Business intelligence analyzes data to provide actionable information for decision making. Big data is a $50 billion market by 2017, referring to technologies that capture, store, manage and analyze large variable data collections. Hadoop is an open source framework for distributed storage and processing of large data sets on commodity hardware, enabling businesses to gain insight from massive amounts of structured and unstructured data. It involves components like HDFS for data storage, MapReduce for processing, and others for accessing, storing, integrating, and managing data.
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.
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
Hadoop is a distributed processing framework for large datasets. It utilizes HDFS for storage and MapReduce as its programming model. The Hadoop ecosystem has expanded to include many other tools. YARN was developed to address limitations in the original Hadoop architecture. It provides a common platform for various data processing engines like MapReduce, Spark, and Storm. YARN improves scalability, utilization, and supports multiple workloads by decoupling cluster resource management from application logic. It allows different applications to leverage shared Hadoop cluster resources.
The document discusses the Hadoop ecosystem, which includes core Apache Hadoop components like HDFS, MapReduce, YARN, as well as related projects like Pig, Hive, HBase, Mahout, Sqoop, ZooKeeper, Chukwa, and HCatalog. It provides overviews and diagrams explaining the architecture and purpose of each component, positioning them as core functionality that speeds up Hadoop processing and makes Hadoop more usable and accessible.
HBase is a column-oriented NoSQL database that provides random real-time read/write access to big data stored in Hadoop's HDFS. It is modeled after Google's Bigtable and sits on top of HDFS to allow fast access to large datasets. HBase architecture includes HMaster, HRegionServers, ZooKeeper, and HDFS. HMaster manages metadata and load balancing while HRegionServers serve read/write requests directly from clients. ZooKeeper coordinates the cluster and HDFS provides storage. Data is stored in tables divided into regions hosted by HRegionServers.
The document provides an introduction to NoSQL and HBase. It discusses what NoSQL is, the different types of NoSQL databases, and compares NoSQL to SQL databases. It then focuses on HBase, describing its architecture and components like HMaster, regionservers, Zookeeper. It explains how HBase stores and retrieves data, the write process involving memstores and compaction. It also covers HBase shell commands for creating, inserting, querying and deleting data.
Introduction to Hadoop Ecosystem was presented to Lansing Java User Group on 2/17/2015 by Vijay Mandava and Lan Jiang. The demo was built on top of HDP 2.2 and AWS cloud.
This document discusses integrating Apache Hive and HBase. It provides an overview of Hive and HBase, describes use cases for querying HBase data using Hive SQL, and outlines features and improvements for Hive and HBase integration. Key points include mapping Hive schemas and data types to HBase tables and columns, pushing filters and other operations down to HBase, and using a storage handler to interface between Hive and HBase. The integration allows analysts to query both structured Hive and unstructured HBase data using a single SQL interface.
The Hadoop Path by Subash DSouza of Archangel Technology Consultants, LLC.Data Con LA
Hadoop is moving towards improved security and real-time analytics. For security, Hadoop vendors have made acquisitions and implemented features like Kerberos authentication and Apache Sentry authorization. For real-time analytics, tools are focusing on real-time streaming (like Storm, Spark Streaming, and Samza) and real-time querying of data (like Hive on Tez, Impala, Drill, and Spark). The right tool depends on use cases, and enterprises should choose what is easiest and most scalable.
Hive is a data warehouse infrastructure tool used to process large datasets in Hadoop. It allows users to query data using SQL-like queries. Hive resides on HDFS and uses MapReduce to process queries in parallel. It includes a metastore to store metadata about tables and partitions. When a query is executed, Hive's execution engine compiles it into a MapReduce job which is run on a Hadoop cluster. Hive is better suited for large datasets and queries compared to traditional RDBMS which are optimized for transactions.
What are Hadoop Components? Hadoop Ecosystem and Architecture | EdurekaEdureka!
