 9953330565 Escorts Service

9953056974 Low Rate Call Girls In Saket, Delhi NCR

Dernier (20)

Anomaly detection and data imputation within time series

Call Girls Jalahalli Just Call 👗 7737669865 👗 Top Class Call Girl Service Ban...

BigBuy dropshipping via API with DroFx.pptx

CebaBaby dropshipping via API with DroFX.pptx

Invezz.com - Grow your wealth with trading signals

Capstone Project on IBM Data Analytics Program

Call Girls Hsr Layout Just Call 👗 7737669865 👗 Top Class Call Girl Service Ba...

5CL-ADBA,5cladba, Chinese supplier, safety is guaranteed

Midocean dropshipping via API with DroFx

Digital Advertising Lecture for Advanced Digital & Social Media Strategy at U...

➥🔝 7737669865 🔝▻ malwa Call-girls in Women Seeking Men 🔝malwa🔝 Escorts Ser...

Discover Why Less is More in B2B Research

Probability Grade 10 Third Quarter Lessons

Mature dropshipping via API with DroFx.pptx

CHEAP Call Girls in Saket (-DELHI )🔝 9953056974🔝(=)/CALL GIRLS SERVICE

Predicting Loan Approval: A Data Science Project

VIP Model Call Girls Hinjewadi ( Pune ) Call ON 8005736733 Starting From 5K t...

Call Girls Indiranagar Just Call 👗 7737669865 👗 Top Class Call Girl Service B...

Call Girls Begur Just Call 👗 7737669865 👗 Top Class Call Girl Service Bangalore

Call Girls In Shalimar Bagh ( Delhi) 9953330565 Escorts Service

You Can Do It in SQL

1. You can do it in SQL

2. Drew Banin (he/him) Chief Product Ofﬁcer 2 mo’ data mo’ problems @drewbanin on Twitter

3. ● Open core data modeling software ● dbt provides a SQL-ﬁrst workﬂow for: - Data modeling - Data testing - Data documentation - And more! You can learn more at getdbt.com We’re the makers of dbt

4. Confessions & disclaimers ● I have never written a line of production Scala code in my life ● I have written a whole lot of SQL in my life ● People who tolerate Python packaging don’t get to complain about the JVM

5. I ❤ SQL , Expressive , Accessible , Universal , Declarative , Thought provoking….?

6. I ❤ SQL , Expressive - SQL reads more or less like a real sentence , Accessible , Universal , Declarative , Thought provoking….?

7. I ❤ SQL , Expressive , Accessible - Lots of people can learn in pretty quickly , Universal , Declarative , Thought provoking….?

8. I ❤ SQL , Expressive , Accessible , Universal - Most databases speak SQL with pretty consistent support , Declarative , Thought provoking….?

9. I ❤ SQL , Expressive , Accessible , Universal , Declarative - No moving parts, limited opportunities for the bad kind of magic , Thought provoking….?

10. I ❤ SQL , Expressive , Accessible , Universal , Declarative , Thought provoking….?

11. Some things you can do in SQL

12. Technique: Pivoting Columns

13. What: convert rows of data into columns of data Why: to derive new metrics from existing rows in a table Pivoting columns

14. What: convert rows of data into columns of data Why: to derive new metrics from existing rows in a table Pivoting columns user_id event_name happened_at 1 signup 2021-01-01 1 purchase 2021-01-02 2 signup 2021-01-03 3 signup 2021-01-04 3 purchase 2021-01-05 1 purchase 2021-01-05

15. What: convert rows of data into columns of data Why: to derive new metrics from existing rows in a table Pivoting columns user_id event_name happened_at 1 signup 2021-01-01 1 purchase 2021-01-02 2 signup 2021-01-03 3 signup 2021-01-04 3 purchase 2021-01-05 1 purchase 2021-01-05

16. What: convert rows of data into columns of data Why: to derive new metrics from existing rows in a table Pivoting columns user_id event_name happened_at 1 signup 2021-01-01 1 purchase 2021-01-02 2 signup 2021-01-03 3 signup 2021-01-04 3 purchase 2021-01-05 1 purchase 2021-01-05 user_id signup_date purchases 1 2021-01-01 2 2 2021-01-02 0 3 2021-01-04 1

17.

18. Technique: Appending datasets

19. Unions What: append tables together Why: combine tables into a taller table

20. Unions What: append tables together Why: combine tables into a taller table user_id event_name happened_at 1 signup 2021-01-01 1 purchase 2021-01-02 2 signup 2021-01-03 user_id event_name happened_at 1 app_install 2021-01-01 1 screen_view 2021-01-02 2 app_install 2021-01-03 web_events mobile_events

21. Unions What: append tables together Why: combine tables into a taller table source user_id event_name happened_at mobile 1 app_install 2021-01-01 mobile 1 screen_view 2021-01-02 mobile 2 app_install 2021-01-03 web 1 signup 2021-01-01 web 1 purchase 2021-01-02 web 2 signup 2021-01-03 all_events

22.

23. Technique: Window functions

24. Window functions What: apply transformations over part of a table Why: use cases abound!

25. Window functions What: apply transformations over part of a table Why: use cases abound! user_id happened_at page_path 1 2021-01-01 /product 1 2021-01-02 /signup 2 2021-01-03 /app pageviews

26. Window functions What: apply transformations over part of a table Why: use cases abound! user_id happened_at page_path 1 2021-01-01 /product 1 2021-01-02 /signup 2 2021-01-03 /docs pageviews user_id ﬁrst_page_path 1 /product 2 /docs user_touches

27.

28. Helpful window functions ● ﬁrst_value ● last_value ● lag ● lead ● row_number ● rank ● sum ● min ● max ● avg ● count

29. When you write a lot of SQL...

30. SQL: The bad parts ● No “functions” for code reuse ● Lots of typing → lots of typos ● It might be the most copy/pasted language of all time! ○ Citation needed ● But…. what if we add templating?

31.

32.

33. ● Yes! ● Accessibility is 🔑 ● What happens when you teach people SQL? ○ Data (engineers|scientists|analysts) can collaborate in code ○ Non-engineers are empowered to understand business logic ○ More people can contribute! You can do it in SQL… but should you?

34. What does the future hold? ● More data workloads will converge around SQL ● Databases will improve support for sophisticated SQL operations - AI & ML? - Streaming? - ….? ● More roles will require SQL knowledge and aptitude ● More & better tools will exist to aid in writing and managing SQL

35. What do you think?

You Can Do It in SQL

Recommandé

Recommandé

Contenu connexe

Tendances

Tendances (20)

Similaire à You Can Do It in SQL

Similaire à You Can Do It in SQL (20)

Plus de Databricks

Plus de Databricks (20)

Dernier

Dernier (20)

You Can Do It in SQL