#define a bucketed hive table

[pleaseburger]

Are you like, fr about recommending trrpg systems? Cause BOY I'm in the market for one. I've only tried Dnd (too rules, too expensive, too genre locked) and Fate which I'm still feeling out but I'm getting the feeling it's too freeform for the players I'm DMing for. I'm having a hard time wrapping my head around it, I guess.

Anyway, if none of that made a system spring to mind, just tell me about your favorite/go-to system and why it's that one!

of course I was!! this is my purpose as a micro influencer!!!

you have basically asked me the equivalent of "I have tried pizza, which is popular but I did not like, and tofu, which is too open to all the possibilities in the world (fate is good and cool, like tofu). what should I try eating next". so hopefully you see why this is hard to answer

I have been playing Starforged as my main game for the last year or so. it's the sci-fi fork of Ironsworn (which is free btw :3). ironsworn is lovely cause you can play it as a GM with a table full of players, or co-op with other players and NO GM, or even just by yourself!! the rules are split up into mini rules packages called "moves", which trigger whenever you do something in the game and then have their own varying outcomes that can suddenly change the game in ways no one expected.

characters in ironsworn are defined by Assets instead of a class or a big list of skills; each asset is like a superjuiced D&D feat, empowering you to do really cool stuff or even *giving you completely new moves that are unique to your character*. whenever you're feeling stuck, the Oracle tables are there to jump-start your imagination. my playgroup loves asking the Oracle for her opinion on stuff all the time. you can run a game of Ironsworn/Starforged as a GM with absolutely zero prepwork and be okay because the moves and oracles are there to support play.

my absolute favourite RPG is HEART: The City Beneath. it's gorgeous, haunting, evocative, and tragic. the core of the game's engine, the Resistance system, is a tension builder and drama generating engine, because as the players Do Stuff, they build up stress to their resistances, and then after building up too much stress, the dam breaks and it all comes crumbling down in a juicy dramatic consequence, which the book has like a hundred options for you to choose from if you cant think of anything interesting right away.

your character's background comes with a bucket list of things you could do (like eating something you really shouldnt, or getting a landmark named after you), called Beats, and then you pick 2 of those Beats every session. if you do either of them, you get a new ability from your class! it's very cool because it inherently tells the players what kind of cool stuff they can do, encourages them to do it by rewarding them for taking action, and it makes prepwork *very* easy for the GM because the players have to tell you exactly what they want to do the next session.

the classes in HEART are also some of my favourite archetypes out of any game I've read. the deep apiarist, who has hollowed out their body to be a host for a hive of psychic bees, can do things like get through tiny gaps by letting their hive chew up their body and rebuild them on the other side. the vermissian knights are basically an order of cursed paladins that are obsessed with creating a failed train network that was planned hundreds of years ago but never came to be. there's a gun wizard class. and all of them have a dozen abilities that are all enticing; no features that boil down to being a +2 bonus to attack rolls, you want to collect as many of these things as you can.

but the best part of game is the setting itself, the Heart. it's a red wet heaven under the surface of the earth, a rip in reality where a strange otherspace has crawled in. whenever someone enters it, it tries to build itself in the image of their desires, but it does this slowly and badly. everyone has a different theory on what it is. what this means for your game is that even though the heart has a very strong identity and hits a specific feel, it shapes to the style of GM you are and the desires of your players as the game unfolds. every table's Heart is just a little bit different.

[roamnook]

"Apache Spark: The Leading Big Data Platform with Fast, Flexible, Developer-Friendly Features Used by Major Tech Giants and Government Agencies Worldwide."

What is Apache Spark? The Big Data Platform that Crushed Hadoop

Apache Spark is a powerful data processing framework designed for large-scale SQL, batch processing, stream processing, and machine learning tasks. With its fast, flexible, and developer-friendly nature, Spark has become the leading platform in the world of big data. In this article, we will explore the key features and real-world applications of Apache Spark, as well as its significance in the digital age.

Apache Spark defined

Apache Spark is a data processing framework that can quickly perform processing tasks on very large data sets. It can distribute data processing tasks across multiple computers, either on its own or in conjunction with other distributed computing tools. This capability is crucial in the realm of big data and machine learning, where massive computing power is necessary to analyze and process vast amounts of data. Spark eases the programming burden of these tasks by offering an easy-to-use API that abstracts away much of the complexities of distributed computing and big data processing.

What is Spark in big data

In the context of big data, the term "big data" refers to the rapid growth of various types of data - structured data in database tables, unstructured data in business documents and emails, semi-structured data in system log files and web pages, and more. Unlike traditional analytics, which focused solely on structured data within data warehouses, modern analytics encompasses insights derived from diverse data sources and revolves around the concept of a data lake. Apache Spark was specifically designed to address the challenges posed by this new paradigm.

Originally developed at U.C. Berkeley in 2009, Apache Spark has become a prominent distributed processing framework for big data. Flexibility lies at the core of Spark's appeal, as it can be deployed in various ways and supports multiple programming languages such as Java, Scala, Python, and R. Furthermore, Spark provides extensive support for SQL, streaming data, machine learning, and graph processing. Its widespread adoption by major companies and organizations, including Apple, IBM, and Microsoft, highlights its significance in the big data landscape.

Spark RDD

Resilient Distributed Dataset (RDD) forms the foundation of Apache Spark. An RDD is an immutable collection of objects that can be split across a computing cluster. Spark performs operations on RDDs in a parallel batch process, enabling fast and scalable parallel processing. The RDD concept allows Spark to transform user's data processing commands into a Directed Acyclic Graph (DAG), which serves as the scheduling layer determining the tasks, nodes, and sequence of execution.

Apache Spark can create RDDs from various data sources, including text files, SQL databases, NoSQL stores like Cassandra and MongoDB, Amazon S3 buckets, and more. Moreover, Spark's core API provides built-in support for joining data sets, filtering, sampling, and aggregation, offering developers powerful data manipulation capabilities.

Spark SQL

Spark SQL has emerged as a vital component of the Apache Spark project, providing a high-level API for processing structured data. Spark SQL adopts a dataframe approach inspired by R and Python's Pandas library, making it accessible to both developers and analysts. Alongside standard SQL support, Spark SQL offers a wide range of data access methods, including JSON, HDFS, Apache Hive, JDBC, Apache ORC, and Apache Parquet. Additional data stores, such as Apache Cassandra and MongoDB, can be integrated using separate connectors from the Spark Packages ecosystem.