YouTube Link: https://youtu.be/ll_O9JsjwT4
** Big Data Hadoop Certification Training - https://www.edureka.co/big-data-hadoop-training-certification **
This Edureka PPT on "Hadoop components" will provide you with detailed knowledge about the top Hadoop Components and it will help you understand the different categories of Hadoop Components. This PPT covers the following topics:
What is Hadoop?
Core Components of Hadoop
Hadoop Architecture
Hadoop EcoSystem
Hadoop Components in Data Storage
General Purpose Execution Engines
Hadoop Components in Database Management
Hadoop Components in Data Abstraction
Hadoop Components in Real-time Data Streaming
Hadoop Components in Graph Processing
Hadoop Components in Machine Learning
Hadoop Cluster Management tools
Follow us to never miss an update in the future.
YouTube: https://www.youtube.com/user/edurekaIN
Instagram: https://www.instagram.com/edureka_learning/
Facebook: https://www.facebook.com/edurekaIN/
Twitter: https://twitter.com/edurekain
LinkedIn: https://www.linkedin.com/company/edureka
Castbox: https://castbox.fm/networks/505?country=in
There is a lot more to Hadoop than Map-Reduce. An increasing number of engineers and researchers involved in processing and analyzing large amount of data, regards Hadoop as an ever expanding ecosystem of open sources libraries, including NoSQL, scripting and analytics tools.
Hadoop World 2011: Hadoop and RDBMS with Sqoop and Other Tools - Guy Harrison...Cloudera, Inc.
The document discusses integrating Hadoop with relational databases. It describes scenarios where reference data is stored in an RDBMS and used in Hadoop, Hadoop is used for offline analytics on data stored in an RDBMS, and exporting MapReduce outputs to an RDBMS. It then presents a case study on extending SQOOP for optimized Oracle integration and compares performance with and without the extension. Other tools for Hadoop-RDBMS integration are also briefly outlined.
Apache hadoop introduction and architectureHarikrishnan K
Hadoop is an open-source framework for distributed storage and processing of large datasets across clusters of computers. It allows for the reliable, scalable and distributed processing of large data sets across clusters of commodity hardware. The core of Hadoop is a storage part known as Hadoop Distributed File System (HDFS) and a processing part known as MapReduce. HDFS provides distributed storage and MapReduce enables distributed processing of large datasets in a reliable, fault-tolerant and scalable manner. Hadoop has become popular for distributed computing as it is reliable, economical and scalable to handle large and varying amounts of data.
This document discusses leveraging major market opportunities with Microsoft Azure. It notes that worldwide cloud software revenue is expected to grow significantly between 2010-2017. By 2017, nearly $1 of every $5 spent on applications will be consumed via the cloud. It also notes that hybrid cloud deployments will be common for large enterprises by the end of 2017. The document then outlines several major enterprise workloads that can be moved to Azure, including test/development, SharePoint, SQL/business intelligence, application migration, SAP, and identity/Office 365. It provides examples of how partners can help customers with these types of migrations.
The document provides an overview of Hadoop and its ecosystem. It discusses the history and architecture of Hadoop, describing how it uses distributed storage and processing to handle large datasets across clusters of commodity hardware. The key components of Hadoop include HDFS for storage, MapReduce for processing, and additional tools like Hive, Pig, HBase, Zookeeper, Flume, Sqoop and Oozie that make up its ecosystem. Advantages are its ability to handle unlimited data storage and high speed processing, while disadvantages include lower speeds for small datasets and limitations on data storage size.
Introduction to Apache Hadoop. Includes Hadoop v.1.0 and HDFS / MapReduce to v.2.0. Includes Impala, Yarn, Tez and the entire arsenal of projects for Apache Hadoop.
This presentation discusses the follow topics
What is Hadoop?
