Hive is a data warehouse system for querying large datasets using SQL. Version 0.6 added views, multiple databases, dynamic partitioning, and storage handlers. Version 0.7 will focus on concurrency control, statistics collection, indexing, and performance improvements. Hive has become a top-level Apache project and aims to improve security, testing, and integration with other Hadoop components in the future.
The document discusses moving from traditional ETL processes to "analytics with no ETL" using Hadoop. It describes how Hadoop currently supports some ETL functions by storing raw and transformed data together. However, this still requires periodic loading of new data. The vision is to support complex schemas, perform background format conversion incrementally, and enable schema inference and evolution to allow analyzing data as it arrives without explicit ETL steps. This would provide an up-to-date, performant single view of all data.
This document provides an overview of Apache Hadoop and HBase. It begins with an introduction to why big data is important and how Hadoop addresses storing and processing large amounts of data across commodity servers. The core components of Hadoop, HDFS for storage and MapReduce for distributed processing, are described. An example MapReduce job is outlined. The document then introduces the Hadoop ecosystem, including Apache HBase for random read/write access to data stored in Hadoop. Real-world use cases of Hadoop at companies like Yahoo, Facebook and Twitter are briefly mentioned before addressing questions.
The document summarizes Carl Steinbach's presentation on SQL on Hadoop. It discusses how earlier systems like Hive had limitations for analytics workloads due to using MapReduce. A new architecture runs PostgreSQL on worker nodes co-located with HDFS data to enable push-down query processing for better performance. Citus Data's CitusDB product was presented as an example of this architecture, allowing SQL queries to efficiently analyze petabytes of data stored in HDFS.
Hadoop is an open source software framework that supports data-intensive distributed applications. Hadoop is licensed under the Apache v2 license. It is therefore generally known as Apache Hadoop. Hadoop has been developed, based on a paper originally written by Google on MapReduce system and applies concepts of functional programming. Hadoop is written in the Java programming language and is the highest-level Apache project being constructed and used by a global community of contributors. Hadoop was developed by Doug Cutting and Michael J. Cafarella. And just don't overlook the charming yellow elephant you see, which is basically named after Doug's son's toy elephant! The topics covered in presentation are: 1. Big Data Learning Path 2.Big Data Introduction 3. Hadoop and its Eco-system 4.Hadoop Architecture 5.Next Step on how to setup Hadoop
This document discusses Hadoop and big data. It begins with definitions of big data and how Hadoop can help with large, complex datasets. It then discusses how Hadoop works with other tools like Pig and Hive. The document outlines different scenarios for big data and whether Hadoop is suitable. It also discusses how big data frameworks have evolved from Google papers. Finally, it provides examples of big data use cases and how education is being democratized with big data tools.
HBase is a distributed, column-oriented database that runs on top of Hadoop and HDFS, providing Bigtable-like capabilities for massive tables of structured and unstructured data. It is modeled after Google's Bigtable and provides a distributed, scalable, versioned storage system with strong consistency for random read/write access to billions of rows and millions of columns. HBase is well-suited for handling large datasets and providing real-time read/write access across clusters of commodity servers.
Hadoop is commonly used for processing large swaths of data in batch. While many of the necessary building blocks for data processing exist within the Hadoop ecosystem – HDFS, MapReduce, HBase, Hive, Pig, Oozie, and so on – it can be a challenge to assemble and operationalize them as a production ETL platform. This presentation covers one approach to data ingest, organization, format selection, process orchestration, and external system integration, based on collective experience acquired across many production Hadoop deployments.
This document provides an introduction and overview of Apache Hive, including what it is, its architecture and components, how it is used in production, and performance considerations. Hive is an open source data warehouse system for Hadoop that allows users to query data using SQL-like language and scales to petabytes of data. It works by compiling queries into a directed acyclic graph of MapReduce jobs for execution. The document outlines Hive's architecture, components like the metastore and Thrift server, and how organizations use it for log processing, data mining and business intelligence tasks.
Hive is a data warehousing system built on Hadoop that allows users to query data using SQL. It addresses issues with using Hadoop for analytics like programmability and metadata. Hive uses a metastore to manage metadata and supports structured data types, SQL queries, and custom MapReduce scripts. At Facebook, Hive is used for analytics tasks like summarization, ad hoc analysis, and data mining on over 180TB of data processed daily across a Hadoop cluster.
Facebook's data warehouse processes petabytes of data daily to support data-driven development, business decisions, and machine learning. Hive provides a SQL interface and metadata management on top of Hadoop to simplify querying large datasets. At Facebook, Hive is used extensively for reporting, ad hoc analysis, and assembling machine learning training data. The Hive cluster processes 800TB of data and 10,000-25,000 jobs daily. Future work includes improving performance, scaling to support dynamic workloads and data growth, enabling incremental loads, and full SQL support.
My presentation deck from Hadoop Summit 2015. Video available at https://www.youtube.com/watch?v=EUz6Pu1lBHQ.
