Apache Spark is an open source, flexible in-memory framework designed for general data analytics on distributed computing clusters. It can handle batch and real-time analytics, and data processing workloads at much faster speed when compared to MapReduce.
Apache Spark runs on the existing Hadoop cluster, has access to HDFS, can also process structured data in Hive, and much more.
The developers of Apache Spark at Berkeley learned a lot from MapReduce like what made programming so hard and what were the performance challenges encountered by users. Taking all these into consideration, the team came up with an interesting open source project with a global community of developers that work hard on making that project better. Currently, there is a very clear shift in interest from MapReduce to Spark for performing general purpose workload on the platform.
An Overview of Apache Spark
When Apache Hadoop was first created it introduced two important innovations – number one was a scale-out storage system later known as HDFS, and the other was a new processing and analysis framework known as MapReduce.
HDFS could store any type of data in a cost-efficient, reliable manner. And the MapReduce engine allowed you to analysis huge amount of information from HDFS very efficiently and in massive parallel. But for certain general purpose workloads, MapReduce wasn’t the most efficient solution.
Apache Spark was the solution to this problem.
Want to Learn Apache Spark: Here we created a list of 10 Best Books For Learning Apache Spark
How Does Apache Spark Solve the Problems of MapReduce?
Coding MapReduce is really problematic. It requires a lot of java code and mastery of different APIs and abstractions. This makes it hard to build long, complex processing jobs in MapReduce. You have to do a bunch of work iteratively.
Apache Spark makes it much easier. The APIs are well designed; you don’t have to break your job into a bunch of little steps and weave them together. It also lets you describe the entire job and execute it in much better parallel than with MapReduce.
Here is a concise detail of all the tools that you get with Spark:
Apache Spark Ecosystem
The Apache Spark Ecosystem mainly consists of 5 key components:
Spark SQL and DataFrames
Spark SQL module helps in structured data processing and comes with a programming abstraction called DataFrames. It can also act as a distributed SQL query engine makes unmodified Hadoop Hive Queries run much faster.
Spark Stream allows powerful interactive and analytical applications across streaming data as well as historical data and at the same time inherits Spark’s ease of use and fault tolerance capabilities.
MLlib is scalable machine learning library that delivers both high-quality algorithms and is super fast. The entire library is usable in Java, Scala, and Python as a part of the Spark application.
GraphX is a graph computation engine which helps to interactively build, transform and reason about graph-structured data. It also comes with its own library of useful algorithms.
Spark Core API
Spark Core is the main general execution engine for the Spark Platform. This provides the in-memory computing capabilities to deliver speed, and a general execution model supporting a wide variety of applications. You will also get access to Java, Scala, Python, R and SQL APIs for making development easier.
Also Read: Top 11 Apache Spark Interview Questions
Spark was written in Scala, and it is the primary language you will need to interact with the Spark Core engine. However, it comes with API connectors for popular languages such as Java and Python. Now, as discussed Java is not the optimal language for data engineering or data science, and so users mainly rely on Python. There is also an R programming package which can be separately downloaded to help build applications that use machine learning algorithms.
What is Apache Spark Used for?
Apache Spark is currently used to handle most of the important production business problems in the World. Financial services sector are one of the biggest consumers of Sparks. For example, while looking for fraud in transaction flows you will want to continually retrain your model and update your model scores with the predictions of fraud in real time. Spark is essentially built for these type of use cases.
Again, it sees a lot of use in fields that require iterative processing. From finding out how much your portfolio is worth and handling simple value at risk analysis, Sparks is currently the best solution to these type of problems.
There is also workloads in many other vertical markets. In health service sectors, sparks can help in scoring and predict onset of disease and disease conditions in their patient populations.
In fact, it can be estimated that most of the new analytics and data processing workloads will find sparks to be most preferential. This is fairly evident seeing that most of the older tools like Hive, Crunch, Sqoop and so on migrating from MapReduce to Spark for the sole reason of ease of development.
5 Best Apache Spark Certification
Getting certified will entitle you to great salary packages, and also give you an edge over your peers in this competitive market.
So if you are thinking about getting yourself certified, we have listed 5 best Apache Spark Certification here:
Learning More About Apache Spark
By now you must have developed a clear understanding of Apache Spark, Its uses, and why it is basically becoming a Gold Standard of the Big Data Tools. If you want to see the framework in action you can go to this link – https://spark.apache.org/downloads.html, and get the first-hand experience. However, for detailed learning, we do encourage you to get a Certificate.
Whizlabs Big Data Certification courses – Spark Developer Certification (HDPCD) and HDP Certified Administrator (HDPCA) are based on the Hortonworks Data Platform, a market giant of Big Data platforms. Whizlabs recognizes that interacting with data and increasing its comprehensibility is the need of the hour and hence, we are proud to launch our Big Data Certifications. We have created state-of-the-art content that should aid data developers and administrators to gain a competitive edge over others.
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