What is Apache Spark?
Hadoop is used by organizations for data analytics for long. The main challenge with Hadoop is that it takes a long time to run queries over a large set of data. To address this problem UC Berkeley’s AMP Lab launched Apache Spark in 2009. Apache Spark is an open source engine for big data analytics. It is a cluster computing system designed for faster computing.
Understanding Apache Spark
Apache Spark is a general-purpose cluster computing framework. It was introduced by UC Berkeley’s AMP Lab in 2009 as a distributed computing system. But later maintained by Apache Software Foundation from 2013 till date. Spark is a lighting fast computing engine designed for faster processing of large size of data. It is based on Hadoop’s Map Reduce model. The main feature of Spark is its in-memory processing which makes computation faster. It has its own cluster management system and it uses Hadoop for storage purpose.
Spark supports batch application, iterative processing, interactive queries, and streaming data. It reduces the burden of managing separate tools for the respective workload.
How does Apache Spark make working so easy?
Spark is a powerful open source data processing engine. It is built to make big data processing easier and faster. It supports Java, Python, Scala, and SQL which gives the programmer the freedom to choose whichever language they are comfortable with and start development quickly. Spark is based on MapReduce but unlike MapReduce, it doesn’t shuffle data from one cluster to another, Spark has in-memory processing which makes it faster than MapReduce but still scalable. It can be used to build application libraries or perform analytics on big data. Spark supports lazy evaluation. This means it will first wait for the complete set of instructions and then process it. So, suppose if the user wants records filtered by date, but he wants only top 10 records. Spark will fetch only 10 records from the given filter rather the fetching all the records from the filter and then displaying 10 as the answer. This will save time as well as resources.
What can you do with Apache Spark?
With a spark, you can perform real-time stream data processing as well as batch processing. Apart from data processing spark supports complex machine learning algorithms. It can iterate through data faster. Spark has the following libraries to support multiple functionalities:
- MLlib is the library that provides machine learning capabilities to spark.
- GraphX is for Graph creation and processing.
- Spark SQL and Data frames library are for performing SQL operations on data.
- Spark stream library is for real-time streaming data processing.
Working with Apache Spark
Just like MapReduce spark works on distributed computing, it takes the code and Driver program creates a job and submits it to DAG Scheduler. DAG creates job graph and submits the job to Task Scheduler. Task Scheduler then runs the job through a cluster management system.
Spark uses master/slave architecture, the master coordinates and distributes the job and rest all distributed systems are slave worker. The master system is called “Driver”.
Apache Spark is a distributed computing system, so when starting with Apache Spark one should also have knowledge of how distributed processing works. Also, for using a spark in analytics, someone who is having knowledge of analytics can make the best out of it.
Top Apache Spark Companies
Below are a few top companies that are using Apache Spark:
- Alibaba Taobao
- eBay Inc.
- Hitachi Solutions
- IBM Almaden
- Nokia Solutions and Networks
- NTT DATA
- Simba Technologies
- Stanford Dawn
- Trip Advisor
Why should we use Apache Spark?
Spark is a distributed computing engine that can be used for real-time stream data processing. Although Hadoop was already there in the market for Big data processing, Spark has many improved features. Below are some of those features:
- Speed: Though spark is based on MapReduce, it is 10 times faster than Hadoop when it comes to big data processing.
- Usability: Spark supports multiple languages thus making it easier to work with.
- Sophisticated Analytics: Spark provides a complex algorithm for Big Data Analytics and Machine Learning.
- In-Memory Processing: Unlike Hadoop, Spark doesn’t move data in and out of the cluster.
- Lazy Evaluation: It means that spark waits for the code to complete and then process the instruction in the most efficient way possible.
- Fault Tolerance: Spark has improved fault tolerance than Hadoop. Both storage and computation can tolerate failure by backing up to another node.
Future is all about big data and spark provides a rich set of tools to handle real-time the large size of data. Its lighting fast speed, fault tolerance, and efficient in-memory processing make Spark a future technology.
Why do we need Apache Spark?
A spark is a one-stop tool for Real-time stream processing, batch processing, graph creation, machine learning, big data analytics. It supports SQL for querying the data. It is also compatible with Hadoop and other cloud providers like Amazon, Google Cloud, Microsoft Azure, etc. It has complex algorithms for big data analytics and supports iterative processing for Machine Learning.
Who is the right audience for learning Apache Spark technologies?
Anyone who wants to do some analytics on big data or machine learning can be the right audience for Apache Spark. It is the most suitable tool for real-time streaming data processing.
How this technology will help you in career growth?
Apache Spark is a next-generation technology. It is easy to work with given that it supports multiple languages. But learning spark can land you up in market best-paying jobs with top companies.
Apache Spark is next-generation technology for real-time stream data processing and big data processing. It’s easy to learn and gives scope for a great career.
This has been a guide to what is Apache Spark. Here we discussed the Career growth, Skills, and Advantages of the Apache Spark. You can also go through our other suggested articles to learn more –