Need for Hadoop
History of Hadoop
Hadoop Overview
Advantages and Disadvantages of Hadoop
Hadoop Distributed File System
Comparing: RDBMS vs. Hadoop
Advantages and Disadvantages of HDFS
Hadoop frameworks
Modules of Hadoop frameworks
Features of 'Hadoop‘
Hadoop Analytics Tools
The strategic relationship between Hortonworks and SAP enables SAP to resell Hortonworks Data Platform (HDP) and provide enterprise support for their global customer base. This means SAP customers can incorporate enterprise Hadoop as a complement within a data architecture that includes SAP HANA, Sybase and SAP BusinessObjects enabling a broad range of new analytic applications.
Apache Hadoop is a popular open-source framework for storing and processing large datasets across clusters of computers. It includes Apache HDFS for distributed storage, YARN for job scheduling and resource management, and MapReduce for parallel processing. The Hortonworks Data Platform is an enterprise-grade distribution of Apache Hadoop that is fully open source.
CDH is a popular distribution of Apache Hadoop and related projects that delivers scalable storage and distributed computing through Apache-licensed open source software. It addresses challenges in storing and analyzing large datasets known as Big Data. Hadoop is a framework for distributed processing of large datasets across computer clusters using simple programming models. Its core components are HDFS for storage, MapReduce for processing, and YARN for resource management. The Hadoop ecosystem also includes tools like Kafka, Sqoop, Hive, Pig, Impala, HBase, Spark, Mahout, Solr, Kudu, and Sentry that provide functionality like messaging, data transfer, querying, machine learning, search, and authorization.
Overview of big data & hadoop version 1 - Tony NguyenThanh Nguyen
Overview of Big data, Hadoop and Microsoft BI - version1
Big Data and Hadoop are emerging topics in data warehousing for many executives, BI practices and technologists today. However, many people still aren't sure how Big Data and existing Data warehouse can be married and turn that promise into value. This presentation provides an overview of Big Data technology and how Big Data can fit to the current BI/data warehousing context.
http://www.quantumit.com.au
http://www.evisional.com
Hadoop is an open source software framework that allows for distributed processing of large data sets across clusters of computers. It uses MapReduce as a programming model and HDFS for storage. Hadoop supports various big data applications like HBase for distributed column storage, Hive for data warehousing and querying, Pig and Jaql for data flow languages, and Hadoop ecosystem projects for tasks like system monitoring and machine learning.
An Introduction-to-Hive and its Applications and Implementations.pptxiaeronlineexm
This document provides an introduction to Hive, a data warehouse infrastructure tool used for querying and analyzing large datasets in Hadoop. It discusses how Hive resides on top of Hadoop and allows users to write SQL-like queries to process structured data using MapReduce. The document also describes Hive's architecture, which includes components like the metastore for storing metadata, the HiveQL process engine for compiling queries, and the execution engine for generating MapReduce jobs. It provides examples of how Hive queries are executed and processed via the different system components.
Big Data is a collection of large and complex data sets that cannot be processed using regular database management tools or processing applications. A lot of challenges such as capture, curation, storage, search, sharing, analysis, and visualization can be encountered while handling Big Data. On the other hand the Apache Hadoop software library is a framework that allows for the distributed processing of large data sets across clusters of computers using simple programming models. It is designed to scale up from single servers to thousands of machines, each offering local computation and storage. Big Data certification is one of the most recognized credentials of today.
For more details Click http://www.simplilearn.com/big-data-and-analytics/big-data-and-hadoop-training
hadoop eco system regarding big data analytics.pptxmrudulasb
The document provides an overview of the Hadoop ecosystem and its core components. It discusses HDFS, YARN, MapReduce, Spark, HBase, Hive, Avro, Zookeeper, and Pig. HDFS is the storage system and consists of NameNode and DataNodes. YARN resource manages and schedules jobs. MapReduce facilitates distributed processing of large datasets. Spark is a faster processing engine. HBase is a NoSQL database. Hive provides data analysis. Avro handles data serialization. Zookeeper coordinates services. Pig is a platform for analyzing large datasets using a scripting language.
