Spark is a cluster computing framework designed to be fast, general-purpose, and able to handle a wide range of workloads including batch processing, iterative algorithms, interactive queries, and streaming. It is faster than Hadoop for interactive queries and complex applications by running computations in-memory when possible. Spark also simplifies combining different processing types through a single engine. It offers APIs in Java, Python, Scala and SQL and integrates closely with other big data tools like Hadoop. Spark is commonly used for interactive queries on large datasets, streaming data processing, and machine learning tasks.
This document provides an overview of Spark SQL and its architecture. Spark SQL allows users to run SQL queries over SchemaRDDs, which are RDDs with a schema and column names. It introduces a SQL-like query abstraction over RDDs and allows querying data in a declarative manner. The Spark SQL component consists of Catalyst, a logical query optimizer, and execution engines for different data sources. It can integrate with data sources like Parquet, JSON, and Cassandra.
The document discusses YARN (Yet Another Resource Negotiator), which is the cluster resource management layer of Hadoop. It describes the limitations of the previous Hadoop 1.0 architecture where MapReduce was responsible for both data processing and resource management. YARN was created to address these limitations by separating resource management from data processing. It discusses the components of YARN including the Resource Manager, Node Manager, Containers, and Application Master. It also provides examples of workloads that can run on YARN beyond MapReduce and describes the YARN architecture and how applications run on the YARN framework.
Delta Lake, an open-source innovations which brings new capabilities for transactions, version control and indexing your data lakes. We uncover how Delta Lake benefits and why it matters to you. Through this session, we showcase some of its benefits and how they can improve your modern data engineering pipelines. Delta lake provides snapshot isolation which helps concurrent read/write operations and enables efficient insert, update, deletes, and rollback capabilities. It allows background file optimization through compaction and z-order partitioning achieving better performance improvements. In this presentation, we will learn the Delta Lake benefits and how it solves common data lake challenges, and most importantly new Delta Time Travel capability.
Apache Spark is a In Memory Data Processing Solution that can work with existing data source like HDFS and can make use of your existing computation infrastructure like YARN/Mesos etc. This talk will cover a basic introduction of Apache Spark with its various components like MLib, Shark, GrpahX and with few examples.
Apache Spark Introduction and Resilient Distributed Dataset basics and deep diveSachin Aggarwal
We will give a detailed introduction to Apache Spark and why and how Spark can change the analytics world. Apache Spark's memory abstraction is RDD (Resilient Distributed DataSet). One of the key reason why Apache Spark is so different is because of the introduction of RDD. You cannot do anything in Apache Spark without knowing about RDDs. We will give a high level introduction to RDD and in the second half we will have a deep dive into RDDs.
This document provides an overview of Apache Spark, including how it compares to Hadoop, the Spark ecosystem, Resilient Distributed Datasets (RDDs), transformations and actions on RDDs, the directed acyclic graph (DAG) scheduler, Spark Streaming, and the DataFrames API. Key points covered include Spark's faster performance versus Hadoop through its use of memory instead of disk, the RDD abstraction for distributed collections, common RDD operations, and Spark's capabilities for real-time streaming data processing and SQL queries on structured data.
The presentation covers following topics: 1) Hadoop Introduction 2) Hadoop nodes and daemons 3) Architecture 4) Hadoop best features 5) Hadoop characteristics. For more further knowledge of Hadoop refer the link: http://data-flair.training/blogs/hadoop-tutorial-for-beginners/
Spark is a cluster computing framework designed to be fast, general-purpose, and able to handle a wide range of workloads including batch processing, iterative algorithms, interactive queries, and streaming. It is faster than Hadoop for interactive queries and complex applications by running computations in-memory when possible. Spark also simplifies combining different processing types through a single engine. It offers APIs in Java, Python, Scala and SQL and integrates closely with other big data tools like Hadoop. Spark is commonly used for interactive queries on large datasets, streaming data processing, and machine learning tasks.
This document provides an overview of Spark SQL and its architecture. Spark SQL allows users to run SQL queries over SchemaRDDs, which are RDDs with a schema and column names. It introduces a SQL-like query abstraction over RDDs and allows querying data in a declarative manner. The Spark SQL component consists of Catalyst, a logical query optimizer, and execution engines for different data sources. It can integrate with data sources like Parquet, JSON, and Cassandra.
The document discusses YARN (Yet Another Resource Negotiator), which is the cluster resource management layer of Hadoop. It describes the limitations of the previous Hadoop 1.0 architecture where MapReduce was responsible for both data processing and resource management. YARN was created to address these limitations by separating resource management from data processing. It discusses the components of YARN including the Resource Manager, Node Manager, Containers, and Application Master. It also provides examples of workloads that can run on YARN beyond MapReduce and describes the YARN architecture and how applications run on the YARN framework.
Delta Lake, an open-source innovations which brings new capabilities for transactions, version control and indexing your data lakes. We uncover how Delta Lake benefits and why it matters to you. Through this session, we showcase some of its benefits and how they can improve your modern data engineering pipelines. Delta lake provides snapshot isolation which helps concurrent read/write operations and enables efficient insert, update, deletes, and rollback capabilities. It allows background file optimization through compaction and z-order partitioning achieving better performance improvements. In this presentation, we will learn the Delta Lake benefits and how it solves common data lake challenges, and most importantly new Delta Time Travel capability.