Spark SQL utilizes Catalyst, Spark's query optimizer, to optimize data locality and computation. Since Spark 2.x, Spark SQL's dataframe and dataset interfaces have become the recommended approach for development, promoting a more efficient and type-safe method for data processing. While the RDD interface remains available, it is typically used when lower-level control or specialized performance optimizations are required.

Spark MLlib and MLflow

Apache Spark includes libraries for machine learning and graph analysis at scale. MLlib offers a framework for building machine learning pipelines, facilitating the implementation of feature extraction, selection, and transformations on structured datasets. The library also features distributed implementations of clustering and classification algorithms, such as k-means clustering and random forests.

MLflow, although not an official part of Apache Spark, is an open-source platform for managing the machine learning lifecycle. The integration of MLflow with Apache Spark enables features such as experiment tracking, model registries, packaging, and user-defined functions (UDFs) for easy inference at scale.

Structured Streaming

Structured Streaming provides a high-level API for creating infinite streaming dataframes and datasets within Apache Spark. It supersedes the legacy Spark Streaming component, addressing pain points encountered by developers in event-time aggregations and late message delivery. With Structured Streaming, all queries go through Spark's Catalyst query optimizer and can be run interactively, allowing users to perform SQL queries against live streaming data. The API also supports watermarking, windowing techniques, and the ability to treat streams as tables and vice versa.

Delta Lake

Delta Lake is a separate project from Apache Spark but has become essential in the Spark ecosystem. Delta Lake augments data lakes with features such as ACID transactions, unified querying semantics for batch and stream processing, schema enforcement, full data audit history, and scalability for exabytes of data. Its adoption has contributed to the rise of the Lakehouse Architecture, eliminating the need for a separate data warehouse for business intelligence purposes.

Pandas API on Spark

The Pandas library is widely used for data manipulation and analysis in Python. Apache Spark 3.2 introduced a new API that allows a significant portion of the Pandas API to be used transparently with Spark. This compatibility enables data scientists to leverage Spark's distributed execution capabilities while benefiting from the familiar Pandas interface. Approximately 80% of the Pandas API is currently covered, with ongoing efforts to increase coverage in future releases.

Running Apache Spark

An Apache Spark application consists of two main components: a driver and executors. The driver converts the user's code into tasks that can be distributed across worker nodes, while the executors run these tasks on the worker nodes. A cluster manager mediates communication between the driver and executors. Apache Spark can run in a stand-alone cluster mode, but is more commonly used with resource or cluster management systems such as Hadoop YARN or Kubernetes. Managed solutions for Apache Spark are also available on major cloud providers, including Amazon EMR, Azure HDInsight, and Google Cloud Dataproc.

Databricks Lakehouse Platform

Databricks, the company behind Apache Spark, offers a managed cloud service that provides Apache Spark clusters, streaming support, integrated notebook development, and optimized I/O performance. The Databricks Lakehouse Platform, available on multiple cloud providers, has become the de facto way many users interact with Apache Spark.

Apache Spark Tutorials

If you're interested in learning Apache Spark, we recommend starting with the Databricks learning portal, which offers a comprehensive introduction to Apache Spark (with a slight bias towards the Databricks Platform). For a more in-depth exploration of Apache Spark's features, the Spark Workshop is a great resource. Additionally, books such as "Spark: The Definitive Guide" and "High-Performance Spark" provide detailed insights into Apache Spark's capabilities and best practices for data processing at scale.

Conclusion

Apache Spark has revolutionized the way large-scale data processing and analytics are performed. With its fast and developer-friendly nature, Spark has surpassed its predecessor, Hadoop, and become the leading big data platform. Its extensive features, including Spark SQL, MLlib, Structured Streaming, and Delta Lake, make it a powerful tool for processing complex data sets and building machine learning models. Whether deployed in a stand-alone cluster or as part of a managed cloud service like Databricks, Apache Spark offers unparalleled scalability and performance. As companies increasingly rely on big data for decision-making, mastering Apache Spark is essential for businesses seeking to leverage their data assets effectively.

Sponsored by RoamNook

This article was brought to you by RoamNook, an innovative technology company specializing in IT consultation, custom software development, and digital marketing. RoamNook's main goal is to fuel digital growth by providing cutting-edge solutions for businesses. Whether you need assistance with data processing, machine learning, or building scalable applications, RoamNook has the expertise to drive your digital transformation. Visit https://www.roamnook.com to learn more about how RoamNook can help your organization thrive in the digital age.

[milindjagre]

Post 35 | HDPCD | Insert records from NON-ORC table into ORC table

Insert records from NON-ORC table into ORC table

Hello, everyone. Thanks for going through the tutorials. The increasing views and visitors act as a motivation for me.

In the last tutorial, we saw how to define a hive ORC table. In this tutorial, we are going to load data in that ORC table from an NON-ORC table.

For doing this, we are going to follow the below process.

Loading records into ORC table from NON-ORC table

As you can see from the…

[isearchgoood]

February 11, 2020 at 10:00PM - The Big Data Bundle (93% discount) Ashraf

The Big Data Bundle (93% discount) Hurry Offer Only Last For HoursSometime. Don't ever forget to share this post on Your Social media to be the first to tell your firends. This is not a fake stuff its real.

Hive is a Big Data processing tool that helps you leverage the power of distributed computing and Hadoop for analytical processing. Its interface is somewhat similar to SQL, but with some key differences. This course is an end-to-end guide to using Hive and connecting the dots to SQL. It’s perfect for both professional and aspiring data analysts and engineers alike. Don’t know SQL? No problem, there’s a primer included in this course!

Access 86 lectures & 15 hours of content 24/7

Write complex analytical queries on data in Hive & uncover insights

Leverage ideas of partitioning & bucketing to optimize queries in Hive

Customize Hive w/ user defined functions in Java & Python

Understand what goes on under the hood of Hive w/ HDFS & MapReduce

Big Data sounds pretty daunting doesn’t it? Well, this course aims to make it a lot simpler for you. Using Hadoop and MapReduce, you’ll learn how to process and manage enormous amounts of data efficiently. Any company that collects mass amounts of data, from startups to Fortune 500, need people fluent in Hadoop and MapReduce, making this course a must for anybody interested in data science.