A walk-thru of core Hadoop, the ecosystem tools, and Hortonworks Data Platform (HDP) followed by code examples in MapReduce (Java and C#), Pig, and Hive. Presented at the Atlanta .NET User Group meeting in July 2014.
The document discusses LatentView Analytics and provides an overview of data processing frameworks and MapReduce. It introduces LatentView Analytics, describing its services, partners, and experience. It then discusses distributed and parallel processing frameworks, providing examples like Hadoop, Spark, and Storm. It also provides a brief history of Hadoop, describing its key developments from 1999 to present day in addressing challenges of indexing, crawling, distributed processing etc. Finally, it explains the MapReduce process and provides a simple example to illustrate mapping and reducing functions.
Hadoop is an open-source framework for distributed storage and processing of large datasets across clusters of commodity hardware. It uses a programming model called MapReduce where developers write mapping and reducing functions that are automatically parallelized and executed on a large cluster. Hadoop also includes HDFS, a distributed file system that stores data across nodes providing high bandwidth. Major companies like Yahoo, Google and IBM use Hadoop to process large amounts of data from users and applications.
Hadoop is an open-source framework for distributed storage and processing of large datasets across clusters of commodity hardware. It was created to support applications handling large datasets operating on many servers. Key Hadoop technologies include MapReduce for distributed computing, and HDFS for distributed file storage inspired by Google File System. Other related Apache projects extend Hadoop capabilities, like Pig for data flows, Hive for data warehousing, and HBase for NoSQL-like big data. Hadoop provides an effective solution for companies dealing with petabytes of data through distributed and parallel processing.
The document compares and contrasts the SAS and Spark frameworks. It provides an overview of their programming models, with SAS using data steps and procedures while Spark uses Scala and distributed datasets. Examples are shown of common tasks like loading data, sorting, grouping, and regression in both SAS Proc SQL and Spark SQL. Spark MLlib is described as Spark's machine learning library, in contrast to SAS Stats. Finally, Spark Streaming is demonstrated for loading and querying streaming data from Kafka. The key takeaways recommend trying Spark for large data, distributed computing, better control of code, open source licensing, or leveraging Hadoop data.
This document provides an overview of 4 solutions for processing big data using Hadoop and compares them. Solution 1 involves using core Hadoop processing without data staging or movement. Solution 2 uses BI tools to analyze Hadoop data after a single CSV transformation. Solution 3 creates a data warehouse in Hadoop after a single transformation. Solution 4 implements a traditional data warehouse. The solutions are then compared based on benefits like cloud readiness, parallel processing, and investment required. The document also includes steps for installing a Hadoop cluster and running sample MapReduce jobs and Excel processing.
Cheetah is a custom data warehouse system built on top of Hadoop that provides high performance for storing and querying large datasets. It uses a virtual view abstraction over star and snowflake schemas to provide a simple yet powerful SQL-like query language. The system architecture utilizes MapReduce to parallelize query execution across many nodes. Cheetah employs columnar data storage and compression, multi-query optimization, and materialized views to improve query performance. Based on evaluations, Cheetah can efficiently handle both small and large queries and outperforms single-query execution when processing batches of queries together.
This document summarizes a presentation about Penton's use of Hadoop and related technologies to improve their data processing capabilities. It describes Penton's data challenges with siloed and slow ETL processes. A proof of concept used HBase for ingesting and manipulating CSV files, Drools for data scrubbing rules, and MapReduce jobs for exports. This new architecture improved performance for mapping and uploads. Lessons included HBase tuning and challenges translating SQL queries to scans. The new system provided better scalability and relieved load from their RDBMS.
Given on a free DevelopMentor webinar. A high level overview of big data and the need for Hadoop. Also covers Pig, Hive, Yarn, and the future of Hadoop.
Hadoop-HBase framework to ingest and process TBs of data. A case study in migrating a legacy Oracle implementation to Hadoop.
ESD (Etu Solution Day) 2014, Track D Slide. A presentation about Impala and Spark introduction and hands-on.
- Hadoop was created to allow processing of large datasets in a distributed, fault-tolerant manner. It was originally developed by Doug Cutting and Mike Cafarella at Nutch in response to the growing amounts of data and computational needs at Google and other companies. - The core of Hadoop consists of Hadoop Distributed File System (HDFS) for storage and Hadoop MapReduce for distributed processing. It also includes utilities like Hadoop Common for file system access and other basic functionality. - Hadoop's goals were to process multi-petabyte datasets across commodity hardware in a reliable, flexible and open source way. It assumes failures are expected and handles them to provide fault tolerance.
Hive provides an SQL-like interface to query and analyze large datasets stored in Hadoop. It allows users to model data as tables and analyze the data using SQL queries without needing to learn MapReduce programming. Hive generates MapReduce jobs behind the scenes to parallelize the processing and generate results. The system works by storing metadata about the tables in a metastore and then using this metadata to generate MapReduce jobs for queries. This allows Hive to provide a more programmer-friendly interface compared to raw MapReduce for working with large datasets.