Hive is a data warehouse infrastructure tool that allows users to query and analyze large datasets stored in Hadoop Distributed File System (HDFS) using SQL-like queries. It provides a mechanism to project structure onto this data and analyze it using tools familiar to analysts like SQL. Hive resides on top of Hadoop to summarize big data and makes querying and analyzing easy. It stores schema in a database and processed data into HDFS. Hive uses a SQL-like language called HiveQL to issue queries against data stored in HDFS.
This document provides information about Hadoop and its components. It discusses the history of Hadoop and how it has evolved over time. It describes key Hadoop components including HDFS, MapReduce, YARN, and HBase. HDFS is the distributed file system of Hadoop that stores and manages large datasets across clusters. MapReduce is a programming model used for processing large datasets in parallel. YARN is the cluster resource manager that allocates resources to applications. HBase is the Hadoop database that provides real-time random data access.
Hadoop is an open-source software framework for distributed storage and processing of large datasets across clusters of computers. It provides reliable storage through its Hadoop Distributed File System (HDFS) and allows for the distributed processing of large data sets across clusters of computers using simple programming models. Hadoop was created by Doug Cutting and Mike Cafarella to address the growing need to handle large datasets in a distributed computing environment.
The document provides an overview of Hadoop and its ecosystem. It discusses the history and architecture of Hadoop, describing how it uses distributed storage and processing to handle large datasets across clusters of commodity hardware. The key components of Hadoop include HDFS for storage, MapReduce for processing, and an ecosystem of related projects like Hive, HBase, Pig and Zookeeper that provide additional functions. Advantages are its ability to handle unlimited data storage and high speed processing, while disadvantages include lower speeds for small datasets and limitations on data storage size.
Hadoop is an open-source software framework for distributed storage and processing of large datasets across clusters of computers. It allows for the reliable, scalable, and distributed processing of large data sets across clusters of commodity hardware. The core of Hadoop includes a storage part called HDFS for reliable data storage, and a processing part called MapReduce that processes data in parallel on a large cluster. Hadoop also includes additional projects like Hive, Pig, HBase, Zookeeper, Oozie, and Sqoop that together form a powerful data processing ecosystem.
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.
This document provides an introduction and overview of Hive, including:
- Hive is a data warehouse infrastructure tool that processes structured data stored in Hadoop using SQL-like queries. It allows for easy querying and analysis of big data.
- Hive was initially developed by Facebook and is now an Apache open source project. It stores schemas in a database and processes data stored in HDFS.
- Key features of Hive include using SQL-like queries (HiveQL), storing data in HDFS for scalability, and providing a familiar interface for analyzing large datasets.
M. Florence Dayana - Hadoop Foundation for Analytics.pptxDr.Florence Dayana
Hadoop Foundation for Analytics
History of Hadoop
Features of Hadoop
Key Advantages of Hadoop
Why Hadoop
Versions of Hadoop
Eco Projects
Essential of Hadoop ecosystem
RDBMS versus Hadoop
Key Aspects of Hadoop
Components of Hadoop
The document provides information on various components of the Hadoop ecosystem including Pig, Zookeeper, HBase, Spark, and Hive. It discusses how HBase offers random access to data stored in HDFS, allowing for faster lookups than HDFS alone. It describes the architecture of HBase including its use of Zookeeper, storage of data in regions on region servers, and secondary indexing capabilities. Finally, it summarizes Hive and how it allows SQL-like queries on large datasets stored in HDFS or other distributed storage systems using MapReduce or Spark jobs.
Hadoop is an open-source framework for distributed storage and processing of large datasets across clusters of computers. It was developed based on Google papers describing Google File System (GFS) for reliable distributed data storage and MapReduce for distributed parallel processing. Hadoop uses HDFS for storage and MapReduce for processing in a scalable, fault-tolerant manner on commodity hardware. It has a growing ecosystem of projects like Pig, Hive, HBase, Zookeeper, Spark and others that provide additional capabilities for SQL queries, real-time processing, coordination services and more. Major vendors that provide Hadoop distributions include Hortonworks and Cloudera.