Apache Spark is a In Memory Data Processing Solution that can work with existing data source like HDFS and can make use of your existing computation infrastructure like YARN/Mesos etc. This talk will cover a basic introduction of Apache Spark with its various components like MLib, Shark, GrpahX and with few examples.
Apache Spark Introduction and Resilient Distributed Dataset basics and deep diveSachin Aggarwal
We will give a detailed introduction to Apache Spark and why and how Spark can change the analytics world. Apache Spark's memory abstraction is RDD (Resilient Distributed DataSet). One of the key reason why Apache Spark is so different is because of the introduction of RDD. You cannot do anything in Apache Spark without knowing about RDDs. We will give a high level introduction to RDD and in the second half we will have a deep dive into RDDs.
This document provides an overview of Apache Spark, including how it compares to Hadoop, the Spark ecosystem, Resilient Distributed Datasets (RDDs), transformations and actions on RDDs, the directed acyclic graph (DAG) scheduler, Spark Streaming, and the DataFrames API. Key points covered include Spark's faster performance versus Hadoop through its use of memory instead of disk, the RDD abstraction for distributed collections, common RDD operations, and Spark's capabilities for real-time streaming data processing and SQL queries on structured data.
The presentation covers following topics: 1) Hadoop Introduction 2) Hadoop nodes and daemons 3) Architecture 4) Hadoop best features 5) Hadoop characteristics. For more further knowledge of Hadoop refer the link: http://data-flair.training/blogs/hadoop-tutorial-for-beginners/
Apache Spark - Basics of RDD | Big Data Hadoop Spark Tutorial | CloudxLabCloudxLab
Big Data with Hadoop & Spark Training: http://bit.ly/2L4rPmM
This CloudxLab Basics of RDD tutorial helps you to understand Basics of RDD in detail. Below are the topics covered in this tutorial:
1) What is RDD - Resilient Distributed Datasets
2) Creating RDD in Scala
3) RDD Operations - Transformations & Actions
4) RDD Transformations - map() & filter()
5) RDD Actions - take() & saveAsTextFile()
6) Lazy Evaluation & Instant Evaluation
7) Lineage Graph
8) flatMap and Union
9) Scala Transformations - Union
10) Scala Actions - saveAsTextFile(), collect(), take() and count()
11) More Actions - reduce()
12) Can We Use reduce() for Computing Average?
13) Solving Problems with Spark
14) Compute Average and Standard Deviation with Spark
15) Pick Random Samples From a Dataset using Spark
What is Apache Spark | Apache Spark Tutorial For Beginners | Apache Spark Tra...Edureka!
This Edureka "What is Spark" tutorial will introduce you to big data analytics framework - Apache Spark. This tutorial is ideal for both beginners as well as professionals who want to learn or brush up their Apache Spark concepts. Below are the topics covered in this tutorial:
1) Big Data Analytics
2) What is Apache Spark?
3) Why Apache Spark?
4) Using Spark with Hadoop
5) Apache Spark Features
6) Apache Spark Architecture
7) Apache Spark Ecosystem - Spark Core, Spark Streaming, Spark MLlib, Spark SQL, GraphX
8) Demo: Analyze Flight Data Using Apache Spark
This document provides an overview of Hadoop and its uses. It defines Hadoop as a distributed processing framework for large datasets across clusters of commodity hardware. It describes HDFS for distributed storage and MapReduce as a programming model for distributed computations. Several examples of Hadoop applications are given like log analysis, web indexing, and machine learning. In summary, Hadoop is a scalable platform for distributed storage and processing of big data across clusters of servers.
"Introduction to the Oracle Application Development Framework (ADF)"
In the presentation will be covered basic architecture of ADF, offered functionality, variety of components, customization features, benefits and lacks. Will be a short demo to have a look and feel how it works. Some shares about real world ADF experience.
we will see an overview of Spark in Big Data. We will start with an introduction to Apache Spark Programming. Then we will move to know the Spark History. Moreover, we will learn why Spark is needed. Afterward, will cover all fundamental of Spark components. Furthermore, we will learn about Spark’s core abstraction and Spark RDD. For more detailed insights, we will also cover spark features, Spark limitations, and Spark Use cases.
Introduction to Apache Spark. With an emphasis on the RDD API, Spark SQL (DataFrame and Dataset API) and Spark Streaming.
Presented at the Desert Code Camp:
http://oct2016.desertcodecamp.com/sessions/all
Apache Spark is a fast distributed data processing engine that runs in memory. It can be used with Java, Scala, Python and R. Spark uses resilient distributed datasets (RDDs) as its main data structure. RDDs are immutable and partitioned collections of elements that allow transformations like map and filter. Spark is 10-100x faster than Hadoop for iterative algorithms and can be used for tasks like ETL, machine learning, and streaming.
Trino: A Ludicrously Fast Query Engine - Pulsar Summit NA 2021StreamNative
You may be familiar with the Presto plugin used to run fast interactive queries over Pulsar using ANSI SQL and can be joined with other data sources. This plugin will soon get a rename to align with the rename of the PrestoSQL project to Trino. What is the purpose of this rename and what does it mean for those using the Presto plugin? We cover the history of the community shift from PrestoDB to PrestoSQL, as well as, the future plans for the Pulsar community to donate this plugin to the Trino project. One of the connector maintainers will then demo the connector and show what is possible when using Trino and Pulsar!