1) Hive is a data warehouse infrastructure built on top of Hadoop for querying large datasets stored in HDFS. It provides SQL-like capabilities to analyze data and supports complex queries using a MapReduce execution engine. 2) Hive compiles SQL queries into a directed acyclic graph (DAG) of MapReduce jobs that are executed by Hadoop. The metadata is stored in a metastore (typically an RDBMS). 3) Hive supports advanced features like partitioning, bucketing, acid transactions, and complex types. It can handle petabyte-scale datasets and integrates with the Hadoop ecosystem but has limitations for low-latency queries.
The document provides an overview of big data and Hadoop fundamentals. It discusses what big data is, the characteristics of big data, and how it differs from traditional data processing approaches. It then describes the key components of Hadoop including HDFS for distributed storage, MapReduce for distributed processing, and YARN for resource management. HDFS architecture and features are explained in more detail. MapReduce tasks, stages, and an example word count job are also covered. The document concludes with a discussion of Hive, including its use as a data warehouse infrastructure on Hadoop and its query language HiveQL.
This document summarizes a meetup about Big Data and SQL on Hadoop. The meetup included discussions on what Hadoop is, why SQL on Hadoop is useful, what Hive is, and introduced IBM's BigInsights software for running SQL on Hadoop with improved performance over other solutions. Key topics included HDFS file storage, MapReduce processing, Hive tables and metadata storage, and how BigInsights provides a massively parallel SQL engine instead of relying on MapReduce.
This document discusses Microsoft's efforts to make big data technologies like Hadoop more accessible through its products. It describes Hadoop, MapReduce, HDFS, and other big data concepts. It then outlines Microsoft's project to create a Hadoop distribution that runs on Windows Server and Windows Azure, including building an ODBC driver to allow tools like Excel to query Hadoop. This will help bring big data to more business users and integrate it with Microsoft's existing BI technologies.
A primer on Big Data, Hadoop and Microsoft's implementation of Hadoop for Windows Server and Windows Azure.
Data Explosion - TBs of data generated everyday Solution – HDFS to store data and Hadoop Map-Reduce framework to parallelize processing of Data What is the catch? Hadoop Map Reduce is Java intensive Thinking in Map Reduce paradigm can get tricky
Apache Drill is an open source SQL query engine for big data that provides highly flexible and high performance querying of data stored in Hadoop and NoSQL systems. It allows for ad-hoc queries on schema-less data without requiring upfront modeling or ETL. Drill uses a distributed, columnar data store and late binding to optimize query execution across systems. The project is actively developed with the goal of releasing version 1.0 in late 2014.
Apache Drill is a distributed SQL query engine that enables fast analytics over NoSQL databases and distributed file systems. It has a plugin-based architecture that allows it to access different data sources. For NoSQL databases, Drill leverages secondary indexes to generate index-based query plans for predicates on non-key columns. For distributed file systems like HDFS, Drill performs partition pruning based on directory metadata and filter pushdown based on Parquet row group statistics to speed up queries. Drill's extensible framework allows data sources to provide metadata like indexes, statistics, and partitioning functions to optimize query execution.
The document discusses best practices and performance tuning for U-SQL in Azure Data Lake. It provides an overview of U-SQL query execution, including the job scheduler, query compilation process, and vertex execution model. The document also covers techniques for analyzing and optimizing U-SQL job performance, including analyzing the critical path, using heat maps, optimizing AU usage, addressing data skew, and query tuning techniques like data loading tips, partitioning, predicate pushing and column pruning.
SQL on Hadoop Looking for the correct tool for your SQL-on-Hadoop use case? There is a long list of alternatives to choose from; how to select the correct tool? The tool selection is always based on use case requirements. Read more on alternatives and our recommendations.
Hive is a data warehouse infrastructure built on top of Hadoop for querying and managing large datasets stored in Hadoop Distributed File System (HDFS). It provides SQL-like interface to query data, and uses MapReduce to parallelize the execution of queries across clusters. The document discusses Hive architecture, how it works, HiveQL syntax, data types, storage formats, DDL commands, data loading, functions, optimizations and getting started with Hive.
Recorded at SpringOne2GX 2013 in Santa Clara, CA Speaker: Adam Shook This session assumes absolutely no knowledge of Apache Hadoop and will provide a complete introduction to all the major aspects of the Hadoop ecosystem of projects and tools. If you are looking to get up to speed on Hadoop, trying to work out what all the Big Data fuss is about, or just interested in brushing up your understanding of MapReduce, then this is the session for you. We will cover all the basics with detailed discussion about HDFS, MapReduce, YARN (MRv2), and a broad overview of the Hadoop ecosystem including Hive, Pig, HBase, ZooKeeper and more. Learn More about Spring XD at: http://projects.spring.io/spring-xd Learn More about Gemfire XD at: http://www.gopivotal.com/big-data/pivotal-hd