Introduction to Big Data and hadoop
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Technological geeks Hindi Video 1 - https://youtu.be/LSvAoo4pYjs Contents :- What is Big Data ? Big Data characteristics Big Data sources Use cases of Big Data Hadoop Daemons Hadoop Master slave architecture Hadoop cluster Secondary namenode
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Technological Geeks Video 13 :-
Video Link :- https://youtu.be/mfLxxD4vjV0
FB page Link :- https://www.facebook.com/bitwsandeep/
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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.
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Apache Hive Tutorial
Technological Geeks Video 13 :-
Video Link :- https://youtu.be/mfLxxD4vjV0
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Contents :-
Hive Architecture
Hive Components
Limitations of Hive
Hive data model
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HDFS has several strengths: horizontally scale its IO bandwidth and scale its storage to petabytes of storage. Further, it provides very low latency metadata operations and scales to over 60K concurrent clients. Hadoop 3.0 recently added Erasure Coding. One of HDFS’s limitations is scaling a number of files and blocks in the system. We describe a radical change to Hadoop’s storage infrastructure with the upcoming Ozone technology. It allows Hadoop to scale to tens of billions of files and blocks and, in the future, to every larger number of smaller objects. Ozone fundamentally separates the namespace layer and the block layer allowing new namespace layers to be added in the future. Further, the use of RAFT protocol has allowed the storage layer to be self-consistent. We show how this technology helps a Hadoop user and also what it means for evolving HDFS in the future. We will also cover the technical details of Ozone.
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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.
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This talk will tell the story of how we doubled our Hadoop infrastructure twice in the past two years.
• We will outline our main use cases and historical rates of cluster growth in multiple dimensions.
• We will focus on optimizations, configuration improvements, performance monitoring and architectural decisions we undertook to allow the infrastructure to keep pace with business needs.
• The topics include improvements in HDFS NameNode performance, and fine tuning of block report processing, the block balancer, and the namespace checkpointer.
• We will reveal a study on the optimal storage device for HDFS persistent journals (SATA vs. SAS vs. SSD vs. RAID).
• We will also describe Satellite Cluster project which allowed us to double the objects stored on one logical cluster by splitting an HDFS cluster into two partitions without the use of federation and practically no code changes.
• Finally, we will take a peek at our future goals, requirements, and growth perspectives.
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What are the course objectives?
This course will enable you to:
1. Understand the different components of the Hadoop ecosystem such as Hadoop 2.7, Yarn, MapReduce, Pig, Hive, Impala, HBase, Sqoop, Flume, and Apache Spark
2. Understand Hadoop Distributed File System (HDFS) and YARN as well as their architecture, and learn how to work with them for storage and resource management
3. Understand MapReduce and its characteristics, and assimilate some advanced MapReduce concepts
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5. Create database and tables in Hive and Impala, understand HBase, and use Hive and Impala for partitioning
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8. Understand HBase, its architecture, data storage, and working with HBase. You will also understand the difference between HBase and RDBMS
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10. Do functional programming in Spark
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I will deep dive into some optimizations which promise to provide major performance gains. Support for ACID tables has also improved considerably. Although some of these features and enhancements are not novel but have existed for years in other DB systems, implementing them on Hive poses some unique challenges and results in lessons which are generally applicable in many other contexts. I will also provide a glimpse of what is expected to come in near future.
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LinkedIn leverages the Apache Hadoop ecosystem for its big data analytics. Steady growth of the member base at LinkedIn along with their social activities results in exponential growth of the analytics infrastructure. Innovations in analytics tooling lead to heavier workloads on the clusters, which generate more data, which in turn encourage innovations in tooling and more workloads. Thus, the infrastructure remains under constant growth pressure. Heterogeneous environments embodied via a variety of hardware and diverse workloads make the task even more challenging.
This talk will tell the story of how we doubled our Hadoop infrastructure twice in the past two years.
• We will outline our main use cases and historical rates of cluster growth in multiple dimensions.
• We will focus on optimizations, configuration improvements, performance monitoring and architectural decisions we undertook to allow the infrastructure to keep pace with business needs.
• The topics include improvements in HDFS NameNode performance, and fine tuning of block report processing, the block balancer, and the namespace checkpointer.
• We will reveal a study on the optimal storage device for HDFS persistent journals (SATA vs. SAS vs. SSD vs. RAID).
• We will also describe Satellite Cluster project which allowed us to double the objects stored on one logical cluster by splitting an HDFS cluster into two partitions without the use of federation and practically no code changes.
• Finally, we will take a peek at our future goals, requirements, and growth perspectives.
SPEAKERS
Konstantin Shvachko, Sr Staff Software Engineer, LinkedIn
Erik Krogen, Senior Software Engineer, LinkedIn
PPT on Hadoop
The document discusses Hadoop, an open-source software framework that allows distributed processing of large datasets across clusters of computers. It describes Hadoop as having two main components - the Hadoop Distributed File System (HDFS) which stores data across infrastructure, and MapReduce which processes the data in a parallel, distributed manner. HDFS provides redundancy, scalability, and fault tolerance. Together these components provide a solution for businesses to efficiently analyze the large, unstructured "Big Data" they collect.
