How big MNC’s manages and manipulates thousands of terabytes of data with high speed and high efficiency?

Let us see some of the current data related facts before getting into the topic.

Currently there are 4.13 billion internet user exists around the world, which is more than half the global population and this is only gonna increase every year and with this huge amount of data comes huge problems regarding how to handle + how o serve the data back to the users with great speed and efficiency.

Let us discuss about certain problems arises because of Big Data which are faced by big MNC’s

Data Quality Management.

Ensuring that data quality is maintained is not a mean feat. Usually, you need to analyze and collect data from different sources and in different formats. For instance, an online store would need to collect data from social media, website logs, competitor’s website scan, etc. The formats of these databases could be different which can make it difficult to connect them with each other.

Another challenge in terms of data quality is accuracy. This is because the raw data collected is not 100% accurate and suffers from issues such as contradictive values, duplicate values, etc. To remove such issues, you can compare the records with different sources to ensure data accuracy. You can also merge similar records so that there are no duplicates.

High Costs

Implementing big data solutions comes at a high cost. If you have the on-premises solution, you need to invest a lot in hardware, staff (developers, administrators, etc.), electricity, etc. Even though many big data frameworks are open source, you still need to pay for setting up and configuring these software applications. If you are deploying a cloud-based solution, then you also need to bear the costs of cloud services.

To minimize big data costs, you need to take a closer look at your requirements. For instance, if you want to deploy cloud-based big data services, then you can pick a hybrid solution in which you put some of the processes on the cloud and some inside the premises which is cost-effective. You can also reduce costs by optimizing algorithms as that leads to lower power consumption, or seek cheaper data storage options, etc.

Upscaling

One of the fundamental principles of a big data projects is that it grows considerably fast. This raises the challenge of how to upscale with the least effort and minimal costs. The actual problem is not the introduction of new processing and storing operations but rather the complication of scaling up. After all, you would want the infrastructure’s performance to stay consistent after upscaling while staying within the budget.

To handle upscaling properly, improve your big data architecture. Also, analyze the algorithms and see if they are future-ready for the upscaling. Lastly, try to perform systematic performance audits on a regular basis to identify weak spots and fix them.

How to choose the right Big Data Technology to solve the required problem?

It can be easy to get lost in the variety of big data technologies now available on the market. Do you need Spark or would the speeds of Hadoop MapReduce be enough? Is it better to store data in Cassandra or HBase? Finding the answers can be tricky. And it’s even easier to choose poorly, if you are exploring the ocean of technological opportunities without a clear view of what you need.

Let us discuss about two Big Data technologies and compare them to see which is better and for what aspects.

Hadoop

In brief, Apache Hadoop is a collection of open-source software utilities that facilitates using a network of many computers to solve problems involving massive amounts of data and computation. It provides a software framework for distributed storage and processing of big data using the MapReduce programming model.

Hadoop is mainly good for:-

• Linear processing of huge data sets. Hadoop MapReduce allows parallel processing of huge amounts of data. It breaks a large chunk into smaller ones to be processed separately on different data nodes and automatically gathers the results across the multiple nodes to return a single result. In case the resulting dataset is larger than available RAM, Hadoop MapReduce may outperform Spark.
• Economical solution, if no immediate results are expected. Our Hadoop team considers MapReduce a good solution if the speed of processing is not critical. For instance, if data processing can be done during night hours, it makes sense to consider using Hadoop MapReduce.

Spark

In brief, Apache Spark is an open-source distributed general-purpose cluster-computing framework. Spark provides an interface for programming entire clusters with implicit data parallelism and fault tolerance.

Spark is mainly good for:-

• Fast data processing. In-memory processing makes Spark faster than Hadoop MapReduce — up to 100 times for data in RAM and up to 10 times for data in storage.
• Iterative processing. If the task is to process data again and again — Spark defeats Hadoop MapReduce. Spark’s Resilient Distributed Datasets (RDDs) enable multiple map operations in memory, while Hadoop MapReduce has to write interim results to a disk.
• Near real-time processing. If a business needs immediate insights, then they should opt for Spark and its in-memory processing.
• Graph processing. Spark’s computational model is good for iterative computations that are typical in graph processing. And Apache Spark has GraphX — an API for graph computation.
• Machine learning. Spark has MLlib — a built-in machine learning library, while Hadoop needs a third-party to provide it. MLlib has out-of-the-box algorithms that also run in memory. But if required, our Spark specialists will tune and adjust them to tailor to your needs.
• Joining datasets. Due to its speed, Spark can create all combinations faster, though Hadoop may be better if joining of very large data sets that requires a lot of shuffling and sorting is needed.

Conclusion

It’s your particular business needs that should determine the choice of a framework. Linear processing of huge datasets is the advantage of Hadoop MapReduce, while Spark delivers fast performance, iterative processing, real-time analytics, graph processing, machine learning and more. In many cases Spark may outperform Hadoop MapReduce. The great news is the Spark is fully compatible with the Hadoop eco-system and works smoothly with Hadoop Distributed File System, Apache Hive, etc.

Thanks for reading …..