Disrupting Big Data with Apache Spark in the Cloud

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This document discusses the challenges of big data analytics and how Apache Spark and Databricks can help address them. It summarizes that: 1) There is a gap between the growth of data and ability to perform real-time analytics on that data due to challenges in managing infrastructure, empowering teams, and establishing production-ready applications. 2) Databricks provides a cloud-hosted platform that uses Apache Spark to allow for just-in-time processing of data across storage silos, with an integrated workspace for interactive exploration, machine learning, and production-ready workflows. 3) Databricks Enterprise Security provides an end-to-end security solution for Apache Spark to address challenges in securing file

Données & analyses

Disrupting Big Data with
Apache Spark in the Cloud
Ali Ghodsi
Co-Founder and CEO

The Dawn of Advanced Analytics
2
WatsonSIRI/assistantsSelf-driving
cars
Not just sci-fi, important applications for businesses

Analytics Transforming Industries
3
Predictive analytics Anomaly Detection
Predict Product Revenue
Customer Assessment
Targeted Advertising
Fraud Detection
Risk Assessment
Equipment Failure
Data-Driven Real-time Analytics Applications

Today’s Data Reality
4
HADOOP
DATA LAKES
DATA HUBS
CLOUD
STORAGE
DATA
WAREHOUSE
S
Siloed, Fast-Growing Size, Cost

The Analytics Gap
5
IndustrialMediaPharma
HADOOP
DATA LAKES
DATA HUBS
CLOUD
STORAGE
DATA
WAREHOUSES
Siloed, Fast-Growing Size, Cost
Real-time Data-Driven Analytics Applications

Why is there a gap?
6
Real-time Data-Driven Analytics Applications
Manage Data
infrastructure
• Create, tune, monitor compute clusters.
• Securely access silos of disparate data sources.
• Enforce proper data governance.
•1
Empower teams to be
productive
• Securely share big data clusters among analysts.
• Interactively explore data and prototype ideas.
• Debug, troubleshoot, version-control big data applications.•
•
•
2
Establish Production-
Ready Applications
• Setup robust data pipelines for ETL/ELT.
• Productionize real-time applications with HA, FT.
• Build, serve, maintain advanced machine learning models.
•
3
Siloed, Fast-Growing Size, Cost

Databricks Cloud-Hosted Platform
7
• Separate compute & storage
• Integrate existing data stores
• Efficient cache on first access
Just-in-Time Data
Platform
1
Agile
• Workflow scheduler for ML,
streaming, SQL, ETL
• High availability, fault-
tolerant, performance-
optimized
Automated Apache
Spark Management
3
Production-Ready
• Interactive notebooks,
dashboards, reports
• Real-time exploration,
machine learning, graph use
cases
Integrated
Workspace
2
Democratize Big Data

HADOOP /
DATA LAKES
DATA
WAREHOUSE
S
YOUR
STORAGE
CLOUD
STORAGE
8
Databricks Just-in-Time Data Platform
INTEGRATED
WORKSPACEDASHBOARD
S
Reports
NOTEBOOKS
github, viz,
collaboration
BI TOOLS
JUST-IN-TIME
PROCESSING
POWERED
BY
APACHE
CLUSTERS: Auto-scaled, resilient, multi-tenant
DATA INTEGRATION: secure and fast data source
integrations
INTERFACES: REST APIs & BI tools
DATABRICKS SERVICES
+
YOUR CUSTOM SPARK
APPS
PRODUCTION JOBS
DATA LAKE
DATA HUB

The Challenge of Securing Analytics
9
End-to-end security a challenge for enterprises
Securing file
management
Secure table
management
Secure
cluster
management
Secure
job
workflows
Secure
dashboards,
report, notebook
management
Today there are piecemeal solutions, but no comprehensive
solution

Databricks Enterprise Security (DBES)
10
Holistic end-to-end security for Data Analytics
Tables Clusters Workflow
s
Notebooks,
Dashboards,
Reports
Files
• Role-based access control
• Auditing and governance
• Integrated identity-management
• Encryption on-disk and on-the-
wire
DBES provides
The First End-to-End Security Solution for Apache Spark

Enterprise use-cases
11
Preventing credit card fraud
Predict energy demand based on massive weather data
Predict player churn, predicting network outages
Natural language processing to extract author graph
Generating tailored programs based on big data

Try Apache Spark with Databricks
13
http://databricks.com/try
Try latest version of Apache Spark and preview of Spark 2.0

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"Kafka and ClickHouse are two powerful and popular technologies that can work together to create a high-performance, real-time data processing and analytics pipeline. They complement each other pretty well, and their synergy can be leveraged in various use cases. In this talk, we'll explore this dynamic synergy between Kafka and ClickHouse with a live demonstration leveraging OpenSky data. We’ll use ClickPipes, the ClickHouse Cloud native Kafka integration, for building an end-to-end real-time data processing and analytics solution. We’ll also dive into the details of what you need to know if you want to build similar real-time analytics applications."

(real-time)²: Real-time data for real-time analytics with Kafka and ClickHouse

HostedbyConfluent

Big Data Analytics with Hadoop

Philippe Julio

Finding Meaning in the Noise: Understanding Big Data with AWS Analytics

Amazon Web Services

Lance Olson. Cortana Analytics is a fully managed big data and advanced analytics suite that helps you transform your data into intelligent action. Come to this two-part session to learn how you can do "big data" processing and storage in Cortana Analytics. In the first part, we will provide an overview of the processing and storage services. We will then talk about the patterns and use cases which make up most big data solutions. In the second part, we will go hands-on, showing you how to get started today with writing batch/interactive queries, real-time stream processing, or NoSQL transactions all over the same repository of data. Crunch petabytes of data by scaling out your computation power to any sized cluster. Store any amount of unstructured data in its native format with no limits to file or account size. All of this can be done with no hardware to acquire or maintain and minimal time to setup giving you the value of "big data" within minutes. Go to https://channel9.msdn.com/ to find the recording of this session.