Memory management is at the heart of any data-intensive system. Spark, in particular, must arbitrate memory allocation between two main use cases: buffering intermediate data for processing (execution) and caching user data (storage). This talk will take a deep dive through the memory management designs adopted in Spark since its inception and discuss their performance and usability implications for the end user.
Spark is an open source cluster computing framework for large-scale data processing. It provides high-level APIs and runs on Hadoop clusters. Spark components include Spark Core for execution, Spark SQL for SQL queries, Spark Streaming for real-time data, and MLlib for machine learning. The core abstraction in Spark is the resilient distributed dataset (RDD), which allows data to be partitioned across nodes for parallel processing. A word count example demonstrates how to use transformations like flatMap and reduceByKey to count word frequencies from an input file in Spark.
This document provides an overview of Azure Data Factory (ADF), including why it is used, its key components and activities, how it works, and differences between versions 1 and 2. It describes the main steps in ADF as connect and collect, transform and enrich, publish, and monitor. The main components are pipelines, activities, datasets, and linked services. Activities include data movement, transformation, and control. Integration runtime and system variables are also summarized.
This session covers how to work with PySpark interface to develop Spark applications. From loading, ingesting, and applying transformation on the data. The session covers how to work with different data sources of data, apply transformation, python best practices in developing Spark Apps. The demo covers integrating Apache Spark apps, In memory processing capabilities, working with notebooks, and integrating analytics tools into Spark Applications.
Deep Dive: Memory Management in Apache SparkDatabricks
Memory management is at the heart of any data-intensive system. Spark, in particular, must arbitrate memory allocation between two main use cases: buffering intermediate data for processing (execution) and caching user data (storage). This talk will take a deep dive through the memory management designs adopted in Spark since its inception and discuss their performance and usability implications for the end user.
This presentation describes how to efficiently load data into Hive. I cover partitioning, predicate pushdown, ORC file optimization and different loading schemes
This document provides an overview of a talk on Apache Spark. It introduces the speaker and their background. It acknowledges inspiration from a previous Spark training. It then outlines the structure of the talk, which will include: a brief history of big data; a tour of Spark including its advantages over MapReduce; and explanations of Spark concepts like RDDs, transformations, and actions. The document serves to introduce the topics that will be covered in the talk.
The document summarizes Spark SQL, which is a Spark module for structured data processing. It introduces key concepts like RDDs, DataFrames, and interacting with data sources. The architecture of Spark SQL is explained, including how it works with different languages and data sources through its schema RDD abstraction. Features of Spark SQL are covered such as its integration with Spark programs, unified data access, compatibility with Hive, and standard connectivity.
Big Data raises challenges about how to process such vast pool of raw data and how to aggregate value to our lives. For addressing these demands an ecosystem of tools named Hadoop was conceived.
Tame the small files problem and optimize data layout for streaming ingestion...Flink Forward
Flink Forward San Francisco 2022.
In modern data platform architectures, stream processing engines such as Apache Flink are used to ingest continuous streams of data into data lakes such as Apache Iceberg. Streaming ingestion to iceberg tables can suffer by two problems (1) small files problem that can hurt read performance (2) poor data clustering that can make file pruning less effective. To address those two problems, we propose adding a shuffling stage to the Flink Iceberg streaming writer. The shuffling stage can intelligently group data via bin packing or range partition. This can reduce the number of concurrent files that every task writes. It can also improve data clustering. In this talk, we will explain the motivations in details and dive into the design of the shuffling stage. We will also share the evaluation results that demonstrate the effectiveness of smart shuffling.
by
Gang Ye & Steven Wu
The document provides a comparative analysis of Apache Hadoop and Apache Spark, two popular platforms for big data analytics. It discusses their key features, capabilities, strengths, limitations, use cases and provides a recommendation on selecting the right tool based on specific business needs and data processing requirements.
This document provides an overview and comparison of RDBMS, Hadoop, and Spark. It introduces RDBMS and describes its use cases such as online transaction processing and data warehouses. It then introduces Hadoop and describes its ecosystem including HDFS, YARN, MapReduce, and related sub-modules. Common use cases for Hadoop are also outlined. Spark is then introduced along with its modules like Spark Core, SQL, and MLlib. Use cases for Spark include data enrichment, trigger event detection, and machine learning. The document concludes by comparing RDBMS and Hadoop, as well as Hadoop and Spark, and addressing common misconceptions about Hadoop and Spark.
Apache Spark - Basics of RDD | Big Data Hadoop Spark Tutorial | CloudxLabCloudxLab
Big Data with Hadoop & Spark Training: http://bit.ly/2L4rPmM
This CloudxLab Basics of RDD tutorial helps you to understand Basics of RDD in detail. Below are the topics covered in this tutorial:
1) What is RDD - Resilient Distributed Datasets
2) Creating RDD in Scala
3) RDD Operations - Transformations & Actions
4) RDD Transformations - map() & filter()
5) RDD Actions - take() & saveAsTextFile()
6) Lazy Evaluation & Instant Evaluation
7) Lineage Graph
8) flatMap and Union
9) Scala Transformations - Union
10) Scala Actions - saveAsTextFile(), collect(), take() and count()
11) More Actions - reduce()
12) Can We Use reduce() for Computing Average?
