Spark Tutorial For Beginners | Big Data Spark Tutorial | Apache Spark Tutorial | Simplilearn

Summary

Apache Spark, an open-source cluster computing framework, was developed to overcome the limitations of Hadoop's MapReduce. It excels in real-time processing, trivial operations, and handling large data on networks, offering up to 100 times faster performance for certain applications. Spark's components, including Spark Core, RDDs, Spark SQL, Streaming, MLlib, and GraphX, provide a unified platform for diverse data processing tasks, from batch to real-time analytics, machine learning, and graph processing. Its in-memory processing capabilities and support for multiple languages enhance developer experience and enable versatile data analysis.

Takeaways

• 🚀 Apache Spark was developed at UC Berkeley's AMP Lab in 2009 and became an open-source project in 2010 under the Berkeley Software Distribution license.
• 🔄 In 2013, the project was donated to the Apache Software Foundation and the license was changed to Apache 2.0, with Spark becoming an Apache top-level project in 2014.
• 🏆 Databricks, founded by the creators of Apache Spark, used Spark to set a world record in large-scale sorting in November 2014 and now provides commercial support and certification for Spark.
• 🔍 Spark is a next-generation real-time and batch processing framework that can be compared with MapReduce, another data processing framework in Hadoop.
• 📈 Batch processing in Spark involves processing large amounts of data in a single run over a time period, typically used for heavy data load, generating reports, and managing data workflow offline.
• 🔥 Real-time processing in Spark occurs instantaneously on data entry or command receipt, with applications like fraud detection requiring stringent response time constraints.
• 🚧 The limitations of MapReduce, such as its suitability primarily for batch processing and not for real-time processing, lack of support for trivial operations, and issues with large data on the network, led to the creation of Spark.
• 💻 Spark is an open-source cluster computing framework that addresses the limitations of MapReduce, offering real-time processing, support for trivial operations, and efficient handling of large data on a network.
• 🌐 Spark's performance is significantly faster than MapReduce for certain applications, thanks to its in-memory processing capabilities, making it suitable for machine learning algorithms.
• 🛠️ A Spark project includes components like Spark Core and RDDs, Spark SQL, Spark Streaming, MLlib (Machine Learning Library), and GraphX, each serving different computational needs from basic I/O to advanced analytics.
• 🔑 In-memory processing in Spark allows for faster data access and improved performance, reducing the need for disk-based storage and enabling more efficient data compression and query execution.

Q & A

What was the original purpose behind the development of Spark?

Spark was developed at UC Berkeley's AMP Lab in 2009 to address the limitations of the MapReduce framework and to provide a more efficient data processing framework for both batch and real-time processing.

When did Spark become an open source project?

Spark became an open source project in 2010 under the Berkeley Software Distribution license.

What significant change occurred in 2013 regarding the Spark project?

In 2013, the project was donated to the Apache Software Foundation and its license was changed to Apache 2.0.

Why did Spark become an Apache Top-Level Project in February 2014?

Spark became an Apache Top-Level Project in February 2014 due to its growing popularity and the recognition of its capabilities in the big data processing domain.

What is the difference between batch processing and real-time processing as mentioned in the script?

Batch processing involves processing a large amount of data in a single run over a time period without manual intervention, typically used for offline data workflows like generating reports. Real-time processing, on the other hand, occurs instantaneously on data entry or command receipt and requires stringent response time constraints, such as in fraud detection.

What limitations of MapReduce did Spark aim to overcome?

Spark aimed to overcome limitations such as the slow processing time for large data sets, the complexity of writing trivial operations like filter and join, issues with large data on the network, unsuitability for online transaction processing (OLTP), and the inability to handle iterative program execution and graph processing efficiently.

What are the main components of a Spark project?

The main components of a Spark project include Spark Core and Resilient Distributed Data Sets (RDDs), Spark SQL, Spark Streaming, the Machine Learning Library (MLlib), and GraphX.

How does Spark Core and its RDDs simplify the complexity of programming?

Spark Core and RDDs simplify programming by providing basic input/output functionalities, distributed task dispatching, and scheduling. RDDs abstract the complexity by allowing data to be partitioned across machines and manipulated through transformations similar to local data collections.

What is Spark SQL and how does it support data manipulation?

Spark SQL is a component that resides on top of Spark Core, introducing Schema RDD, a new data abstraction that supports semi-structured and structured data. It allows data to be manipulated using domain-specific languages like Java, Scala, and Python and supports SQL queries through JDBC or ODBC interfaces.

How does Spark Streaming differ from traditional batch processing?