13. Business intelligence (BI) is a technology-driven process for analyzing data and presenting actionable
information to help corporate executives, business managers and other end users make more informed
business decisions.
16. Big Data Is Big Market & Big Business - $50
Billion Market by 2017
Big Data not only refers to the data itself but also a set of
technologies that capture, store, manage and analyze large
and variable collections of data to solve complex problems.
36. Hadoop
Apache Hadoop®
is an open source framework for distributed storage and processing of large
sets of data on commodity hardware. Hadoop enables businesses to quickly gain insight from
massive amounts of structured and unstructured data.
39. Hadoop is not a database:
Hadoop an efficient distributed file
system and not a database. It is designed
specifically for information that comes in
many forms, such as social media, server
log files or other documents. Anything
that can be stored as a file can be placed
in a Hadoop repository.
49. ● The MapReduce is the processing part of Hadoop and the MapReduce server on a
typical machine is called a TaskTracker.
● HDFS is the data part of Hadoop and the HDFS server on a a typical machine is called a
DataNode.
73. Hive – A Data Warehouse
on top of Hadoop
Most of us might have already heard of the
history of Hadoop and how Hadoop is being
used in more and more organizations today
for batch processing of large sets of data.
There are many components in the Hadoop
eco-system, each serving a definite purpose.
For e.g. HDFS is the storage layer and a File
System, Map-Reduce is a programming
model and an execution framework. Now
talking about Hive, it is another component in
Hadoop stack; which was built by Facebook
and contributed back to the community.
86. Core Hadoop:
HDFS:
HDFS stands for Hadoop Distributed File System for managing big data sets with High Volume, Velocity and Variety. HDFS
implements master slave architecture. Master is Name node and slave is data node.
Features:
• Scalable
• Reliable
• Commodity Hardware
HDFS is the well known for Big Data storage.
Map Reduce:
Map Reduce is a programming model designed to process high volume distributed data. Platform is built using Java for better
exception handling. Map Reduce includes two deamons, Job tracker and Task Tracker.
Features:
• Functional Programming.
• Works very well on Big Data.
• Can process large datasets.
Map Reduce is the main component known for processing big data.
YARN:
YARN stands for Yet Another Resource Negotiator. It is also called as MapReduce 2(MRv2). The two major functionalities of Job
Tracker in MRv1, resource management and job scheduling/ monitoring are split into separate daemons which are
ResourceManager, NodeManager and ApplicationMaster.
Features:
• Better resource management.
• Scalability
• Dynamic allocation of cluster resources.
88. Data Access:
Pig:
Apache Pig is a high level language built on top of MapReduce for analyzing large datasets with simple adhoc data analysis
programs. Pig is also known as Data Flow language. It is very well integrated with python. It is initially developed by yahoo.
Salient features of pig:
• Ease of programming
• Optimization opportunities
• Extensibility.
Pig scripts internally will be converted to map reduce programs.
Hive:
Apache Hive is another high level query language and data warehouse infrastructure built on top of Hadoop for providing data
summarization, query and analysis. It is initially developed by yahoo and made open source.
Salient features of hive:
• SQL like query language called HQL.
• Partitioning and bucketing for faster data processing.
• Integration with visualization tools like Tableau.
Hive queries internally will be converted to map reduce programs.
If you want to become a big data analyst, these two high level languages are a must know!!
90. Data Storage:
Hbase:
Apache HBase is a NoSQL database built for hosting large tables with billions of rows and millions of columns on top of Hadoop
commodity hardware machines. Use Apache Hbase when you need random, realtime read/write access to your Big Data.
Features:
• Strictly consistent reads and writes. In memory operations.