Access 71 lectures & 13 hours of content 24/7

Set up your own Hadoop cluster using virtual machines (VMs) & the Cloud

Understand HDFS, MapReduce & YARN & their interaction

Use MapReduce to recommend friends in a social network, build search engines & generate bigrams

Chain multiple MapReduce jobs together

Write your own customized partitioner

Learn to globally sort a large amount of data by sampling input files

Analysts and data scientists typically have to work with several systems to effectively manage mass sets of data. Spark, on the other hand, provides you a single engine to explore and work with large amounts of data, run machine learning algorithms, and perform many other functions in a single interactive environment. This course’s focus on new and innovating technologies in data science and machine learning makes it an excellent one for anyone who wants to work in the lucrative, growing field of Big Data.

Access 52 lectures & 8 hours of content 24/7

Use Spark for a variety of analytics & machine learning tasks

Implement complex algorithms like PageRank & Music Recommendations

Work w/ a variety of datasets from airline delays to Twitter, web graphs, & product ratings

Employ all the different features & libraries of Spark, like RDDs, Dataframes, Spark SQL, MLlib, Spark Streaming & GraphX

The functional programming nature and the availability of a REPL environment make Scala particularly well suited for a distributed computing framework like Spark. Using these two technologies in tandem can allow you to effectively analyze and explore data in an interactive environment with extremely fast feedback. This course will teach you how to best combine Spark and Scala, making it perfect for aspiring data analysts and Big Data engineers.

Access 51 lectures & 8.5 hours of content 24/7

Use Spark for a variety of analytics & machine learning tasks

Understand functional programming constructs in Scala

Implement complex algorithms like PageRank & Music Recommendations

Work w/ a variety of datasets from airline delays to Twitter, web graphs, & Product Ratings

Use the different features & libraries of Spark, like RDDs, Dataframes, Spark SQL, MLlib, Spark Streaming, & GraphX

Write code in Scala REPL environments & build Scala applications w/ an IDE

For Big Data engineers and data analysts, HBase is an extremely effective databasing tool for organizing and manage massive data sets. HBase allows an increased level of flexibility, providing column oriented storage, no fixed schema and low latency to accommodate the dynamically changing needs of applications. With the 25 examples contained in this course, you’ll get a complete grasp of HBase that you can leverage in interviews for Big Data positions.

Access 41 lectures & 4.5 hours of content 24/7

Set up a database for your application using HBase

Integrate HBase w/ MapReduce for data processing tasks

Create tables, insert, read & delete data from HBase

Get a complete understanding of HBase & its role in the Hadoop ecosystem

Explore CRUD operations in the shell, & with the Java API

Think about the last time you saw a completely unorganized spreadsheet. Now imagine that spreadsheet was 100,000 times larger. Mind-boggling, right? That’s why there’s Pig. Pig works with unstructured data to wrestle it into a more palatable form that can be stored in a data warehouse for reporting and analysis. With the massive sets of disorganized data many companies are working with today, people who can work with Pig are in major demand. By the end of this course, you could qualify as one of those people.

Access 34 lectures & 5 hours of content 24/7

Clean up server logs using Pig

Work w/ unstructured data to extract information, transform it, & store it in a usable form

Write intermediate level Pig scripts to munge data

Optimize Pig operations to work on large data sets

Data sets can outgrow traditional databases, much like children outgrow clothes. Unlike, children’s growth patterns, however, massive amounts of data can be extremely unpredictable and unstructured. For Big Data, the Cassandra distributed database is the solution, using partitioning and replication to ensure that your data is structured and available even when nodes in a cluster go down. Children, you’re on your own.

Access 44 lectures & 5.5 hours of content 24/7

Set up & manage a cluster using the Cassandra Cluster Manager (CCM)

Create keyspaces, column families, & perform CRUD operations using the Cassandra Query Language (CQL)

Design primary keys & secondary indexes, & learn partitioning & clustering keys

Understand restrictions on queries based on primary & secondary key design

Discover tunable consistency using quorum & local quorum

Learn architecture & storage components: Commit Log, MemTable, SSTables, Bloom Filters, Index File, Summary File & Data File

Build a Miniature Catalog Management System using the Cassandra Java driver

Working with Big Data, obviously, can be a very complex task. That’s why it’s important to master Oozie. Oozie makes managing a multitude of jobs at different time schedules, and managing entire data pipelines significantly easier as long as you know the right configurations parameters. This course will teach you how to best determine those parameters, so your workflow will be significantly streamlined.

Access 23 lectures & 3 hours of content 24/7

Install & set up Oozie

Configure Workflows to run jobs on Hadoop

Create time-triggered & data-triggered Workflows

Build & optimize data pipelines using Bundles

Flume and Sqoop are important elements of the Hadoop ecosystem, transporting data from sources like local file systems to data stores. This is an essential component to organizing and effectively managing Big Data, making Flume and Sqoop great skills to set you apart from other data analysts.

Access 16 lectures & 2 hours of content 24/7

Use Flume to ingest data to HDFS & HBase

Optimize Sqoop to import data from MySQL to HDFS & Hive

Ingest data from a variety of sources including HTTP, Twitter & MySQL

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[software-training-in-ameerp-blog]

Hadoop training in Hyderabad

Hadoop Training

HADOOP TRAINING COURSE CONTENTMAP REDUCE

Map Reduce Architecture

Map Reduce Programing Model

Map Reduce Program structure

Hadoop streaming

Executing Java – Map Reduce Job

Understanding of Java Map Reduce Classes

Configuration

Path

Job

Mapper

Reducer

Text

Intwritables

Long writables

File Input Format

File Output Format

Generic Options Pavser

Joining Datasets in Map Reduce Jobs

Map Joins

reduce Joins

Combiners Partitioners

Python Map Reduce

Unit Testing Mapeduce Jobs

Hadoop Pipelining

Creating Input and Output Formats in Map Reduce Jobs

Text Input Format

Key Value Input Format

Sequence File Input Format

Data Localization in Map Reduce

Examples

HIVE

Introduction

Hive Architecture

Hive Metastore

Hive Query Launguage

Difference between HQL and SQL

Hive Built in Functions

Hive UDF (user defined functions)

Hive UDAF (user defined Aggregated functions)

Hive UDTF (user defined table Generated functions)

Hive Serde?