Spark Streaming leverages the fast scheduling capability of Spark Core for streaming analytics by ingesting data in small batches and performing RDD transformations on them. This design allows the same application code set written for batch analytics to be used for streaming analytics.

What advantages does Spark offer over MapReduce in terms of performance and versatility?

Spark offers up to 100 times faster performance for certain applications due to its in-memory processing capabilities, making it suitable for machine learning algorithms. It is also more versatile, being suitable for real-time processing, trivial operations, processing larger data on a network, OLTP, graphs, and iterative execution.

Outlines

00:00

🚀 Introduction to Apache Spark and Its Evolution

Apache Spark is a data processing framework that originated at UC Berkeley's AMP Lab in 2009. It was open-sourced in 2010 under the Berkeley Software Distribution license. In 2013, it was donated to the Apache Software Foundation, transitioning to the Apache 2.0 license. By February 2014, Spark had become an Apache top-level project. It is recognized for its ability to handle both real-time and batch processing, distinguishing it from MapReduce, which is limited to batch processing. Spark's creators used it to set a world record in large-scale sorting by November 2014. Now, it is supported commercially by Databricks, the company founded by Spark's creators, and is a next-generation framework that overcomes the limitations of MapReduce, such as its inability to handle real-time processing, complex operations, large data on the network, online transaction processing, and iterative program execution.

05:01

🌟 Core Components and Capabilities of Apache Spark

Apache Spark's core components include Spark Core and Resilient Distributed Datasets (RDDs), which form the foundation of the project by providing basic I/O functionalities, distributed task dispatching, and scheduling. RDDs are a fundamental programming abstraction, offering a simplified complexity of programming by allowing data manipulation similar to local data collections. Spark SQL introduces Schema RDD, a new data abstraction for semi-structured and structured data, supporting SQL and various domain-specific languages. Spark Streaming enables real-time streaming analytics by leveraging Spark Core's fast scheduling capability. MLlib, the machine learning library, applies common statistical and machine learning algorithms, and GraphX is a distributed graph processing framework that provides an API and runtime for graph computations. In-memory processing is highlighted as a key feature, allowing for faster performance and enabling Spark to handle large datasets efficiently.

10:02

🛠️ Spark's Advantages and Its Place in the Hadoop Ecosystem

Spark is favored over MapReduce for its performance and versatility, offering a rewarding development experience with support for multiple languages like Java, Scala, and Python. It simplifies the development process by allowing the use of lambda functions and closures. In contrast, the Hadoop ecosystem, which uses MapReduce for batch analytics, is limited to batch processing and requires extensive setup for different types of data processing. Spark, however, supports a variety of workloads, including streaming, iterative algorithms, and batch applications, all on the same engine. It also integrates with Hadoop by creating distributed datasets from files stored in Hadoop's file systems or other supported storage systems. The unification provided by Spark simplifies the learning curve for developers and allows for easy management of applications across different systems.

15:04

📈 The Impact of In-Memory Processing and Spark's Future Prospects

In-memory processing allows for faster data access and improved performance, which is crucial for interactive data exploration and analysis. Spark's in-memory capabilities provide speed and efficiency, making it suitable for complex applications and various processing types. It also supports the development of distributed applications that combine different processing models, such as real-time data categorization using machine learning. The IT team benefits from maintaining a single system, as Spark integrates tightly with various components for different workloads. For those aspiring to become big data experts, the Simply Learn channel offers educational content and certification opportunities in big data, highlighting the growing importance and demand for expertise in this field.

Keywords

💡Apache Spark

Apache Spark is an open-source cluster-computing framework that was developed to address the limitations of Hadoop's MapReduce. It is designed for both real-time and batch processing of large-scale data. In the video, Spark is highlighted for its ability to perform up to 100 times faster than MapReduce for certain applications, making it suitable for machine learning algorithms and other data-intensive tasks.

💡Batch Processing

Batch processing refers to the execution of a large number of operations or transactions in a single run over a period of time without any manual intervention. The video explains that in batch processing, data is pre-selected and fed using command line parameters and scripts, often used for generating reports and managing data workflows offline, such as creating daily or hourly reports for decision making.

💡Real-time Processing

Real-time processing is the instantaneous processing of data upon entry or command receipt, requiring stringent response time constraints. The video uses fraud detection as an example of real-time processing, highlighting its importance in scenarios where immediate data processing is crucial.

💡MapReduce

MapReduce is a programming model and an associated implementation for processing and generating large datasets. The video discusses the limitations of MapReduce, such as its suitability primarily for batch processing and its inability to handle real-time processing efficiently, as well as its complexity when dealing with trivial operations like filter and join.

