The Big Data Trends of 2016

Solr + Hadoop = Big Data Love

2016’s BIG Three Trends are:

  1. Apache Spark production deployments
  2. Conversion from other platforms to Hadoop
  3. Leveraging Hadoop for advanced use cases

One of the actual utilizations of future era parallel and appropriated frameworks is in big-data analytics. Analysis tasks regularly have hard due dates, and data quality is an essential concern in yet different applications. For most rising applications, data-driven models and strategies, fit for operating at scale, are as-yet unknown.

Solr + Hadoop = Big Data Love big data image

Hadoop, a structure, and collection of tools for processing enormous data sets, was originally designed to work on clusters of physical machines. That has changed.

Distributed analytic frameworks, for example, MapReduce, are developing into appropriate resource managers that are gradually transforming Hadoop into a universally useful data operating system. With these frameworks, one can perform a broad range of data manipulations and analytics operations by connecting them to Hadoop as the disseminated document storage system.

Solr + Hadoop = Big Data Love big data image 2

The blend of big data and compute power likewise allows analysts investigate new behavioral data for the duration of the day, for example, websites visited or location.

Big data isn’t significantly big and can be as much about the complexities of preparing information as about volumes or data types.

One of the actual utilizations of future era parallel and appropriated frameworks is in big-data analytics. Analysis tasks regularly have hard due dates, and data quality is an essential concern in yet different applications. For most rising applications, data-driven models and strategies, fit for operating at scale, are as-yet unknown.

Solr + Hadoop = Big Data Love why is hadoop important new image

Hadoop

Hadoop, a structure, and collection of tools for processing enormous data sets, was originally designed to work on groups of physical machines. That has changed.

Hadoop is the first data operating system which makes it so powerful, and large enterprises are interested in it. But maybe they’re not all followers yet.

Research shows that 45% of big companies say they’re doing a Hadoop proof of concept, with 16 percent using it in

Hadoop is an open-source software framework for storing data and running applications on bunches of specialty hardware. It provides massive storage for any data, enormous processing power and the ability to manage essentially endless concurrent tasks or jobs.

Many people use the Hadoop accessible source project to process large data sets because it’s an excellent solution for scalable, reliable data processing workflows. Hadoop is by far the most conventional system for handling big data, with companies using massive clusters to store and process petabytes of data on thousands of servers.

Solr

Solr + Hadoop = Big Data Love solr image

Solr is highly reliable, scalable and faults liberal, implementing assigned indexing, replication and load-balanced querying, automated failover and restoration, centralized arrangement and further. Solr capability the exploration and research features of many of the world’s largest internet sites.

Features:

  1. Uses the Lucene library for full-text search
  2. Faceted navigation
  3. Hit highlighting
  4. Inquiry language supports structured as well as textual search
  5. Schema-less mode and Schema REST API
  6. JSON, XML, PHP, Ruby, Python, XSLT, Velocity and custom Java binary output formats over HTTP
  7. HTML administration interface
  8. Built-in security: Authentication, Authorization, SSL
  9. Replication to separate Solr servers – facilitates scaling QPS and High Availability
  10. Distributed Search through Sharding – enables scaling content volume
  11. Search results clustering based on Carrot2
  12. Extensible through plugins
  13. Flexible relevance – boost through function queries
  14. Caching – queries, filters, and documents
  15. Embeddable in a Java Application
  16. Geo-spatial search, including multiple points per documents and polygons
  17. Automated management of large clusters through ZooKeeper

 

The opinions expressed in this article are the author’s own and do not reflect the view of the organization. Original post here

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Virtualizing Analytics with Apache Spark

In the race to invent multi-million dollar business opportunities with exclusive insights, data scientists and engineers are hampered by a multitude of challenges just to make one use case a reality – the need to ingest data from multiple sources, apply real-time analytics, build machine learning algorithms, and intermix different data processing models, all while navigating around their legacy data infrastructure that is just not up to the task. This need has created the demand for Virtual Analytics, where the complexities of disparate data and technology silos have been abstracted away, coupled with a powerful range of analytics and processing horsepower, all in one unified data platform. This talk describes how Databricks is powering this revolutionary new trend with Apache Spark.