Hive & Hbase Integration

Hive Working with unstructured data

Hive Working With Xml Data

Hive Working With Json Data

Hive Working With Urls And Weblog Data

Hive – Json – Serde

Loading Data From Local Files To Hive Tables

Loading Data From Hdfs Files To Hive Tables

Tables Types

Inner Tables

External Tables

Partitioned Tables

Non – Partitioned Tables

Dynamic Partitions In Hive

Concept Of Bucketing

Hive Views

Hive Unions

Hive Joins

Multi Table / File Inserts

Inserting Into Local Files

Inserting Into Hdfs Files

Array Operations In Hive

Map (Associative Arrays) Operations in Hive

Hive UDF by Java

Hive UDF by Python

PIG

Introduction to pig

Pig Latin Script

Pig Console / Grunt Shell

Execting Pig Latin Script

Pig Relations, Bags, Tuples, Fields

Data Types

Nulls

Constants

Expressions

Schemas

Parameter Substitution

Arithmetic Operators

Comparison Operators

Null Operators

Boolean Operators

Defence Operators

Sign Operators

Flatten Operators

Caster Operators

Relational Operators in Pig

ICOGROUP

CROSS

DISTINCT

FILTER

FOREACH

GROUP

JOIN (INNER)

JOIN (OUTER)

LIMIT

LOAD

ORDER

SAMPLE

SPILT

STORE

UNION

Diagnostic Operators in Pig

Describe

Dump

Explain

Illustrate

Eval Functions in Pig

AVG

CONCAT

COUNT

CONI-STAR

DIFF

IS EMPTY

MAX

MIN

SIZE

SUM

TOKENIZE

writing Custom UDFS in Pig

Using Java

Using Python

SQOOP (SQL + HADOOP)

Introduction to Sqoop

SQOOP Import

SQOOP Export

Importing Data From RDBMS to HDFS

Importing Data From RDBMS to HIVE

Importing Data From RDBMS to HBASE

Exporting From HASE to RDBMS

Exporting From HBASE to RDBMS

Exporting From HIVE to RDBMS

Exporting From HDFS to RDBMS

Transformations While Importing / Exporting

Defining SQOOP Jobs

NOSQL

What is “Not only SQL”

NOSQL Advantages

What is problem with RDBMS for Large

Data Scaling Systems

Types of NOSQL & Purposes

Key Value Store

Columer Store

Document Store

Graph Store

Introduction to ricsk – NOSQL Database

Introduction to cassandra – NOSQL Database

Introduction to MangoDB and CouchDB Database

Introduction to Neo4j – NOSQL Database

Intergration of NOSQL Databases with Hadoop

HBASE

Introduction to big table

What is NOSQL and colummer store Database

HBASE Introduction

Hbase use cases

Hbase basics

Column families

Scans

Hbase Architecture

Clients

Rest

Thrift

Java

Hive

Map Reduce Integration

Map Reduce Over Hbase

Hbase data Modeling

Hbase Schema design

Hbase CRUD operators

Hive & Hbase interagation

Hbase storage handles

OOZIE

Introduction to OOZIE

OOZIE as a seheduler

OOZIE as a Workflow designer

Seheduling jobs (OOZIE CODE)

Defining Dependences between jobs

(OOZIE Code Examples)

Conditionally controling jobs

(OOZIE Code Examples)

Defining parallel jobs (OOZIE Code Examples)

FLUME

Introduction to FLUME

What is the streaming File

FLUME Architecture

FLUME Nodes & FLUME Manager

FLUME Local & Physical Node

FLUME Agents & FLUME Collector

ZOOKIPER

Introduction to ZOOKEEPER

ZOOKEEPER Architecture

Controlling Connection of Distbrited Apps

HBASE & ZOOKEEPER

Flume & ZOOKEEPER

A Sample Code

Free On Hadoop Course

Phyton & Pydoop

Mongo DB

Cascading

Tags:-

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hadoop training in hyderabad

[milindjagre]

Post 34 | HDPCD | Defining Hive Table using an ORC File Format

Post 34 | HDPCD | Defining Hive Table using an ORC File Format

Hi, everyone. Thanks for joining me today for this tutorial.

In the last tutorial, we saw how to create a hive table using the SELECT query. In this tutorial, we are going to see how to create a hive table which stores the data in the ORC File Format.

The process of creating this table is similar to the internal table creation process in thistutorial with only one change in the InputFileFormat of…

[milindjagre]

Post 33 | HDPCD | Define a Table from a SELECT Query

Apache HIVE: Define a Table from a SELECT Query

Hello everyone and welcome to one more tutorial in the HDPCD certification series.

In the last tutorial, we saw how to define a BUCKETED hive table. In this tutorial, we are going to see how to create a Hive table from a SELECT query.

Let us begin then.

We are going to follow the below process for creating a brand new table from a SELECT query. It is also called as CTASwhich stands for Create…

[milindjagre]

Post 32 | HDPCD | Defining a Bucketed Hive Table

Define a BUCKETED HIVE table

Hello everyone to the next tutorial in the HDPCD certification series.

In the last tutorial, we saw how to create a Partitioned Hive Table. In this tutorial, we are going to see how to create a Bucketed Hive table.

The process is depicted in the following infographics.

Apache Hive: Creating a BUCKETED table

As you can see from the above picture, it follows the same process like the previoustutori…

[isearchgoood]

January 20, 2020 at 10:00PM - The Big Data Bundle (93% discount) Ashraf

The Big Data Bundle (93% discount) Hurry Offer Only Last For HoursSometime. Don't ever forget to share this post on Your Social media to be the first to tell your firends. This is not a fake stuff its real.

Hive is a Big Data processing tool that helps you leverage the power of distributed computing and Hadoop for analytical processing. Its interface is somewhat similar to SQL, but with some key differences. This course is an end-to-end guide to using Hive and connecting the dots to SQL. It’s perfect for both professional and aspiring data analysts and engineers alike. Don’t know SQL? No problem, there’s a primer included in this course!

Access 86 lectures & 15 hours of content 24/7

Write complex analytical queries on data in Hive & uncover insights

Leverage ideas of partitioning & bucketing to optimize queries in Hive

Customize Hive w/ user defined functions in Java & Python

Understand what goes on under the hood of Hive w/ HDFS & MapReduce

Big Data sounds pretty daunting doesn’t it? Well, this course aims to make it a lot simpler for you. Using Hadoop and MapReduce, you’ll learn how to process and manage enormous amounts of data efficiently. Any company that collects mass amounts of data, from startups to Fortune 500, need people fluent in Hadoop and MapReduce, making this course a must for anybody interested in data science.

Access 71 lectures & 13 hours of content 24/7

Set up your own Hadoop cluster using virtual machines (VMs) & the Cloud

Understand HDFS, MapReduce & YARN & their interaction

Use MapReduce to recommend friends in a social network, build search engines & generate bigrams

Chain multiple MapReduce jobs together

Write your own customized partitioner

Learn to globally sort a large amount of data by sampling input files

Analysts and data scientists typically have to work with several systems to effectively manage mass sets of data. Spark, on the other hand, provides you a single engine to explore and work with large amounts of data, run machine learning algorithms, and perform many other functions in a single interactive environment. This course’s focus on new and innovating technologies in data science and machine learning makes it an excellent one for anyone who wants to work in the lucrative, growing field of Big Data.