💡Databricks

Databricks is a company founded by the creators of Apache Spark, which provides commercial support and certification for Spark. The video mentions that the engineering team at Databricks used Spark to set a world record in large-scale sorting, demonstrating the framework's capabilities.

💡Resilient Distributed Datasets (RDDs)

RDDs are the fundamental data structure in Spark, representing a fault-tolerant collection of elements that can be operated on in parallel. The video describes RDDs as being created by applying transformations on existing RDDs or by referencing external datasets, simplifying the complexity of programming by allowing operations similar to those on local data collections.

💡Spark SQL

Spark SQL is a Spark module that provides support for structured and semi-structured data, allowing users to run SQL queries or use the DataFrame API for data manipulation. The video explains that Spark SQL introduces Schema RDD, a new data abstraction that supports SQL with JDBC or ODBC interfaces.

💡Spark Streaming

Spark Streaming is a component of Spark that enables scalable, high-throughput, fault-tolerant stream processing of live data streams. The video describes how Spark Streaming leverages the fast scheduling capability of Spark Core for streaming analytics, allowing the same application code set written for batch analytics to be used for streaming analytics as well.

💡MLlib

MLlib is Spark's machine learning library, which provides a distributed machine learning framework. The video notes that MLlib is nine times faster than Apache Mahout's disk-based version due to its memory-based architecture and supports various common statistical and machine learning algorithms.

💡GraphX

GraphX is a component of Spark designed for graph processing, providing an API and optimized runtime for the Pregel abstraction, which is used for large-scale graph processing. The video explains that GraphX allows for the computation of graphs and can model the Pregel abstraction for graph processing tasks.

💡In-memory Processing

In-memory processing refers to the practice of loading data into the memory of a system for faster data access and processing. The video discusses the advantages of in-memory processing, such as increased speed of processing, reduced memory requirements for queries, and the ability to implement compression algorithms that decrease the in-memory size.

Highlights

Spark was developed at UC Berkeley's AMP Lab in 2009 and became an open-source project in 2010.

In 2013, Spark was donated to the Apache Software Foundation and its license changed to Apache 2.0.

Spark became an Apache Top-Level Project in February 2014.

Databricks, founded by Spark creators, used Spark to set a world record in large-scale sorting in 2014.

Spark supports both real-time and batch processing, unlike MapReduce which is limited to batch processing.

Batch processing in Spark is used for operations like generating reports and managing data workflows offline.

Real-time processing in Spark is instantaneous and crucial for applications like fraud detection.

MapReduce's limitations include its unsuitability for real-time processing and complex operations like filter and join.

Spark addresses MapReduce's limitations, offering superior performance for real-time processing and network data processing.

Spark provides up to 100 times faster performance in certain applications due to its in-memory processing capabilities.

Spark's components include Spark Core, RDDs, Spark SQL, Spark Streaming, MLlib, and GraphX.

RDDs are Spark's fundamental programming abstraction, simplifying distributed data processing.

Spark SQL introduces Schema RDD for structured and semi-structured data manipulation.

Spark Streaming enables the use of the same batch analytics code for streaming analytics.

MLlib is Spark's distributed machine learning framework, offering performance improvements over other frameworks.

GraphX is a distributed graph processing framework in Spark, providing APIs and runtime for graph computations.

In-memory processing in Spark allows for faster data access and reduced memory requirements compared to disk-based systems.

Spark's in-memory capabilities provide a significant speed advantage for machine learning algorithms.

Spark supports multiple development languages, including Java, Scala, and Python, enhancing developer experience.

Spark's lambda functions and closures allow for inline function definitions, simplifying code comprehension.

Spark can replace Hadoop's MapReduce for batch processing, offering speed and versatility advantages.

Spark's unification feature simplifies development by allowing the use of one platform for various processing types.

Spark's integration with Hadoop systems provides flexibility and support for various data storage formats.

Spark's performance and ease of use make it a preferred choice for big data processing over MapReduce.