13) Solving Problems with Spark
14) Compute Average and Standard Deviation with Spark
15) Pick Random Samples From a Dataset using Spark
What is Apache Spark | Apache Spark Tutorial For Beginners | Apache Spark Tra...Edureka!
This Edureka "What is Spark" tutorial will introduce you to big data analytics framework - Apache Spark. This tutorial is ideal for both beginners as well as professionals who want to learn or brush up their Apache Spark concepts. Below are the topics covered in this tutorial:
1) Big Data Analytics
2) What is Apache Spark?
3) Why Apache Spark?
4) Using Spark with Hadoop
5) Apache Spark Features
6) Apache Spark Architecture
7) Apache Spark Ecosystem - Spark Core, Spark Streaming, Spark MLlib, Spark SQL, GraphX
8) Demo: Analyze Flight Data Using Apache Spark
This document provides an overview of Hadoop and its uses. It defines Hadoop as a distributed processing framework for large datasets across clusters of commodity hardware. It describes HDFS for distributed storage and MapReduce as a programming model for distributed computations. Several examples of Hadoop applications are given like log analysis, web indexing, and machine learning. In summary, Hadoop is a scalable platform for distributed storage and processing of big data across clusters of servers.
"Introduction to the Oracle Application Development Framework (ADF)"
In the presentation will be covered basic architecture of ADF, offered functionality, variety of components, customization features, benefits and lacks. Will be a short demo to have a look and feel how it works. Some shares about real world ADF experience.
we will see an overview of Spark in Big Data. We will start with an introduction to Apache Spark Programming. Then we will move to know the Spark History. Moreover, we will learn why Spark is needed. Afterward, will cover all fundamental of Spark components. Furthermore, we will learn about Spark’s core abstraction and Spark RDD. For more detailed insights, we will also cover spark features, Spark limitations, and Spark Use cases.
Introduction to Apache Spark. With an emphasis on the RDD API, Spark SQL (DataFrame and Dataset API) and Spark Streaming.
Presented at the Desert Code Camp:
http://oct2016.desertcodecamp.com/sessions/all
Apache Spark is a fast distributed data processing engine that runs in memory. It can be used with Java, Scala, Python and R. Spark uses resilient distributed datasets (RDDs) as its main data structure. RDDs are immutable and partitioned collections of elements that allow transformations like map and filter. Spark is 10-100x faster than Hadoop for iterative algorithms and can be used for tasks like ETL, machine learning, and streaming.
Trino: A Ludicrously Fast Query Engine - Pulsar Summit NA 2021StreamNative
You may be familiar with the Presto plugin used to run fast interactive queries over Pulsar using ANSI SQL and can be joined with other data sources. This plugin will soon get a rename to align with the rename of the PrestoSQL project to Trino. What is the purpose of this rename and what does it mean for those using the Presto plugin? We cover the history of the community shift from PrestoDB to PrestoSQL, as well as, the future plans for the Pulsar community to donate this plugin to the Trino project. One of the connector maintainers will then demo the connector and show what is possible when using Trino and Pulsar!
Memory management is at the heart of any data-intensive system. Spark, in particular, must arbitrate memory allocation between two main use cases: buffering intermediate data for processing (execution) and caching user data (storage). This talk will take a deep dive through the memory management designs adopted in Spark since its inception and discuss their performance and usability implications for the end user.
Spark is an open source cluster computing framework for large-scale data processing. It provides high-level APIs and runs on Hadoop clusters. Spark components include Spark Core for execution, Spark SQL for SQL queries, Spark Streaming for real-time data, and MLlib for machine learning. The core abstraction in Spark is the resilient distributed dataset (RDD), which allows data to be partitioned across nodes for parallel processing. A word count example demonstrates how to use transformations like flatMap and reduceByKey to count word frequencies from an input file in Spark.
This document provides an overview of Azure Data Factory (ADF), including why it is used, its key components and activities, how it works, and differences between versions 1 and 2. It describes the main steps in ADF as connect and collect, transform and enrich, publish, and monitor. The main components are pipelines, activities, datasets, and linked services. Activities include data movement, transformation, and control. Integration runtime and system variables are also summarized.
This session covers how to work with PySpark interface to develop Spark applications. From loading, ingesting, and applying transformation on the data. The session covers how to work with different data sources of data, apply transformation, python best practices in developing Spark Apps. The demo covers integrating Apache Spark apps, In memory processing capabilities, working with notebooks, and integrating analytics tools into Spark Applications.
Deep Dive: Memory Management in Apache SparkDatabricks
Memory management is at the heart of any data-intensive system. Spark, in particular, must arbitrate memory allocation between two main use cases: buffering intermediate data for processing (execution) and caching user data (storage). This talk will take a deep dive through the memory management designs adopted in Spark since its inception and discuss their performance and usability implications for the end user.
This presentation describes how to efficiently load data into Hive. I cover partitioning, predicate pushdown, ORC file optimization and different loading schemes
This document provides an overview of a talk on Apache Spark. It introduces the speaker and their background. It acknowledges inspiration from a previous Spark training. It then outlines the structure of the talk, which will include: a brief history of big data; a tour of Spark including its advantages over MapReduce; and explanations of Spark concepts like RDDs, transformations, and actions. The document serves to introduce the topics that will be covered in the talk.