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The document discusses big data and distributed computing. It explains that big data refers to large, unstructured datasets that are too large for traditional databases. Distributed computing uses multiple computers connected via a network to process large datasets in parallel. Hadoop is an open-source framework for distributed computing that uses MapReduce and HDFS for parallel processing and storage across clusters. HDFS stores data redundantly across nodes for fault tolerance.
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This document discusses YARN high availability (HA) features. It describes the YARN architecture and how the ResourceManager is a single point of failure. It then covers how YARN HA implements an active-standby ResourceManager pair with shared state storage to enable failover. The document provides details on state persistence, automatic election of the active ResourceManager, fencing to prevent split-brain scenarios, and client-side failover transparency.
Hadoop Tutorial For Beginners
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/
Hadoop and Big Data
This presentation provides an overview of Hadoop, including:
- A brief history of data and the rise of big data from various sources.
- An introduction to Hadoop as an open source framework used for distributed processing and storage of large datasets across clusters of computers.
- Descriptions of the key components of Hadoop - HDFS for storage, and MapReduce for processing - and how they work together in the Hadoop architecture.
- An explanation of how Hadoop can be installed and configured in standalone, pseudo-distributed and fully distributed modes.
- Examples of major companies that use Hadoop like Amazon, Facebook, Google and Yahoo to handle their large-scale data and analytics needs.
Big Data Tutorial For Beginners | What Is Big Data | Big Data Tutorial | Hado...
This Edureka Big Data tutorial helps you to understand Big Data in detail. This tutorial will be discussing about evolution of Big Data, factors associated with Big Data, different opportunities in Big Data. Further it will discuss about problems associated with Big Data and how Hadoop emerged as a solution. Below are the topics covered in this tutorial:
1) Evolution of Data
2) What is Big Data?
3) Big Data as an Opportunity
4) Problems in Encasing Big Data Opportunity
5) Hadoop as a Solution
6) Hadoop Ecosystem
7) Edureka Big Data & Hadoop Training
Big Data Analytics with Hadoop
Hadoop, flexible and available architecture for large scale computation and data processing on a network of commodity hardware.
HBase Tutorial For Beginners | HBase Architecture | HBase Tutorial | Hadoop T...
This presentation about HBase will help you understand what is HBase, what are the applications of HBase, how is HBase is different from RDBMS, what is HBase Storage, what are the architectural components of HBase and at the end, we will also look at some of the HBase commands using a demo. HBase is an essential part of the Hadoop ecosystem. It is a column-oriented database management system derived from Google’s NoSQL database Bigtable that runs on top of HDFS. After watching this video, you will know how to store and process large datasets using HBase. Now, let us get started and understand HBase and what it is used for.
Below topics are explained in this HBase presentation:
1. What is HBase?
2. HBase Use Case
3. Applications of HBase
4. HBase vs RDBMS
5. HBase Storage
6. HBase Architectural Components
What is this Big Data Hadoop training course about?
Simplilearn’s Big Data Hadoop training course lets you master the concepts of the Hadoop framework and prepares you for Cloudera’s CCA175 Big data certification. The Big Data Hadoop and Spark developer course have been designed to impart in-depth knowledge of Big Data processing using Hadoop and Spark. The course is packed with real-life projects and case studies to be executed in the CloudLab.
What are the course objectives?
This course will enable you to:
1. Understand the different components of the Hadoop ecosystem such as Hadoop 2.7, Yarn, MapReduce, Pig, Hive, Impala, HBase, Sqoop, Flume, and Apache Spark
2. Understand Hadoop Distributed File System (HDFS) and YARN as well as their architecture, and learn how to work with them for storage and resource management
3. Understand MapReduce and its characteristics, and assimilate some advanced MapReduce concepts
4. Get an overview of Sqoop and Flume and describe how to ingest data using them
5. Create database and tables in Hive and Impala, understand HBase, and use Hive and Impala for partitioning
6. Understand different types of file formats, Avro Schema, using Arvo with Hive, and Sqoop and Schema evolution
7. Understand Flume, Flume architecture, sources, flume sinks, channels, and flume configurations
8. Understand HBase, its architecture, data storage, and working with HBase. You will also understand the difference between HBase and RDBMS
9. Gain a working knowledge of Pig and its components
10. Do functional programming in Spark
11. Understand resilient distribution datasets (RDD) in detail
12. Implement and build Spark applications
13. Gain an in-depth understanding of parallel processing in Spark and Spark RDD optimization techniques
14. Understand the common use-cases of Spark and the various interactive algorithms
15. Learn Spark SQL, creating, transforming, and querying Data frames
Learn more at https://www.simplilearn.com/big-data-and-analytics/big-data-and-hadoop-training
What's new in apache hive
Apache Hive is a rapidly evolving project which continues to enjoy great adoption in big data ecosystem. Although, Hive started primarily as batch ingestion and reporting tool, community is hard at work in improving it along many different dimensions and use cases. This talk will provide an overview of latest and greatest features and optimizations which have landed in project over last year. Materialized view, micro managed tables and workload management are some noteworthy features.