Transcripts

[Music]

spark as a data processing framework was

developed at uc berkeley's amp lab by

mate

in 2009

in 2010 it became an open source project

under a berkeley software distribution

license in the year 2013 the project was

donated to the apache software

foundation and the license was changed

to apache 2.0

in february 2014

spark became an apache top level project

by november 2014

spark was used by the engineering team

at databricks a company founded by the

creators of apache spark

to set a world record in large scale

sorting

now databricks provides commercial

support and certification for taking the

spark programming test

at present spark exists as a next

generation real time and batch

processing framework

let's try to understand what batch and

real-time processing mean

we will use this information in the

subsequent slides to compare spark with

mapreduce both of which are data

processing framework in hadoop

in case of batch processing a large

amount of data or transactions are

processed in a single run over a time

period

the associated jobs generally run

entirely without any manual intervention

additionally the entire data is

pre-selected and fed using command line

parameters and scripts

in typical cases batch processing is

used to execute multiple operations

handle heavy data load

generate reports and manage data

workflow which is offline an example is

to create daily or hourly reports to aid

decision making on the other hand

real-time processing occurs

instantaneously on data entry or command

receipt

it needs to execute within stringent

response time constraints

an example is fraud detection

the need for spark was created by the

limitations of mapreduce which is

another data processing framework in

hadoop let's see what these limitations

are

mapreduce is suitable for batch

processing where data is processed as a

periodic job thus it takes time to

process data and provide results if the

data is high depending on the amount of

data and the number of nodes in the

cluster to complete a job

it just takes minutes to process the

data however it is not a good choice for

real-time processing

mapreduce is also not suitable for

writing trivial operations such as

filter and join

to write such operations you might need

to rewrite the jobs using the mapreduce

framework which becomes complex because

of the key value pattern

this pattern is required to be followed

in reducer and mapper codes

mapreduce doesn't work so well with

large data on the network

the reason is that it takes a lot of

time to copy the data which may cause

network bandwidth issues it works on the

data locality principle and hence works

well on the node where the data resides

mapreduce is also unsuitable for online

transaction processing or oltp which

includes a large number of short

transactions

since it works on the batch-oriented

framework it lacks latency of seconds or

sub-seconds

additionally mapreduce is unfit for

processing graphs

graphs represent the structures to

explore relationships between various

points

for example finding common friends in

social media sites like facebook hadoop

has the apache giraffe library for such

cases

it runs on top of mapreduce and adds to

the complexity another important

limitation is its unsuitability for

iterative program execution

some use cases like k-means need such

execution where data needs to be

processed again and again to refine

results

mapreduce runs from the start every time

as it is stateless executor

spark is an open source cluster

computing framework which addresses all

of the limitations of mapreduce

it is suitable for real-time processing

trivial operations and processing larger

data on a network

it is also suitable for oltp

graphs and iterative execution

compared to the disk based two-stage

mapreduce of hadoop

spark provides up to 100 times faster

performance for a few applications

within memory primitives

fast performance makes it suitable for

machine learning algorithms as it allows

programs to load data into the memory of

a cluster and create the data constantly

a spark project comprises various

components such as spark core and

resilient distributed data sets or rdds

spark sql spark streaming machine

learning library or ml lib and graphics

let's discuss the components of spark

the first component spark core and rdds

are the foundation of the entire spark

project

they provide basic input output

functionalities distributed task

dispatching and scheduling

let's look at rdd closely rdds are the

basic programming abstraction and is a

collection of data that is partitioned

across machines logically

rdds can be created by applying

coarse-grained transformations on the

existing rdds

or by referencing external data sets the

examples of these transformations are

reduce

join filter and map

the abstraction of rdds is exposed

similarly as in process and local

collections through a language

integrated application programming

interface or api

in python java and scala as a result of

the rdd abstraction the complexity of

programming is simplified as the manner

in which applications change rdds is

similar to changing local data

collections

the second component is spark sql which

resides on the top of spark core

it introduces schema rdd which is a new

data abstraction and supports

semi-structured and structured data

schema rdd can be manipulated in any of

the provided domain specific languages

such as java scala and python by the

spark sql spark sql also supports sql

with open database connectivity or java

database connectivity commonly known as

odbc or jdbc server and command line

interfaces

the third component is spark streaming

spark streaming leverages the fast

scheduling capability of spark core for

streaming analytics ingesting data in

small batches

and performing rdd transformations on

them

with this design the same application

code set written for batch analytics can

be used on a single engine for streaming

analytics

the fourth component of spark is machine

learning library

also known as ml lib

it lies on top of spark and is a

distributed machine learning framework

ml lib applies various common

statistical and machine learning

algorithms

with its memory-based architecture it is

nine times faster than the apache

mahouts hadoop disk based version

in addition the library performs even

better than valpal wabit or vw

the vw project is a fast out of core

learning system sponsored by microsoft

the last component graphics also lies on

the top of spark and is a distributed

graph processing framework

for the computation of graphs it

provides an api and an optimized runtime