The document summarizes Spark SQL, which is a Spark module for structured data processing. It introduces key concepts like RDDs, DataFrames, and interacting with data sources. The architecture of Spark SQL is explained, including how it works with different languages and data sources through its schema RDD abstraction. Features of Spark SQL are covered such as its integration with Spark programs, unified data access, compatibility with Hive, and standard connectivity.
Big Data raises challenges about how to process such vast pool of raw data and how to aggregate value to our lives. For addressing these demands an ecosystem of tools named Hadoop was conceived.
Tame the small files problem and optimize data layout for streaming ingestion...Flink Forward
Flink Forward San Francisco 2022.
In modern data platform architectures, stream processing engines such as Apache Flink are used to ingest continuous streams of data into data lakes such as Apache Iceberg. Streaming ingestion to iceberg tables can suffer by two problems (1) small files problem that can hurt read performance (2) poor data clustering that can make file pruning less effective. To address those two problems, we propose adding a shuffling stage to the Flink Iceberg streaming writer. The shuffling stage can intelligently group data via bin packing or range partition. This can reduce the number of concurrent files that every task writes. It can also improve data clustering. In this talk, we will explain the motivations in details and dive into the design of the shuffling stage. We will also share the evaluation results that demonstrate the effectiveness of smart shuffling.
by
Gang Ye & Steven Wu
The document provides a comparative analysis of Apache Hadoop and Apache Spark, two popular platforms for big data analytics. It discusses their key features, capabilities, strengths, limitations, use cases and provides a recommendation on selecting the right tool based on specific business needs and data processing requirements.
This document provides an overview and comparison of RDBMS, Hadoop, and Spark. It introduces RDBMS and describes its use cases such as online transaction processing and data warehouses. It then introduces Hadoop and describes its ecosystem including HDFS, YARN, MapReduce, and related sub-modules. Common use cases for Hadoop are also outlined. Spark is then introduced along with its modules like Spark Core, SQL, and MLlib. Use cases for Spark include data enrichment, trigger event detection, and machine learning. The document concludes by comparing RDBMS and Hadoop, as well as Hadoop and Spark, and addressing common misconceptions about Hadoop and Spark.
Compare and contrast big data processing platforms RDBMS, Hadoop, and Spark. pros and cons of each platform are discussed. Business use cases are also included.
Apache Hadoop is a framework for distributed storage and processing of large datasets across clusters of commodity hardware. It provides HDFS for distributed file storage and MapReduce as a programming model for distributed computations. Hadoop includes other technologies like YARN for resource management, Spark for fast computation, HBase for NoSQL database, and tools for data analysis, transfer, and security. Hadoop can run on-premise or in cloud environments and supports analytics workloads.
The Apache Hadoop software library is essentially a framework that allows for the distributed processing of large datasets across clusters of computers using a simple programming model. Hadoop can scale up from single servers to thousands of machines, each offering local computation and storage.
Hadoop is an open-source framework for storing and processing large datasets in a distributed computing environment. It allows for massive data storage, enormous processing power, and the ability to handle large numbers of concurrent tasks across clusters of commodity hardware. The framework includes Hadoop Distributed File System (HDFS) for reliable data storage and MapReduce for parallel processing of large datasets. An ecosystem of related projects like Pig, Hive, HBase, Sqoop and Flume extend the functionality of Hadoop.
Apache Spark is an open source framework for large-scale data processing. It was originally developed at UC Berkeley and provides fast, easy-to-use tools for batch and streaming data. Spark features include SQL queries, machine learning, streaming, and graph processing. It is up to 100 times faster than Hadoop for iterative algorithms and interactive queries due to its in-memory processing capabilities. Spark uses Resilient Distributed Datasets (RDDs) that allow data to be reused across parallel operations.
This document provides an overview of the big data technology stack, including the data layer (HDFS, S3, GPFS), data processing layer (MapReduce, Pig, Hive, HBase, Cassandra, Storm, Solr, Spark, Mahout), data ingestion layer (Flume, Kafka, Sqoop), data presentation layer (Kibana), operations and scheduling layer (Ambari, Oozie, ZooKeeper), and concludes with a brief biography of the author.
Big Data Applications with Java discusses various big data technologies including Apache Hadoop, Apache Spark, Apache Kafka, and Apache Cassandra. It defines big data as huge volumes of data that cannot be processed using traditional approaches due to constraints on storage and processing time. The document then covers characteristics of big data like volume, velocity, variety, veracity, variability, and value. It provides overviews of Apache Hadoop and its ecosystem including HDFS and MapReduce. Apache Spark is introduced as an enhancement to MapReduce that processes data faster in memory. Apache Kafka and Cassandra are also summarized as distributed streaming and database platforms respectively. The document concludes by comparing Hadoop and Spark, outlining their relative performance, costs, processing capabilities,
Comparison between RDBMS, Hadoop and Apache based on parameters like Data Variety, Data Storage, Querying, Cost, Schema, Speed, Data Objects, Hardware profile, and Used cases. It also mentions benefits and limitations.
This document provides an overview of big data and Hadoop. It discusses what big data is, its types including structured, semi-structured and unstructured data. Some key sources of big data are also outlined. Hadoop is presented as a solution for managing big data through its core components like HDFS for storage and MapReduce for processing. The Hadoop ecosystem including other related tools like Hive, Pig, Spark and YARN is also summarized. Career opportunities in working with big data are listed in the end.