Access 52 lectures & 8 hours of content 24/7

Use Spark for a variety of analytics & machine learning tasks

Implement complex algorithms like PageRank & Music Recommendations

Work w/ a variety of datasets from airline delays to Twitter, web graphs, & product ratings

Employ all the different features & libraries of Spark, like RDDs, Dataframes, Spark SQL, MLlib, Spark Streaming & GraphX

The functional programming nature and the availability of a REPL environment make Scala particularly well suited for a distributed computing framework like Spark. Using these two technologies in tandem can allow you to effectively analyze and explore data in an interactive environment with extremely fast feedback. This course will teach you how to best combine Spark and Scala, making it perfect for aspiring data analysts and Big Data engineers.

Access 51 lectures & 8.5 hours of content 24/7

Use Spark for a variety of analytics & machine learning tasks

Understand functional programming constructs in Scala

Implement complex algorithms like PageRank & Music Recommendations

Work w/ a variety of datasets from airline delays to Twitter, web graphs, & Product Ratings

Use the different features & libraries of Spark, like RDDs, Dataframes, Spark SQL, MLlib, Spark Streaming, & GraphX

Write code in Scala REPL environments & build Scala applications w/ an IDE

For Big Data engineers and data analysts, HBase is an extremely effective databasing tool for organizing and manage massive data sets. HBase allows an increased level of flexibility, providing column oriented storage, no fixed schema and low latency to accommodate the dynamically changing needs of applications. With the 25 examples contained in this course, you’ll get a complete grasp of HBase that you can leverage in interviews for Big Data positions.

Access 41 lectures & 4.5 hours of content 24/7

Set up a database for your application using HBase

Integrate HBase w/ MapReduce for data processing tasks

Create tables, insert, read & delete data from HBase

Get a complete understanding of HBase & its role in the Hadoop ecosystem

Explore CRUD operations in the shell, & with the Java API

Think about the last time you saw a completely unorganized spreadsheet. Now imagine that spreadsheet was 100,000 times larger. Mind-boggling, right? That’s why there’s Pig. Pig works with unstructured data to wrestle it into a more palatable form that can be stored in a data warehouse for reporting and analysis. With the massive sets of disorganized data many companies are working with today, people who can work with Pig are in major demand. By the end of this course, you could qualify as one of those people.

Access 34 lectures & 5 hours of content 24/7

Clean up server logs using Pig

Work w/ unstructured data to extract information, transform it, & store it in a usable form

Write intermediate level Pig scripts to munge data

Optimize Pig operations to work on large data sets

Data sets can outgrow traditional databases, much like children outgrow clothes. Unlike, children’s growth patterns, however, massive amounts of data can be extremely unpredictable and unstructured. For Big Data, the Cassandra distributed database is the solution, using partitioning and replication to ensure that your data is structured and available even when nodes in a cluster go down. Children, you’re on your own.

Access 44 lectures & 5.5 hours of content 24/7

Set up & manage a cluster using the Cassandra Cluster Manager (CCM)

Create keyspaces, column families, & perform CRUD operations using the Cassandra Query Language (CQL)

Design primary keys & secondary indexes, & learn partitioning & clustering keys

Understand restrictions on queries based on primary & secondary key design

Discover tunable consistency using quorum & local quorum

Learn architecture & storage components: Commit Log, MemTable, SSTables, Bloom Filters, Index File, Summary File & Data File

Build a Miniature Catalog Management System using the Cassandra Java driver

Working with Big Data, obviously, can be a very complex task. That’s why it’s important to master Oozie. Oozie makes managing a multitude of jobs at different time schedules, and managing entire data pipelines significantly easier as long as you know the right configurations parameters. This course will teach you how to best determine those parameters, so your workflow will be significantly streamlined.

Access 23 lectures & 3 hours of content 24/7

Install & set up Oozie

Configure Workflows to run jobs on Hadoop

Create time-triggered & data-triggered Workflows

Build & optimize data pipelines using Bundles

Flume and Sqoop are important elements of the Hadoop ecosystem, transporting data from sources like local file systems to data stores. This is an essential component to organizing and effectively managing Big Data, making Flume and Sqoop great skills to set you apart from other data analysts.

Access 16 lectures & 2 hours of content 24/7

Use Flume to ingest data to HDFS & HBase

Optimize Sqoop to import data from MySQL to HDFS & Hive

Ingest data from a variety of sources including HTTP, Twitter & MySQL

from Active Sales – SharewareOnSale https://ift.tt/2qeN7bl https://ift.tt/eA8V8J via Blogger https://ift.tt/36fNMJC #blogger #bloggingtips #bloggerlife #bloggersgetsocial #ontheblog #writersofinstagram #writingprompt #instapoetry #writerscommunity #writersofig #writersblock #writerlife #writtenword #instawriters #spilledink #wordgasm #creativewriting #poetsofinstagram #blackoutpoetry #poetsofig

[isearchgoood]

October 12, 2019 at 10:00PM - The Big Data Bundle (93% discount) Ashraf

The Big Data Bundle (93% discount) Hurry Offer Only Last For HoursSometime. Don't ever forget to share this post on Your Social media to be the first to tell your firends. This is not a fake stuff its real.

Hive is a Big Data processing tool that helps you leverage the power of distributed computing and Hadoop for analytical processing. Its interface is somewhat similar to SQL, but with some key differences. This course is an end-to-end guide to using Hive and connecting the dots to SQL. It’s perfect for both professional and aspiring data analysts and engineers alike. Don’t know SQL? No problem, there’s a primer included in this course!

Access 86 lectures & 15 hours of content 24/7

Write complex analytical queries on data in Hive & uncover insights

Leverage ideas of partitioning & bucketing to optimize queries in Hive

Customize Hive w/ user defined functions in Java & Python

Understand what goes on under the hood of Hive w/ HDFS & MapReduce

Big Data sounds pretty daunting doesn’t it? Well, this course aims to make it a lot simpler for you. Using Hadoop and MapReduce, you’ll learn how to process and manage enormous amounts of data efficiently. Any company that collects mass amounts of data, from startups to Fortune 500, need people fluent in Hadoop and MapReduce, making this course a must for anybody interested in data science.

