Getting Started with Spark Scala

Apr 11, 20220 likes122 views

The exponential growth of data is not a problem but processing/managing the huge diversity of data is a concern. In this session, we are gonna discuss about one of the most popular big data distributed processing framework "Spark" where we'll be developing API using "Scala", a gained prominence amid big data developers.

Presented By:
Kuldeepak Gupta
Software Consultant
Getting Started with
Spark Scala

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Agenda
What, When & Why
Introduction to Apache Spark
01
Master-slave architecture
Spark Architecture
02
Situations where spark is helpful.
Use-cases for Spark
03
Components & API in Spark eco-system
Spark Eco-System
04
Spark Scala in Action
Demonstration
05

c
What is Spark
LEARN NOW
● A General Purpose Distributed Data Processing
Engine.
● One of the most popular big data distributed
processing framework.
● A multi-language engine for executing data
engineering, data science, and machine
learning on single-node machine.

c
Why Spark
LEARN NOW
● Supported Language (Java, Python, Scala, R)
● Support multiple languages and integrations
with other popular products.
● Oﬀers much less reading and writing to and
from the disk.

c
When Spark
LEARN NOW
● Implements a full server- and client-side HTTP
stack on top of akka-actor and akka-stream.
● Works with Distributed data (S3, XD, HDFS),
NoSQL databases (HBase, Cassandra,
MongoDB).
● Machine Learning and Fog Computing.

Master Slave Architecture
Well deﬁned layered architecture, components and layers are loosely coupled.
Cluster Manager
Spark Driver
● Control the execution of
Spark Application.
● Maintains all states of
Spark Cluster.
● Interface with Cluster
Manager.
Spark Executor
● Process that perform the
tasks assigned by the
Spark driver.
● Take the tasks assigned
by the driver, run them,
and report back their
state.
● Responsible for
maintaining a cluster of
machines that will run
your Spark Application.
● Have its own “Driver” and
“Worker” abstractions.

Ideal situation to use Spark
Batch and
Streaming
Supports
both batch
and real time
processing.
Big Data in
Cloud
Easy to setup
Spark with
Data lake
technologies
Finance
Industry
Analyse the
text inside the
regulatory
ﬁlling of their
own reports.
E-Commerce
Sector
Giants like
Ebay, Alibaba
uses Spark.

Components in Spark Ecosystem
SparkR
06
Spark Core
01
Spark SQL
02
Spark Streaming
03
Spark MLLib
04
Spark GraphX
05

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Toon is a leading brand in the European smart energy market, currently expanding internationally, providing energy usage insights, eco-friendly energy management and smart thermostat use for the connected home. As value added services become ever more relevant in this market, we have the need to ensure that we can easily and safely on-board new tenants into our data platform. In this talk we’re going to guide you across a less discussed side of using Spark in production – devops. We will speak about our journey from an on-premise cluster to a managed solution in the cloud. A lot of moving parts were involved: ETL flows, data sharing with 3rd parties and data migration to the new environment. Add to this the need to have a multi-tenant environment, revamp our toolset and deploy a live public facing service. It’s possible to find a lot of great examples of how Spark is used for data-science purposes. On the data engineering side, we need to deploy production services, ensure data is cleaned, secured and available, and keep the data-science teams happy. We’d like to share some of the options we took and some of the lessons learned from this (ongoing) transition.

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This document provides an overview of Apache Spark, including its history, features, architecture and use cases. Spark started in 2009 at UC Berkeley and was later adopted by the Apache Foundation. It provides faster processing than Hadoop by keeping data in memory. Spark supports batch, streaming and interactive processing on large datasets using its core abstraction called resilient distributed datasets (RDDs).

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Getting Started with Spark Scala

1. Presented By: Kuldeepak Gupta Software Consultant Getting Started with Spark Scala

2. Lack of etiquette and manners is a huge turn oﬀ. KnolX Etiquettes Punctuality Respect Knolx session timings, you are requested not to join sessions after a 5 minutes threshold post the session start time. Feedback Make sure to submit a constructive feedback for all sessions as it is very helpful for the presenter. Mute Please keep your window on mute Avoid Disturbance Avoid leaving your window unmuted after asking a question

3. Agenda What, When & Why Introduction to Apache Spark 01 Master-slave architecture Spark Architecture 02 Situations where spark is helpful. Use-cases for Spark 03 Components & API in Spark eco-system Spark Eco-System 04 Spark Scala in Action Demonstration 05

4. Introduction to Spark

5. c What is Spark LEARN NOW ● A General Purpose Distributed Data Processing Engine. ● One of the most popular big data distributed processing framework. ● A multi-language engine for executing data engineering, data science, and machine learning on single-node machine.

6. c Why Spark LEARN NOW ● Supported Language (Java, Python, Scala, R) ● Support multiple languages and integrations with other popular products. ● Oﬀers much less reading and writing to and from the disk.

7. c When Spark LEARN NOW ● Implements a full server- and client-side HTTP stack on top of akka-actor and akka-stream. ● Works with Distributed data (S3, XD, HDFS), NoSQL databases (HBase, Cassandra, MongoDB). ● Machine Learning and Fog Computing.

8. Spark Architecture

9. Master Slave Architecture Well deﬁned layered architecture, components and layers are loosely coupled. Cluster Manager Spark Driver ● Control the execution of Spark Application. ● Maintains all states of Spark Cluster. ● Interface with Cluster Manager. Spark Executor ● Process that perform the tasks assigned by the Spark driver. ● Take the tasks assigned by the driver, run them, and report back their state. ● Responsible for maintaining a cluster of machines that will run your Spark Application. ● Have its own “Driver” and “Worker” abstractions.

11. Use-Cases for Apache Spark

12. Ideal situation to use Spark Batch and Streaming Supports both batch and real time processing. Big Data in Cloud Easy to setup Spark with Data lake technologies Finance Industry Analyse the text inside the regulatory ﬁlling of their own reports. E-Commerce Sector Giants like Ebay, Alibaba uses Spark.

13. Spark Eco-System

14. Components in Spark Ecosystem SparkR 06 Spark Core 01 Spark SQL 02 Spark Streaming 03 Spark MLLib 04 Spark GraphX 05

15. Demonstration

16. Thank You !

Getting Started with Spark Scala

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