The document discusses big data, including what it is, sources of big data like social media and stock exchange data, and the three Vs of big data - volume, velocity, and variety. It then discusses Hadoop, the open-source framework for distributed storage and processing of large datasets across clusters of computers. Key components of Hadoop include HDFS for distributed storage, MapReduce for distributed computation, and YARN which manages computing resources. The document also provides overviews of Pig and Jaql, programming languages used for analyzing data in Hadoop.
Exploiting Apache Spark's Potential Changing Enormous Information Investigati...rajeshseo5
By providing a powerful, adaptable, and effective framework for processing and analyzing massive datasets, Apache Spark has revolutionized big data analytics. It is the preferred choice for both data engineers and data scientists due to its lightning-fast processing capabilities, extensive ecosystem, and support for various data processing tasks. Spark is poised to play a crucial role in the future of big data analytics by driving innovation and uncovering insights from massive datasets with continued development and adoption.
Find more information @ https://olete.in/?subid=165&subcat=Apache Spark
Presented By :- Rahul Sharma
B-Tech (Cloud Technology & Information Security)
2nd Year 4th Sem.
Poornima University (I.Nurture),Jaipur
www.facebook.com/rahulsharmarh18
Brief Introduction about Hadoop and Core Services.Muthu Natarajan
I have given quick introduction about Hadoop, Big Data, Business Intelligence and other core services and program involved to use Hadoop as a successful tool for Big Data analysis.
My true understanding in Big-Data:
“Data” become “information” but now big data bring information to “Knowledge” and ‘knowledge” becomes “Wisdom” and “Wisdom” turn into “Business” or “Revenue”, All if you use promptly & timely manner
Big Data Warsaw v 4 I "The Role of Hadoop Ecosystem in Advance Analytics" - R...Dataconomy Media
What is Big Data? What is Hadoop? What is MapReduce? How do the other components such as: Oozie, Hue, Hive, Impala works? Which are the main Hadoop distributions? What is Spark? What are the differences between Batch and Streaming processing? What are some Business Intelligence Solutions by focusing on some business cases?
Big data is a combination of structured, semi-structured and unstructured data collected by organizations that can be mined for information and used in machine learning projects. Systems that process and store big data have become common in organizations, combined with tools that support big data analytics uses such as improving operations, providing better customer performance, and creating personalized marketing campaigns. Hadoop is an open-source framework for distributed storage and processing of large data sets across clusters of computers. It includes projects like HDFS, MapReduce, YARN, and common utilities.
The document provides an overview of Hadoop, including:
- What Hadoop is and its core modules like HDFS, YARN, and MapReduce.
- Reasons for using Hadoop like its ability to process large datasets faster across clusters and provide predictive analytics.
- When Hadoop should and should not be used, such as for real-time analytics versus large, diverse datasets.
- Options for deploying Hadoop including as a service on cloud platforms, on infrastructure as a service providers, or on-premise with different distributions.
- Components that make up the Hadoop ecosystem like Pig, Hive, HBase, and Mahout.
Predictably Improve Your B2B Tech Company's Performance by Leveraging DataKiwi Creative
Harness the power of AI-backed reports, benchmarking and data analysis to predict trends and detect anomalies in your marketing efforts.
Peter Caputa, CEO at Databox, reveals how you can discover the strategies and tools to increase your growth rate (and margins!).
From metrics to track to data habits to pick up, enhance your reporting for powerful insights to improve your B2B tech company's marketing.
- - -
This is the webinar recording from the June 2024 HubSpot User Group (HUG) for B2B Technology USA.
Watch the video recording at https://youtu.be/5vjwGfPN9lw
Sign up for future HUG events at https://events.hubspot.com/b2b-technology-usa/
STATATHON: Unleashing the Power of Statistics in a 48-Hour Knowledge Extravag...sameer shah
"Join us for STATATHON, a dynamic 2-day event dedicated to exploring statistical knowledge and its real-world applications. From theory to practice, participants engage in intensive learning sessions, workshops, and challenges, fostering a deeper understanding of statistical methodologies and their significance in various fields."
Beyond the Basics of A/B Tests: Highly Innovative Experimentation Tactics You...Aggregage
This webinar will explore cutting-edge, less familiar but powerful experimentation methodologies which address well-known limitations of standard A/B Testing. Designed for data and product leaders, this session aims to inspire the embrace of innovative approaches and provide insights into the frontiers of experimentation!
End-to-end pipeline agility - Berlin Buzzwords 2024Lars Albertsson
We describe how we achieve high change agility in data engineering by eliminating the fear of breaking downstream data pipelines through end-to-end pipeline testing, and by using schema metaprogramming to safely eliminate boilerplate involved in changes that affect whole pipelines.
A quick poll on agility in changing pipelines from end to end indicated a huge span in capabilities. For the question "How long time does it take for all downstream pipelines to be adapted to an upstream change," the median response was 6 months, but some respondents could do it in less than a day. When quantitative data engineering differences between the best and worst are measured, the span is often 100x-1000x, sometimes even more.
A long time ago, we suffered at Spotify from fear of changing pipelines due to not knowing what the impact might be downstream. We made plans for a technical solution to test pipelines end-to-end to mitigate that fear, but the effort failed for cultural reasons. We eventually solved this challenge, but in a different context. In this presentation we will describe how we test full pipelines effectively by manipulating workflow orchestration, which enables us to make changes in pipelines without fear of breaking downstream.