• Easy to use Java API for client access.
• Well integrated with pig, hive and sqoop.
• Is a consistent and partition tolerant system in CAP theorem.
Cassandra:
Cassandra is a NoSQL database designed for linear scalability and high availability. Cassandra is based on key-value model.
Developed by Facebook and known for faster response to queries.
Features:
• Column indexes
• Support for de-normalization
• Materialized views
• Powerful built-in caching.
92. Interaction -Visualization- execution-development:
Hcatalog:
HCatalog is a table management layer which provides integration of hive metadata for other Hadoop applications. It enables
users with different data processing tools like Apache pig, Apache MapReduce and Apache Hive to more easily read and write
data.
Features:
• Tabular view for different formats.
• Notifications of data availability.
• REST API’s for external systems to access metadata.
Lucene:
Apache LuceneTM is a high-performance, full-featured text search engine library written entirely in Java. It is a technology
suitable for nearly any application that requires full-text search, especially cross-platform.
Features:
• Scalable, High – Performance indexing.
• Powerful, Accurate and Efficient search algorithms.
• Cross-platform solution.
Hama:
Apache Hama is a distributed framework based on Bulk Synchronous Parallel(BSP) computing. Capable and well known for
massive scientific computations like matrix, graph and network algorithms.
Features:
• Simple programming model
• Well suited for iterative algorithms
• YARN supported
• Collaborative filtering unsupervised machine learning.
• K-Means clustering.
93. Crunch:
Apache crunch is built for pipelining MapReduce programs which are simple and efficient. This framework is used for writing,
testing and running MapReduce pipelines.
Features:
• Developer focused.
• Minimal abstractions
• Flexible data model.
95. Data Intelligence:
Drill:
Apache Drill is a low latency SQL query engine for Hadoop and NoSQL.
Features:
• Agility
• Flexibility
• Familiarilty.
Mahout:
Apache Mahout is a scalable machine learning library designed for building predictive analytics on Big Data. Mahout now has
implementations apache spark for faster in memory computing.
Features:
• Collaborative filtering.
• Classification
• Clustering
• Dimensionality reduction
97. Data Serialization:
Avro:
Apache Avro is a data serialization framework which is language neutral. Designed for language portability, allowing data to
potentially outlive the language to read and write it.
Thrift:
Thrift is a language developed to build interfaces to interact with technologies built on Hadoop. It is used to define and create
services for numerous languages.
99. Data Integration:
Apache Sqoop:
Apache Sqoop is a tool designed for bulk data transfers between relational databases and Hadoop.
Features:
• Import and export to and from HDFS.
• Import and export to and from Hive.
• Import and export to HBase.
Apache Flume:
Flume is a distributed, reliable, and available service for efficiently collecting, aggregating, and moving large amounts of log data.
Features:
• Robust
• Fault tolerant
• Simple and flexible Architecture based on streaming data flows.
Apache Chukwa:
Scalable log collector used for monitoring large distributed files systems.
Features:
• Scales to thousands of nodes.
• Reliable delivery.
• Should be able to store data indefinitely.
101. Management, Monitoring and Orchestration:
Apache Ambari:
Ambari is designed to make hadoop management simpler by providing an interface for provisioning, managing and monitoring
Apache Hadoop Clusters.
Features:
• Provision a Hadoop Cluster.
• Manage a Hadoop Cluster.
• Monitor a Hadoop Cluster.
Apache Zookeeper:
Zookeeper is a centralized service designed for maintaining configuration information, naming, providing distributed
synchronization, and providing group services.
Features:
• Serialization
• Atomicity
• Reliability
• Simple API
Apache Oozie:
Oozie is a workflow scheduler system to manage Apache Hadoop jobs.
Features:
• Scalable, reliable and extensible system.
• Supports several types of Hadoop jobs such as Map-Reduce, Hive, Pig and Sqoop.
• Simple and easy to use.