for the pregal abstraction

pragel is a system for large-scale graph

processing

the api can also model the pragal

abstraction we discussed earlier that

spark provides up to 100 times faster

performance for a few applications

within memory primitives

let's discuss the application of

in-memory processing using

column-centric databases

in column-centric databases similar

information can be stored together and

hence data can be stored with more

compression and efficiency

it also permits the storage of large

amounts of data in the same space

thereby reducing the amount of memory

required for performing a query

it also increases the speed of

processing

in an in-memory database the entire

information is loaded into memory

eliminating the need for indices

aggregates

optimized databases star schemas and

cubes

with the use of in-memory tools

compression algorithms can be

implemented that decrease the in-memory

size even beyond what is required for

hard disks

users querying data loaded in memory is

different from caching

in memory processing also helps to avoid

performance bottlenecks and slow

database access

caching is a popular method for speeding

up the performance of a query where

caches are subsets of a very particular

organized data which are already defined

within memory tools

data analysis can be flexible in size

and can be accessed within seconds by

concurrent users with an excellent

analytics potential this is possible as

data lies completely in memory

in theoretical terms this leads to data

access improvement that is 10 000 to one

million times fast when compared to a

disc

in addition it reduces the performance

tuning needed by i t professionals and

therefore provides faster data access

for end users

with in-memory processing it is possible

to access visually rich dashboards and

existing data sources

this ability is provided by several

vendors

in turn in memory processing allows end

users and business analytics to create

customized queries and reports without

any need of extensive expertise or

training

we have already discussed that spark

provides performance which in turn

offers developers a rewarding experience

spark is chosen over mapreduce mainly

for its performance advantages and

versatility

apart from these another critical

advantage is its development experience

along with language flexibility

spark provides support to various

development languages like java scala

and python and will likely support r as

well

in addition spark has the capability to

define functions in line

with the temporary exception of java a

common element in these languages is

that they provide methods to express

operations using lambda functions and

closures

using lambda closures

you can use the application core logic

to define the functions inline which

helps to create easy to comprehend codes

and preserve application flow

let's look at mapreduce in the hadoop

ecosystem the hadoop ecosystem which

allows you to store large files on

various machines

uses mapreduce for batch analytics that

is as easy as it is distributed in

nature

on the other hand apache spark supports

both real-time and batch processing

in hadoop third-party support is also

available

for example by using etl talented tools

various batch oriented workflows can be

designed

in addition it supports pig and hive

queries that enable non-java developers

to use and prepare batch workflows using

sql scripts

you can perform every type of data

processing using spark that you can

execute in hadoop

for batch processing spark batch can be

used over hadoop mapreduce

for structured data analysis spark sql

can be implemented using sql

for machine learning analysis the

machine learning library can be used for

clustering recommendation and

classification

for interactive sql analysis spark sql

can be used instead of impala in

addition for real-time streaming data

analysis spark streaming can be used in

place of a specialized library like

storm

spark has three main advantages which

are

provide speed capability

combines a various processing types

and supports hadoop

the feature of speed is critical to

process large data sets as this implies

the difference of waiting for hours or

minutes and exploring the data

interactively

spark has extended the mapreduce model

to support computations like stream

processing and interactive queries

supporting run computations in memory

with respect to speed

also its related system is more

effective when compared to mapreduce to

run complex applications on a disk

this adds to the speed capability of

spark

spark covers various workloads that

require different distributed systems

such as streaming iterative algorithms

and batch applications

as these workloads are supported on the

same engine combining different

processing types is easy

spark is normally required in the

production of data analysis pipelines

the combination feature

also allows easy management of separate

tools

spark is capable of creating distributed

data sets from any file that is stored

in the hadoop distributed file system or

any other supported storage systems

you must note that spark does not need

hadoop

it just supports the storage systems

that implement the apis of hadoop and

sequence files parquet avro text files

and all other input output formats of

hadoop

now the question is why unification

matters

unification not only provides developers

with the advantage of learning only one

platform but also allows users to take

their apps everywhere

the graphic shows the apps and the

systems that can be combined with spark

a spark project includes various closely

integrated components for distributing

scheduling and monitoring applications

with many computational tasks across a

computing cluster or various worker

machines

the spark core engine is general purpose

and fast

as a result it empowers various higher

level components that are specialized

for different workloads like machine

learning or sql these components can

inter-operate closely

another important advantage is that it

integrates tightly allowing you to

create applications that easily combine

different processing models

an example is the ability to write an

application using machine learning to

categorize data in real time as it is

ingested from sources of streaming

additionally it allows analysts to query

the data which results through sql

moreover data scientists and engineers

can access the same data through the

python shell for ad-hoc analysis and in

standalone batch applications

for all this the it team needs to

maintain one system only

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