Making schema changes that affect many jobs also involves a lot of toil and boilerplate. Using schema-on-read mitigates some of it, but has drawbacks since it makes it more difficult to detect errors early. We will describe how we have rejected this tradeoff by applying schema metaprogramming, eliminating boilerplate but keeping the protection of static typing, thereby further improving agility to quickly modify data pipelines without fear.
Learn SQL from basic queries to Advance queriesmanishkhaire30
Dive into the world of data analysis with our comprehensive guide on mastering SQL! This presentation offers a practical approach to learning SQL, focusing on real-world applications and hands-on practice. Whether you're a beginner or looking to sharpen your skills, this guide provides the tools you need to extract, analyze, and interpret data effectively.
Key Highlights:
Foundations of SQL: Understand the basics of SQL, including data retrieval, filtering, and aggregation.
Advanced Queries: Learn to craft complex queries to uncover deep insights from your data.
Data Trends and Patterns: Discover how to identify and interpret trends and patterns in your datasets.
Practical Examples: Follow step-by-step examples to apply SQL techniques in real-world scenarios.
Actionable Insights: Gain the skills to derive actionable insights that drive informed decision-making.
Join us on this journey to enhance your data analysis capabilities and unlock the full potential of SQL. Perfect for data enthusiasts, analysts, and anyone eager to harness the power of data!
#DataAnalysis #SQL #LearningSQL #DataInsights #DataScience #Analytics
ViewShift: Hassle-free Dynamic Policy Enforcement for Every Data LakeWalaa Eldin Moustafa
Dynamic policy enforcement is becoming an increasingly important topic in today’s world where data privacy and compliance is a top priority for companies, individuals, and regulators alike. In these slides, we discuss how LinkedIn implements a powerful dynamic policy enforcement engine, called ViewShift, and integrates it within its data lake. We show the query engine architecture and how catalog implementations can automatically route table resolutions to compliance-enforcing SQL views. Such views have a set of very interesting properties: (1) They are auto-generated from declarative data annotations. (2) They respect user-level consent and preferences (3) They are context-aware, encoding a different set of transformations for different use cases (4) They are portable; while the SQL logic is only implemented in one SQL dialect, it is accessible in all engines.
#SQL #Views #Privacy #Compliance #DataLake
Codeless Generative AI Pipelines
(GenAI with Milvus)
https://ml.dssconf.pl/user.html#!/lecture/DSSML24-041a/rate
Discover the potential of real-time streaming in the context of GenAI as we delve into the intricacies of Apache NiFi and its capabilities. Learn how this tool can significantly simplify the data engineering workflow for GenAI applications, allowing you to focus on the creative aspects rather than the technical complexities. I will guide you through practical examples and use cases, showing the impact of automation on prompt building. From data ingestion to transformation and delivery, witness how Apache NiFi streamlines the entire pipeline, ensuring a smooth and hassle-free experience.
Timothy Spann
https://www.youtube.com/@FLaNK-Stack
https://medium.com/@tspann
https://www.datainmotion.dev/
milvus, unstructured data, vector database, zilliz, cloud, vectors, python, deep learning, generative ai, genai, nifi, kafka, flink, streaming, iot, edge
The Ipsos - AI - Monitor 2024 Report.pdfSocial Samosa
According to Ipsos AI Monitor's 2024 report, 65% Indians said that products and services using AI have profoundly changed their daily life in the past 3-5 years.
Build applications with generative AI on Google CloudMárton Kodok
We will explore Vertex AI - Model Garden powered experiences, we are going to learn more about the integration of these generative AI APIs. We are going to see in action what the Gemini family of generative models are for developers to build and deploy AI-driven applications. Vertex AI includes a suite of foundation models, these are referred to as the PaLM and Gemini family of generative ai models, and they come in different versions. We are going to cover how to use via API to: - execute prompts in text and chat - cover multimodal use cases with image prompts. - finetune and distill to improve knowledge domains - run function calls with foundation models to optimize them for specific tasks. At the end of the session, developers will understand how to innovate with generative AI and develop apps using the generative ai industry trends.
3. RDBMS
Stands for ‘Relational Database Management
System’
It is a database that stores data in a structured
format using rows and columns.
One can execute queries on the data like
adding, updating, and searching for values.
It also provides a visual representation of the
data.
It is "relational" because the values within each
table are related to each other.
The relational structure makes it possible to run
queries across multiple tables at once.
Structured Query Language is the standard
programming language used to access the
database.
ADVANTAGES
Addresses the need for integrating,
managing and analysing data from
multiple sources across on-
premises and cloud environments
Ease to locate and access specific
values within the database
High flexibility due to storage,
retrieval and publishing of JSON data
within a relational database
EXAMPLES
4. It is a matter of the past when data were limited.
Now, the world has already experienced the power
of Big Data, and the same is used to analyze to
frame different business strategies and others.
Apache Hadoop is one of the kinds of open-
source platforms that we can use to store and
process relatively large datasets amounting from
gigabytes to petabytes. This open-source allows
multiple computers to make clusters and analyze
the large datasets in parallel and effectively.
5. Four main
components
of
the Hadoop
ecosystem:
HADOOP DISTRIBUTED FILE SYSTEM (HDFS)
A primary data storage system that runs on commodity
hardware and manages enormous data collections. It
also has a high data throughput and a high fault
tolerance.