Access 71 lectures & 13 hours of content 24/7

Set up your own Hadoop cluster using virtual machines (VMs) & the Cloud

Understand HDFS, MapReduce & YARN & their interaction

Use MapReduce to recommend friends in a social network, build search engines & generate bigrams

Chain multiple MapReduce jobs together

Write your own customized partitioner

Learn to globally sort a large amount of data by sampling input files

Analysts and data scientists typically have to work with several systems to effectively manage mass sets of data. Spark, on the other hand, provides you a single engine to explore and work with large amounts of data, run machine learning algorithms, and perform many other functions in a single interactive environment. This course’s focus on new and innovating technologies in data science and machine learning makes it an excellent one for anyone who wants to work in the lucrative, growing field of Big Data.

Access 52 lectures & 8 hours of content 24/7

Use Spark for a variety of analytics & machine learning tasks

Implement complex algorithms like PageRank & Music Recommendations

Work w/ a variety of datasets from airline delays to Twitter, web graphs, & product ratings

Employ all the different features & libraries of Spark, like RDDs, Dataframes, Spark SQL, MLlib, Spark Streaming & GraphX

The functional programming nature and the availability of a REPL environment make Scala particularly well suited for a distributed computing framework like Spark. Using these two technologies in tandem can allow you to effectively analyze and explore data in an interactive environment with extremely fast feedback. This course will teach you how to best combine Spark and Scala, making it perfect for aspiring data analysts and Big Data engineers.

Access 51 lectures & 8.5 hours of content 24/7

Use Spark for a variety of analytics & machine learning tasks

Understand functional programming constructs in Scala

Implement complex algorithms like PageRank & Music Recommendations

Work w/ a variety of datasets from airline delays to Twitter, web graphs, & Product Ratings

Use the different features & libraries of Spark, like RDDs, Dataframes, Spark SQL, MLlib, Spark Streaming, & GraphX

Write code in Scala REPL environments & build Scala applications w/ an IDE

For Big Data engineers and data analysts, HBase is an extremely effective databasing tool for organizing and manage massive data sets. HBase allows an increased level of flexibility, providing column oriented storage, no fixed schema and low latency to accommodate the dynamically changing needs of applications. With the 25 examples contained in this course, you’ll get a complete grasp of HBase that you can leverage in interviews for Big Data positions.

Access 41 lectures & 4.5 hours of content 24/7

Set up a database for your application using HBase

Integrate HBase w/ MapReduce for data processing tasks

Create tables, insert, read & delete data from HBase

Get a complete understanding of HBase & its role in the Hadoop ecosystem

Explore CRUD operations in the shell, & with the Java API

Think about the last time you saw a completely unorganized spreadsheet. Now imagine that spreadsheet was 100,000 times larger. Mind-boggling, right? That’s why there’s Pig. Pig works with unstructured data to wrestle it into a more palatable form that can be stored in a data warehouse for reporting and analysis. With the massive sets of disorganized data many companies are working with today, people who can work with Pig are in major demand. By the end of this course, you could qualify as one of those people.

Access 34 lectures & 5 hours of content 24/7

Clean up server logs using Pig

Work w/ unstructured data to extract information, transform it, & store it in a usable form

Write intermediate level Pig scripts to munge data

Optimize Pig operations to work on large data sets

Data sets can outgrow traditional databases, much like children outgrow clothes. Unlike, children’s growth patterns, however, massive amounts of data can be extremely unpredictable and unstructured. For Big Data, the Cassandra distributed database is the solution, using partitioning and replication to ensure that your data is structured and available even when nodes in a cluster go down. Children, you’re on your own.

Access 44 lectures & 5.5 hours of content 24/7

Set up & manage a cluster using the Cassandra Cluster Manager (CCM)

Create keyspaces, column families, & perform CRUD operations using the Cassandra Query Language (CQL)

Design primary keys & secondary indexes, & learn partitioning & clustering keys

Understand restrictions on queries based on primary & secondary key design

Discover tunable consistency using quorum & local quorum

Learn architecture & storage components: Commit Log, MemTable, SSTables, Bloom Filters, Index File, Summary File & Data File

Build a Miniature Catalog Management System using the Cassandra Java driver

Working with Big Data, obviously, can be a very complex task. That’s why it’s important to master Oozie. Oozie makes managing a multitude of jobs at different time schedules, and managing entire data pipelines significantly easier as long as you know the right configurations parameters. This course will teach you how to best determine those parameters, so your workflow will be significantly streamlined.

Access 23 lectures & 3 hours of content 24/7

Install & set up Oozie

Configure Workflows to run jobs on Hadoop

Create time-triggered & data-triggered Workflows

Build & optimize data pipelines using Bundles

Flume and Sqoop are important elements of the Hadoop ecosystem, transporting data from sources like local file systems to data stores. This is an essential component to organizing and effectively managing Big Data, making Flume and Sqoop great skills to set you apart from other data analysts.

Access 16 lectures & 2 hours of content 24/7

Use Flume to ingest data to HDFS & HBase

Optimize Sqoop to import data from MySQL to HDFS & Hive

Ingest data from a variety of sources including HTTP, Twitter & MySQL

from Active Sales – SharewareOnSale https://ift.tt/2qeN7bl https://ift.tt/eA8V8J via Blogger https://ift.tt/31cHtUE #blogger #bloggingtips #bloggerlife #bloggersgetsocial #ontheblog #writersofinstagram #writingprompt #instapoetry #writerscommunity #writersofig #writersblock #writerlife #writtenword #instawriters #spilledink #wordgasm #creativewriting #poetsofinstagram #blackoutpoetry #poetsofig

[isearchgoood]

July 29, 2019 at 10:00PM - The Big Data Bundle (93% discount) Ashraf

The Big Data Bundle (93% discount) Hurry Offer Only Last For HoursSometime. Don't ever forget to share this post on Your Social media to be the first to tell your firends. This is not a fake stuff its real.

Hive is a Big Data processing tool that helps you leverage the power of distributed computing and Hadoop for analytical processing. Its interface is somewhat similar to SQL, but with some key differences. This course is an end-to-end guide to using Hive and connecting the dots to SQL. It’s perfect for both professional and aspiring data analysts and engineers alike. Don’t know SQL? No problem, there’s a primer included in this course!