I will deep dive into some optimizations which promise to provide major performance gains. Support for ACID tables has also improved considerably. Although some of these features and enhancements are not novel but have existed for years in other DB systems, implementing them on Hive poses some unique challenges and results in lessons which are generally applicable in many other contexts. I will also provide a glimpse of what is expected to come in near future.
Speaker: Ashutosh Chauhan, Engineering Manager, Hortonworks
Local Secondary Indexes in Apache Phoenix
The document discusses local secondary indexes in Apache Phoenix. Local indexes are stored in the same region as the base table data, providing faster index building and reads compared to global indexes. The write process involves preparing index updates along with data updates and writing them atomically to memstores and the write ahead log. Reads scan the local index and retrieve any missing columns from the base table. Local indexes improve write performance over global indexes due to reduced network utilization. The document provides performance results and tips on using local indexes.
Hadoop Architecture
This presentation discusses the following topics:
Introduction
Components of Hadoop
MapReduce
Map Task
Reduce Task
Anatomy of a Map Reduce
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- 1. Hadoop Development Series By Sandeep Patil 11/1/2017 1Footer Text
- 2. Introduction to Big Data and Hadoop 11/1/2017Footer Text 2
- 3. What is Big Data?? • Large amount of Data . • Its a popular term used to express exponential growth of data . • Big data is difficult to store , collect , maintain , Analyze and Visualize . 11/1/2017Footer Text 3
- 4. Big Data characteristics • Volume :- Large amount of data . • Velocity :- The rate at which data is getting generated • Variety :- Different types of Data - Structured data ,eg MySql - Semi-Structured data, eg xml , json - Unstructured data, eg text , audio, video 11/1/2017Footer Text 4
- 5. Big Data sources • Social Media • Banks • Instruments • Websites • Stock Market 11/1/2017Footer Text 5
- 6. Use cases of Big Data • Recommendation engines • Analyzing Call Detail Record(CDR) • Fraud Detection • Market Basket Analysis • Sentimental Analysis 11/1/2017Footer Text 6
- 7. Hadoop Introduction • Open source framework that allows distributed processing of large datasets on the cluster of commodity hardware • Hadoop is a data management tool and uses scale out storage . 11/1/2017Footer Text 7
- 8. Defining Hadoop Cluster • Size of data is most important factor while defining hadoop cluster 11/1/2017Footer Text 8 5 Servers with 10 TB storage capacity each Total Storage Capacity : - 50TB
- 9. Defining Hadoop Cluster 11/1/2017Footer Text 9 7 Servers with 10 TB storage capacity each Total storage capacity : 70TB
- 10. Hadoop Components • Hadoop 1 Componets - HDFS (Hadoop distributed file system) - MapReduce • Hadoop 2 Component - HDFS (Hadoop distributed file system) - YARN/MRv2 11/1/2017Footer Text 10 HDFS MR/ YARN Storage/ Reads-Writes Processing
- 11. Hadoop Daemons • Hadoop 1 Daemos Namenode Datanode Secondary Namenode job Tracker Task Tracker 11/1/2017Footer Text 11 HDFS MapReduce NameNode DataNode Job Tracker Task Tracker
- 12. Hadoop Daemons • Hadoop 2 Daemos Namenode Datanode Secondary Namenode Resource Manager Node Manager 11/1/2017Footer Text 12 HDFS YARN NameNode DataNode Resource Manager Node Manager
- 13. Hadoop Master Slave Architecture 11/1/2017Footer Text 13 HDFS MR/YARN NameNode DataNode ResourceManager NodeManager Master Slave Master Slave
- 14. Hadoop Cluster • Assume that we have hadoop cluster with 4 nodes 11/1/2017Footer Text 14 Master NameNode ResourceManager Slave DataNode NodeManager
- 15. Secondary Name Node • Secondary Namenode is not a hot backup for Namenode . • It just takes hourly backup of Namenode metadata • It is can be used to Restart a crashed Hadoop Cluster • Secondary Namenode is an important demon for Hadoop1 , However in hadoop2 It is not that much Important . 11/1/2017Footer Text 15
- 16. Modes of Operation • Stand Alone • Pseudo Distributed • Fully Distributed 11/1/2017Footer Text 16
- 17. Next Video • Comparison between Hadoop1 and Hadoop2 11/1/2017Footer Text 17
- 18. Like and Subscribe 11/1/2017Footer Text 18 sdp117@gmail.com