Cortana Analytics Workshop: The "Big Data" of the Cortana Analytics Suite, Pa...

MSAdvAnalytics

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Cloudian 451-hortonworks - webinar

(real-time)²: Real-time data for real-time analytics with Kafka and ClickHouse

Big Data Analytics with Hadoop

Finding Meaning in the Noise: Understanding Big Data with AWS Analytics

Cortana Analytics Workshop: The "Big Data" of the Cortana Analytics Suite, Pa...

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2017 continues to be an exciting year for Apache Spark. I will talk about new updates in two major areas in the Spark community this year: stream processing with Structured Streaming, and deep learning with high-level libraries such as Deep Learning Pipelines and TensorFlowOnSpark. In both areas, the community is making powerful new functionality available in the same high-level APIs used in the rest of the Spark ecosystem (e.g., DataFrames and ML Pipelines), and improving both the scalability and ease of use of stream processing and machine learning.

Deep Learning and Streaming in Apache Spark 2.x with Matei Zaharia

Jen Aman

Building applications that can read and analyze a wide variety of data may change the way we do science and make business decisions. However, building such applications is challenging: real world data is expressed in natural language, images, or other “dark” data formats which are fraught with imprecision and ambiguity and so are difficult for machines to understand. This talk will describe Snorkel, whose goal is to make routine Dark Data and other prediction tasks dramatically easier. At its core, Snorkel focuses on a key bottleneck in the development of machine learning systems: the lack of large training datasets. In Snorkel, a user implicitly creates large training sets by writing simple programs that label data, instead of performing manual feature engineering or tedious hand-labeling of individual data items. We’ll provide a set of tutorials that will allow folks to write Snorkel applications that use Spark. Snorkel is open source on github and available from Snorkel.Stanford.edu.

Snorkel: Dark Data and Machine Learning with Christopher Ré

Jen Aman

The combination of Deep Learning with Apache Spark has the potential for tremendous impact in many sectors of the industry. This webinar, based on the experience gained in assisting customers with the Databricks Virtual Analytics Platform, will present some best practices for building deep learning pipelines with Spark. Rather than comparing deep learning systems or specific optimizations, this webinar will focus on issues that are common to deep learning frameworks when running on a Spark cluster, including: * optimizing cluster setup; * configuring the cluster; * ingesting data; and * monitoring long-running jobs. We will demonstrate the techniques we cover using Google’s popular TensorFlow library. More specifically, we will cover typical issues users encounter when integrating deep learning libraries with Spark clusters. Clusters can be configured to avoid task conflicts on GPUs and to allow using multiple GPUs per worker. Setting up pipelines for efficient data ingest improves job throughput, and monitoring facilitates both the work of configuration and the stability of deep learning jobs.

Deep Learning on Apache® Spark™: Workflows and Best Practices

Jen Aman

Deep Learning on Apache® Spark™ : Workflows and Best Practices

Jen Aman

Spark Summit East Keynote by Ion Stoica A long-standing grand challenge in computing is to enable machines to act autonomously and intelligently: to rapidly and repeatedly take appropriate actions based on information in the world around them. To address this challenge, at UC Berkeley we are starting a new five year effort that focuses on the development of data-intensive systems that provide Real-Time Intelligence with Secure Execution (RISE). Following in the footsteps of AMPLab, RISELab is an interdisciplinary effort bringing together researchers across AI, robotics, security, and data systems. In this talk I’ll present our research vision and then discuss some of the applications that will be enabled by RISE technologies.

RISELab:Enabling Intelligent Real-Time Decisions

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Spatial Analysis On Histological Images Using Spark

Jen Aman

Massive Simulations In Spark: Distributed Monte Carlo For Global Health Forec...

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A Graph-Based Method For Cross-Entity Threat Detection

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Yggdrasil: Faster Decision Trees Using Column Partitioning In Spark

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Time-Evolving Graph Processing On Commodity Clusters

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Deploying Accelerators At Datacenter Scale Using Spark

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Re-Architecting Spark For Performance Understandability

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Re-Architecting Spark For Performance Understandability

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Low Latency Execution For Apache Spark

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Efficient State Management With Spark 2.0 And Scale-Out Databases

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Livy: A REST Web Service For Apache Spark

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GPU Computing With Apache Spark And Python

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Deep Learning and Streaming in Apache Spark 2.x with Matei Zaharia

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Deep Learning on Apache® Spark™ : Workflows and Best Practices

RISELab:Enabling Intelligent Real-Time Decisions

Spatial Analysis On Histological Images Using Spark

Massive Simulations In Spark: Distributed Monte Carlo For Global Health Forec...

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Yggdrasil: Faster Decision Trees Using Column Partitioning In Spark

Time-Evolving Graph Processing On Commodity Clusters

Deploying Accelerators At Datacenter Scale Using Spark

Re-Architecting Spark For Performance Understandability

Low Latency Execution For Apache Spark

Efficient State Management With Spark 2.0 And Scale-Out Databases

Livy: A REST Web Service For Apache Spark

GPU Computing With Apache Spark And Python

Spark And Cassandra: 2 Fast, 2 Furious

Building Custom Machine Learning Algorithms With Apache SystemML

Spark on Mesos

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