Access 86 lectures & 15 hours of content 24/7

Write complex analytical queries on data in Hive & uncover insights

Leverage ideas of partitioning & bucketing to optimize queries in Hive

Customize Hive w/ user defined functions in Java & Python

Understand what goes on under the hood of Hive w/ HDFS & MapReduce

Big Data sounds pretty daunting doesn’t it? Well, this course aims to make it a lot simpler for you. Using Hadoop and MapReduce, you’ll learn how to process and manage enormous amounts of data efficiently. Any company that collects mass amounts of data, from startups to Fortune 500, need people fluent in Hadoop and MapReduce, making this course a must for anybody interested in data science.

Access 71 lectures & 13 hours of content 24/7

Set up your own Hadoop cluster using virtual machines (VMs) & the Cloud

Understand HDFS, MapReduce & YARN & their interaction

Use MapReduce to recommend friends in a social network, build search engines & generate bigrams

Chain multiple MapReduce jobs together

Write your own customized partitioner

Learn to globally sort a large amount of data by sampling input files

Analysts and data scientists typically have to work with several systems to effectively manage mass sets of data. Spark, on the other hand, provides you a single engine to explore and work with large amounts of data, run machine learning algorithms, and perform many other functions in a single interactive environment. This course’s focus on new and innovating technologies in data science and machine learning makes it an excellent one for anyone who wants to work in the lucrative, growing field of Big Data.

Access 52 lectures & 8 hours of content 24/7

Use Spark for a variety of analytics & machine learning tasks

Implement complex algorithms like PageRank & Music Recommendations

Work w/ a variety of datasets from airline delays to Twitter, web graphs, & product ratings

Employ all the different features & libraries of Spark, like RDDs, Dataframes, Spark SQL, MLlib, Spark Streaming & GraphX

The functional programming nature and the availability of a REPL environment make Scala particularly well suited for a distributed computing framework like Spark. Using these two technologies in tandem can allow you to effectively analyze and explore data in an interactive environment with extremely fast feedback. This course will teach you how to best combine Spark and Scala, making it perfect for aspiring data analysts and Big Data engineers.

Access 51 lectures & 8.5 hours of content 24/7

Use Spark for a variety of analytics & machine learning tasks

Understand functional programming constructs in Scala

Implement complex algorithms like PageRank & Music Recommendations

Work w/ a variety of datasets from airline delays to Twitter, web graphs, & Product Ratings

Use the different features & libraries of Spark, like RDDs, Dataframes, Spark SQL, MLlib, Spark Streaming, & GraphX

Write code in Scala REPL environments & build Scala applications w/ an IDE

For Big Data engineers and data analysts, HBase is an extremely effective databasing tool for organizing and manage massive data sets. HBase allows an increased level of flexibility, providing column oriented storage, no fixed schema and low latency to accommodate the dynamically changing needs of applications. With the 25 examples contained in this course, you’ll get a complete grasp of HBase that you can leverage in interviews for Big Data positions.

Access 41 lectures & 4.5 hours of content 24/7

Set up a database for your application using HBase

Integrate HBase w/ MapReduce for data processing tasks

Create tables, insert, read & delete data from HBase

Get a complete understanding of HBase & its role in the Hadoop ecosystem

Explore CRUD operations in the shell, & with the Java API

Think about the last time you saw a completely unorganized spreadsheet. Now imagine that spreadsheet was 100,000 times larger. Mind-boggling, right? That’s why there’s Pig. Pig works with unstructured data to wrestle it into a more palatable form that can be stored in a data warehouse for reporting and analysis. With the massive sets of disorganized data many companies are working with today, people who can work with Pig are in major demand. By the end of this course, you could qualify as one of those people.

Access 34 lectures & 5 hours of content 24/7

Clean up server logs using Pig

Work w/ unstructured data to extract information, transform it, & store it in a usable form

Write intermediate level Pig scripts to munge data

Optimize Pig operations to work on large data sets

Data sets can outgrow traditional databases, much like children outgrow clothes. Unlike, children’s growth patterns, however, massive amounts of data can be extremely unpredictable and unstructured. For Big Data, the Cassandra distributed database is the solution, using partitioning and replication to ensure that your data is structured and available even when nodes in a cluster go down. Children, you’re on your own.

Access 44 lectures & 5.5 hours of content 24/7

Set up & manage a cluster using the Cassandra Cluster Manager (CCM)

Create keyspaces, column families, & perform CRUD operations using the Cassandra Query Language (CQL)

Design primary keys & secondary indexes, & learn partitioning & clustering keys

Understand restrictions on queries based on primary & secondary key design

Discover tunable consistency using quorum & local quorum

Learn architecture & storage components: Commit Log, MemTable, SSTables, Bloom Filters, Index File, Summary File & Data File

Build a Miniature Catalog Management System using the Cassandra Java driver

Working with Big Data, obviously, can be a very complex task. That’s why it’s important to master Oozie. Oozie makes managing a multitude of jobs at different time schedules, and managing entire data pipelines significantly easier as long as you know the right configurations parameters. This course will teach you how to best determine those parameters, so your workflow will be significantly streamlined.

Access 23 lectures & 3 hours of content 24/7

Install & set up Oozie

Configure Workflows to run jobs on Hadoop

Create time-triggered & data-triggered Workflows

Build & optimize data pipelines using Bundles

Flume and Sqoop are important elements of the Hadoop ecosystem, transporting data from sources like local file systems to data stores. This is an essential component to organizing and effectively managing Big Data, making Flume and Sqoop great skills to set you apart from other data analysts.

Access 16 lectures & 2 hours of content 24/7

Use Flume to ingest data to HDFS & HBase

Optimize Sqoop to import data from MySQL to HDFS & Hive

Ingest data from a variety of sources including HTTP, Twitter & MySQL

from Active Sales – SharewareOnSale https://ift.tt/2qeN7bl https://ift.tt/eA8V8J via Blogger https://ift.tt/2MjWyQG #blogger #bloggingtips #bloggerlife #bloggersgetsocial #ontheblog #writersofinstagram #writingprompt #instapoetry #writerscommunity #writersofig #writersblock #writerlife #writtenword #instawriters #spilledink #wordgasm #creativewriting #poetsofinstagram #blackoutpoetry #poetsofig

[isearchgoood]

July 11, 2018 at 10:01PM - The Big Data Bundle (93% discount) Ashraf

The Big Data Bundle (93% discount) Hurry Offer Only Last For HoursSometime. Don't ever forget to share this post on Your Social media to be the first to tell your firends. This is not a fake stuff its real.

