Review Big Data Analytics Apache Hadoop & Spark Machine Learning Pune Mumbai In pune, maharashtra, india | School In Pune
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Big Data Analytics Apache Hadoop & Spark Machine Learning Pune Mumbai
Amanora Chamber, Amanora Market City
Pune,Pune
Maharashtra,India - 411028
9766733010
Detailed description is Online and Classroom Training in Business Analysis Certification CBAP CCBA IIBA EEP, Big Data Analytics & Apache Hadoop, Agile, Project Management, PMP Spark A to Z:.
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Duration : 6-days (3 Hours each).
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Background: Spark is fast emerging as an alternative to Hadoop & Map/Reduce due to its speed.
Spark Programming is often necessary to address complex processing loads, involving huge data volumes, which can't processed by Hadoop in a timely manner.
Its in-memory computing engine makes Spark the choice of platform for real-time analytics, which requires high speed data ingestion and processing within seconds.
A whole new generation of analytics applications is now emerging to process geo-location data, streaming web events, sensors data, as well as data received from mobile and wearable devices.
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Training Goals : To provide a thorough understanding of concepts of in-memory distributed computing and Spark API, so as to enable participants in development of Spark programs of moderate complexity.
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Audience : Hadoop developers, ETL developers, Java developers, BI Professionals..
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Methodology: The program is designed to provide an overall conceptual framework and common design patterns.
Key concepts in each area will be explained and working code provided.
Participants will be able to run the examples and expected to understand code.
While explanation of key concepts is provided, a detailed code walk-though is usually not feasible in the interest of time.
Code is written in Java and Scala.
Therefore prior knowledge of these languages will be helpful to understand the code-level implementation of key concepts..
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Highlights of the Course:.
Complete coverage of Spark Programming fundamentals.
Each participant creates a fully functional multi-node Spark cluster.
End-To-End real-time analytics with Spark.
Integration with Hadoop, Hive, HBase, Cassandra and many more..
Participants build a complete project during the course.
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Cassandra - Architecture & Programming.
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Duration : 6-days (3 hours each).
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Background: Cassandra is emerging as one of the most powerful Big Data stores.
it is often seen as a replacement for RDBMS but nothing can be farther than truth.
Cassandra is a No-SQL database designed to store Terabytes of data in memory and provide less-than-50-milliseconds response to SQL like queries.
But this is possible, only if, we know where to use Cassandra and how to deploy it.
Cassandra is also an essential accompaniment for many real-time analytics applications.
Cassandra can be used as part of Java or .NET applications using a variety of readily available Drivers..
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Pre-requisite : SQL knowledge & some object oriented programming background, preferably Java..
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Training Goals : To provide a thorough understanding of Cassandra Architecture, Data Model and Programming concepts.
Participants should be able to build Cassandra applications at end of the course..
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Audience : SQL developers, ETL developers, Java developers and Hadoop professionals..
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Highlights:.
Complete discussion of potential Cassandra use cases..
Differences between SQL and No-SQL databases.
Creating a full-fledged multi-node Cassandra Cluster.
High-speed Data ingestion with Cassandra.
Cassandra Design Patterns.
Methodology: The program is designed to provide an overview of Cassandra.
Key concepts in each area will be explained and working code provided.
Participants will be able to run the examples and expected to understand code on their own with some pointers.
Detailed code walk-though is not provided.
Code is written in Java.
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Data Ingestion With Flume & Kafka.
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Duration : 4-days (3 hours each).
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Background: Data ingestion is an art and science in itself.
Ingesting data effectively into a Hadoop cluster or any other data store, requires a good understanding of the source and sink with an ability to configure data pipelines.
Ingestion becomes a complex task, as we source events from multiple sources in parallel and need to deliver them to various destinations in real-time.
High-speed data ingestion is especially critical when implementing real-time analytics..
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Pre-requisite : Some programming background, preferably Java..
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Training Goals : To provide a thorough understanding of Flume configuration & Kafka.
Participants will be able to implement practical data flows in their projects.
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Audience : ETL developers, Java developers, Analytics professionals and Hadoop developers..
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Contents:.
Introduction to multiplexed data flows, fan-out flows, aggregators..
Implementing Custom De-Serialisers and Interceptors..
Advanced Flume Configuration..
Kafka Architecture - Publish/Subscribe Model.
Implementing custom Publishers.
Kafka Consumers - HDFS consumer, HBase consumer, Cassandra Consumer and many others..
Methodology: The program is designed to provide working knowledge of Flume & Kafka.
It involves implementing various data flows on a multi-node cluster.
Key concepts in each area will be explained and working code provided.
Participants will be able to run the examples and expected to understand code on their own with some pointers.
Detailed code walk-though is not provided.
Code is written in Java.
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Hadoop Survival Guide.
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Duration : 10-days (3 hours each).
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Background: Hadoop is everywhere - from social media mining to real-time geo-location tracking.
Its no wonder that it is becoming an essential skill for all IT professionals.
However, learning Hadoop on your own is a daunting task - not only is the Hadoop Ecosystem quite vast, its also fairly complex.
This program is designed to abstract that complexity and make Hadoop learning a rewarding experience.
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Pre-requisite : Some programming background, preferably Java..
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Training Goals : To provide a thorough understanding of essential Hadoop eco-system components.
Participants will acquire all essentials to get started on a real project in the shortest possible time.
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Audience : ETL developers, Java developers, Analytics professionals and Hadoop developers..
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Contents:.
Introduction to Big Data - 3V Paradigm.
Hadoop Architecture.
Setting up a Hadoop Cluster.
Distributed Computing essentials with Map/Reduce.
Hadoop Eco-system - Pig, Hive, HBase, Zookeeper.
Data Ingestion tools - Sqoop and Flume.
Hadoop 2.0 - YARN, HDFS2 and MRv2.
Methodology: The program is designed to provide working knowledge of Hadoop.
It involves implementing various data flows on a multi-node cluster.
Key concepts in each area will be explained and working code provided.
Participants will be able to run the examples and expected to understand code on their own with some pointers.
Real-world practical projects is offered as an assignment at the end of the session.
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