Speaker: Arsalan Tavakoli

This talk was originally presented at Spark Summit East 2017.

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What to Expect for Big Data and Apache Spark in 2017

Big data remains a rapidly evolving field with new applications and infrastructure appearing every year. In this talk, Matei Zaharia will cover new trends in 2016 / 2017 and how Apache Spark is moving to meet them. In particular, he will talk about work Databricks is doing to make Apache Spark interact better with native code (e.g. deep learning libraries), support heterogeneous hardware, and simplify production data pipelines in both streaming and batch settings through Structured Streaming.

This talk was originally presented at Spark Summit East 2017.

You can view the slides on Slideshare:
http://www.slideshare.net/databricks/…

 

 

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Get Rid of Traditional ETL, Move to Spark! (Bas Geerdink)

ETL has been around since the 90s, supporting a whole ecosystem of BI tools and practises. While traditional ETL has proven its value, it’s time to move on to modern ways of getting your data from A to B. Since BI moved to big data, data warehousing became data lakes, and applications became microservices, ETL is next our our list of obsolete terms. Spark provides an ideal middleware framework for writing code that gets the job done fast, reliable, readable. In this session I will support this statement with some nice ‘old vs new’ diagrams, code examples and use cases. Please join if you want to know more about the NoETL paradigm, or just want to be convinced of the possibilities of Spark in this area!

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Coursera: Functional Programming in Scala Specialization

Discover how to write elegant code that works the first time it is run.

This Specialization provides a hands-on introduction to functional programming using the widespread programming language, Scala. It begins from the basic building blocks of the functional paradigm, first showing how to use these blocks to solve small problems, before building up to combining these concepts to architect larger functional programs. You’ll see how the functional paradigm facilitates parallel and distributed programming, and through a series of hands on examples and programming assignments, you’ll learn how to analyze data sets small to large; from parallel programming on multicore architectures, to distributed programming on a cluster using Apache Spark. A final capstone project will allow you to apply the skills you learned by building a large data-intensive application using real-world data.

More information 

 

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Spark Reading and Writing to Parquet Storage Format

 

Spark: Reading and Writing to Parquet Format
————————————————————————–

– Using Spark Data Frame save capability
– Code/Approach works on both local HDD and in HDFS environments

Related video:
Introduction to Apache Spark and Parquet, https://www.youtube.com/watch?v=itm0T…

Code for demo

case class Person(name: String, age: Int, sex:String)
val data = Seq(Person(“Jack”, 25,”M”), Person(“Jill”, 25,”F”), Person(“Jess”, 24,”F”))
val df = data.toDF()

import org.apache.spark.sql.SaveMode
df.select(“name”, “age”, “sex”).write.mode(SaveMode.Append).format(“parquet”).save(“/tmp/person”)

df.select(“name”, “age”, “sex”).write.partitionBy(“sex”).mode(SaveMode.Append).format(“parquet”).save(“/tmp/person_partitioned/”)

val sqlContext = new org.apache.spark.sql.SQLContext(sc)
val dfPerson = sqlContext.read.parquet(“/tmp/person”)

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Using Spark and Hive – PART 1: Spark as ETL tool

Part 1: Scenario – Spark as ETL tool
Write to Parquet file using Spark

Part 2: SparkSQL to query data from Hive
Read Hive table data from Spark

Create an External Table
Query the data from Hive
Add new data
Query the data from Hive

case class Person(name: String, age: Int, sex:String)
val data = Seq(Person(“Jack”, 25,”M”), Person(“Jill”, 25,”F”), Person(“Jess”, 24,”F”))

val df = data.toDF()

import org.apache.spark.sql.SaveMode
df.select(“name”, “age”, “sex”).write.mode(SaveMode.Append).format(“parquet”).save(“/tmp/person”)

//Add new data
val data = Seq(Person(“John”, 25,”M”))
val df = data.toDF()
df.select(“name”, “age”, “sex”).write.mode(SaveMode.Append).format(“parquet”).save(“/tmp/person”)

CREATE EXTERNAL TABLE person ( name String, age Int, sex String)
STORED as PARQUET
LOCATION ‘/tmp/person’

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