Hive is a Big Data processing tool that helps you leverage the power of distributed computing and Hadoop for analytical processing. Its interface is somewhat similar to SQL, but with some key differences. This course is an end-to-end guide to using Hive and connecting the dots to SQL. It’s perfect for both professional and aspiring data analysts and engineers alike. Don’t know SQL? No problem, there’s a primer included in this course!

Access 86 lectures & 15 hours of content 24/7

Write complex analytical queries on data in Hive & uncover insights

Leverage ideas of partitioning & bucketing to optimize queries in Hive

Customize Hive w/ user defined functions in Java & Python

Understand what goes on under the hood of Hive w/ HDFS & MapReduce

Big Data sounds pretty daunting doesn’t it? Well, this course aims to make it a lot simpler for you. Using Hadoop and MapReduce, you’ll learn how to process and manage enormous amounts of data efficiently. Any company that collects mass amounts of data, from startups to Fortune 500, need people fluent in Hadoop and MapReduce, making this course a must for anybody interested in data science.

Access 71 lectures & 13 hours of content 24/7

Set up your own Hadoop cluster using virtual machines (VMs) & the Cloud

Understand HDFS, MapReduce & YARN & their interaction

Use MapReduce to recommend friends in a social network, build search engines & generate bigrams

Chain multiple MapReduce jobs together

Write your own customized partitioner

Learn to globally sort a large amount of data by sampling input files

Analysts and data scientists typically have to work with several systems to effectively manage mass sets of data. Spark, on the other hand, provides you a single engine to explore and work with large amounts of data, run machine learning algorithms, and perform many other functions in a single interactive environment. This course’s focus on new and innovating technologies in data science and machine learning makes it an excellent one for anyone who wants to work in the lucrative, growing field of Big Data.

Access 52 lectures & 8 hours of content 24/7

Use Spark for a variety of analytics & machine learning tasks

Implement complex algorithms like PageRank & Music Recommendations

Work w/ a variety of datasets from airline delays to Twitter, web graphs, & product ratings

Employ all the different features & libraries of Spark, like RDDs, Dataframes, Spark SQL, MLlib, Spark Streaming & GraphX

The functional programming nature and the availability of a REPL environment make Scala particularly well suited for a distributed computing framework like Spark. Using these two technologies in tandem can allow you to effectively analyze and explore data in an interactive environment with extremely fast feedback. This course will teach you how to best combine Spark and Scala, making it perfect for aspiring data analysts and Big Data engineers.

Access 51 lectures & 8.5 hours of content 24/7

Use Spark for a variety of analytics & machine learning tasks

Understand functional programming constructs in Scala

Implement complex algorithms like PageRank & Music Recommendations

Work w/ a variety of datasets from airline delays to Twitter, web graphs, & Product Ratings

Use the different features & libraries of Spark, like RDDs, Dataframes, Spark SQL, MLlib, Spark Streaming, & GraphX

Write code in Scala REPL environments & build Scala applications w/ an IDE

For Big Data engineers and data analysts, HBase is an extremely effective databasing tool for organizing and manage massive data sets. HBase allows an increased level of flexibility, providing column oriented storage, no fixed schema and low latency to accommodate the dynamically changing needs of applications. With the 25 examples contained in this course, you’ll get a complete grasp of HBase that you can leverage in interviews for Big Data positions.

Access 41 lectures & 4.5 hours of content 24/7

Set up a database for your application using HBase

Integrate HBase w/ MapReduce for data processing tasks

Create tables, insert, read & delete data from HBase

Get a complete understanding of HBase & its role in the Hadoop ecosystem

Explore CRUD operations in the shell, & with the Java API

Think about the last time you saw a completely unorganized spreadsheet. Now imagine that spreadsheet was 100,000 times larger. Mind-boggling, right? That’s why there’s Pig. Pig works with unstructured data to wrestle it into a more palatable form that can be stored in a data warehouse for reporting and analysis. With the massive sets of disorganized data many companies are working with today, people who can work with Pig are in major demand. By the end of this course, you could qualify as one of those people.

Access 34 lectures & 5 hours of content 24/7

Clean up server logs using Pig

Work w/ unstructured data to extract information, transform it, & store it in a usable form

Write intermediate level Pig scripts to munge data

Optimize Pig operations to work on large data sets

Data sets can outgrow traditional databases, much like children outgrow clothes. Unlike, children’s growth patterns, however, massive amounts of data can be extremely unpredictable and unstructured. For Big Data, the Cassandra distributed database is the solution, using partitioning and replication to ensure that your data is structured and available even when nodes in a cluster go down. Children, you’re on your own.

Access 44 lectures & 5.5 hours of content 24/7

Set up & manage a cluster using the Cassandra Cluster Manager (CCM)

Create keyspaces, column families, & perform CRUD operations using the Cassandra Query Language (CQL)

Design primary keys & secondary indexes, & learn partitioning & clustering keys

Understand restrictions on queries based on primary & secondary key design

Discover tunable consistency using quorum & local quorum

Learn architecture & storage components: Commit Log, MemTable, SSTables, Bloom Filters, Index File, Summary File & Data File

Build a Miniature Catalog Management System using the Cassandra Java driver

Working with Big Data, obviously, can be a very complex task. That’s why it’s important to master Oozie. Oozie makes managing a multitude of jobs at different time schedules, and managing entire data pipelines significantly easier as long as you know the right configurations parameters. This course will teach you how to best determine those parameters, so your workflow will be significantly streamlined.

Access 23 lectures & 3 hours of content 24/7

Install & set up Oozie

Configure Workflows to run jobs on Hadoop

Create time-triggered & data-triggered Workflows

Build & optimize data pipelines using Bundles

Flume and Sqoop are important elements of the Hadoop ecosystem, transporting data from sources like local file systems to data stores. This is an essential component to organizing and effectively managing Big Data, making Flume and Sqoop great skills to set you apart from other data analysts.

Access 16 lectures & 2 hours of content 24/7

Use Flume to ingest data to HDFS & HBase

Optimize Sqoop to import data from MySQL to HDFS & Hive

Ingest data from a variety of sources including HTTP, Twitter & MySQL

from Active Sales – SharewareOnSale https://ift.tt/2qeN7bl https://ift.tt/eA8V8J via Blogger https://ift.tt/2NbgbHL #blogger #bloggingtips #bloggerlife #bloggersgetsocial #ontheblog #writersofinstagram #writingprompt #instapoetry #writerscommunity #writersofig #writersblock #writerlife #writtenword #instawriters #spilledink #wordgasm #creativewriting #poetsofinstagram #blackoutpoetry #poetsofig