Streaming Big Data Analytics with Spark, Kafka and Cassandra


Analytics has become important for every business to run successfully. Understanding your customer’s requirement, predicting what they want at the right time has become more and more important. Time to react has become the key to success for many business lines, particularly for the e-commerce, automobile, healthcare, advertising industry and others. Data being gold for the business has to be mined as it comes in. Gone are the days where you mine data to find HISTORICAL INSIGHTS.
ACTION BASED INSIGHTS need to take place in real-time to make meaningful impacts.
We will explore a set of tools that have been successfully used across the industry to implement a system that helps business to ingest actions based on data in near real-time.
The present day business challenges:-
Huge volumes of data and a variety of data, making it difficult to process using traditional systems.
Large number of tools are available. Hadoop, Map-Reduce, Hive, Pig, Impala, Drill, SparkSql, Cassandra, MongoDB, Hbase, Spark, Storm, Spark Streaming, Flume ,kafka, Mahout, Mllib, Oryx and the list goes on. What should we choose from the above to build a successful platform?
Involves a huge talent pool to address / build the system as there are a lot of dependencies between different systems.
Making the analytics available on time using the right streaming framework.
Possible Solutions:
Using a technology stack which has a common platform and different abilities to solve my business problem, by providing a way to stream, process and analyze the data with a little learning curve for the developer teams, thus reducing the cost to the business.
Spark provides exactly what we want by providing a better map-reduce implementation, richer functionality, support for machine learning, streaming and ease to implement in three different and powerful languages, which are java, scala, python.
There is a tough challenge over the NOSQL area. Cassandra is a clear winner and has tight integration with apache spark.
Kafka perfectly fits today’s real world streaming applications by providing scalability, data partitioning and ability to handle a large number of diverse consumers, all which is currently used across various companies.
Sign-off Note:
We will be watching where these technology fits in to build a streaming model that resembles the architecture required to solve the above business problem.[Source]-https://www.happiestminds.com/blogs/streaming-big-data-analytics-with-spark-kafka-and-cassandra/
big data and hadoop training at Asterix Solution is designed to scale up from single servers to thousands of machines, each offering local computation and storage. With the rate at which memory cost decreased the processing speed of data never increased and hence loading the large set of data is still a big headache and here comes Hadoop as the solution for it.


Comments

Post a Comment

Popular posts from this blog

What Is Java? A Beginner’s Guide to Java and Its Evolution

What is Java used for? Before I answer the question, what is Java used for, let me brief you about why you should choose Java. Java is highly popular and has dominated this field from early 2000’s till the present 2018. Java has been used in different domains. Some of them are listed below: Banking: To deal with transaction management. Retail: Billing applications that you see in a store/restaurant are completely written in Java. Information Technology: Java is designed to solve implementation dependencies. Android: Applications are either written in Java or use Java API. Financial services: It is used in server-side applications. Stock market: To write algorithms as to which company they should invest in.   Big Data: Hadoop MapReduce framework is written using Java. Scientific and Research Community: To deal with huge amount of data. Wait! Java can do more. Let’s see how some of the technologies make use of Java as an essential core of their functionalit...
Read more

Is Data Scientist & Data Analyst are same? Learn the Differences Now!

Image
If we talk about the hottest jobs in tech right now, then only two names will pop up in your mind those are a data analyst or data scientist.  And, that’s not only we are saying even the Harvard Business Review declared “data scientist” as the“sexiest job of the 21st century .” But there is a lot of confusion between both the names even people who have some basic knowledge of data science consider both the job profiles as one.  Well, the reason behind this confusion is both of the professions deal with big data, rather defined vaguely and sometimes used interchangeably with one another. So, here we are to explain the differences between the two of them and that too in the easiest way. Let’s begin the brief introduction of both the terms, followed by skillsets each require to understand well.   Definition a. Data Analysts Data Analysts play a vital role in the Data Science domain. As a role, they play a variety of tasks associated with collectin...
Read more

Full Stack Development : All that you need to know

What is a Full Stack Development professional? A full stack development professional is the one who is having a sound technical knowledge of each and every aspect of development – front end, back end, various operating systems among other details of the technology. These people are usually termed as “developer generalists”. The significant thing to be noted here is that full stack developer should not be confused with senior developers. In nutshell, this stack of developers can create any complex application from the scratch, provided they would have understood how each technical layer should interact with the other. Why Full Stack Development? At the beginning of this post, we have mentioned about a query that had instantly popped in the mind – why would a company require full stack development professional. Here’s the answer: 1. Creation of unique code: The developer or the agency would be able to create a unique code for multiple technologies; as they could work with ...
Read more