YET ANOTHER RESOURCE NEGOTIATOR (YARN)
YARN is a cluster resource manager that schedules
tasks and assigns resources (such as CPU and memory)
to applications.
1
2
3
HADOOP MAPREDUCE
Breaks down the big data processing tasks into smaller
ones, distributes them across different nodes, and then
runs each one.
4
HADOOP COMMON (HADOOP CORE):
A collection of common libraries and utilities on which
the other three modules rely.
6. Importance
of
Hadoop
Ability to quickly store and handle large amounts of any type of data
That's an important concern as data volumes and varieties continue to grow, notably from social
media and the Internet of Things (IoT).
Computer processing power.
Hadoop's distributed computing model efficiently processes large amounts of data. The more
computing nodes you use, the more processing power you have.
Fault tolerance
Data and application processing are protected against hardware failure. If a node fails, jobs are
automatically transferred to other nodes, ensuring that the distributed computing does not fail.
Multiple copies of all data are stored automatically.
Flexibility
Unlike traditional relational databases, we don’t have to preprocess data before storing it. We can
store as much data as we want and decide how to use it later. It includes unstructured data like text,
pictures, and videos.
Low cost
The open-source framework is free and stores large amounts of data by using commodity
hardware.
Scalability
By simply adding nodes, we can easily expand our system to handle more data. A little administrative
is required.
7. Challenges in using Hadoop
1 MAPREDUCE PROGRAMMING ISN'T SUITED FOR EVERY PROBLEM
It performs well for simple information requests and problems that can be broken down into independent units, but it is
inefficient for iterative and interactive analytic operations. MapReduce is file-intensive. Iterative algorithms require
multiple map-shuffle/sort-reduce phases to complete because the nodes mainly communicate through sorts and
shuffles. This results in so many files being created between MapReduce phases, which is inefficient for advanced
analytics computing.
2
It can be difficult to find entry-level programmers who have adequate Java expertise to be productive with MapReduce.
That's one reason distribution providers are racing to put relational (SQL) technology on top of Hadoop. Programmers
with SQL skills are easy to find than MapReduce skills. And, Hadoop administration seems a mix of art and science,
requiring a basic understanding of operating systems, hardware, and Hadoop kernel settings.
THERE’S A WIDELY ACKNOWLEDGED TALENT GAP
3
Another concern is the fragmented data protection challenges, which are being handled by new tools and technology.
The Kerberos authentication protocol is a significant step toward securing Hadoop environments.
DATA SECURITY
4
Hadoop lacks user-friendly, full-featured tools for data management, data cleansing, governance, and metadata services.
FULL-FLEDGED DATA GOVERNANCE AND MANAGEMENT
8. Apache Spark began in 2009 as a research project at UC Berkeley's AMPLab focused on data-
intensive application areas.
Apache Spark is an open-source, distributed processing system used for big data workloads.
For rapid analytic queries against any quantity of data, it uses in-memory caching and efficient
query execution.
It allows code reuse across different workloads—batch processing, interactive queries, real-
time analytics, machine learning, and graph processing—and provides development APIs in
Java, Scala, Python, and R.
Spark's objective was to build a new framework that was optimised for quick iterative
processing, such as machine learning and interactive data analysis, while preserving Hadoop
MapReduce's scalability and fault tolerance.
The primary importance of Apache Spark in the Big data industry is because of its in-memory
data processing that makes it a high-speed data processing engine compared to MapReduce.
Apache Spark delivers a better-integrated framework which supports all ranges of Big data
formats like batch data, text data, real-time streaming data, graphical data, etc.
Apache Spark
9. Core Components
Spark SQL and Data Frames: Spark SQL
allows users to run SQL and HQL queries in
order to process structured and semi-
structured data.
Spark Streaming: Spark streaming facilitates
the processing of live stream data i.e. log files.
It also contains APIs to manipulate data
streams.
MLib Machine Learning: MLib is the Spark
library with machine learning functionality. It
contains various machine learning algorithms
such as regressions, clustering, collaborative
filtering, classification, etc.
GraphX: The library that supports graph
computation is known as GraphX. It
enables users to perform graph
manipulation. It also provides graph
computation algorithms.
Apache Spark Core API: It provides a
platform to execute Spark applications.
Apache Spark framework consists of the main five components that are responsible
for the functioning of the Spark.
10. Advantages
Speed: For large-scale data processing, Spark is 100 times quicker than Hadoop. Apache Spark
utilizes an in-memory (RAM) processing architecture.
Ease of Use: Apache Spark provides simple APIs for working with big datasets. It has over 80
high-level operators that make creating parallel programs a breeze.
Advanced Analytics: Spark does more than only support 'MAP' and 'reduce'. Machine learning
(ML), graph algorithms, streaming data, SQL queries, and other features are also supported.
Apache Spark is faster than most data warehouses.
Dynamic: Apache Spark allows simple creation of parallel apps. Over 80 high-level operators
are available through Spark.
Multilingual: Python, Java, Scala, and more programming languages are supported by Apache
Spark.
Powerful: Because of its low-latency in-memory data processing capacity, Apache Spark can
handle a wide range of analytics problems. It has well-developed libraries for graph analytics
and machine learning techniques.
Open-source: The best thing about Apache Spark is, it has a massive Open-source community
behind it.