Apache Spark and Hadoop: The best big data solution for enterprises


The term big data has become the center of attention for enterprises. In the past, business decisions have been made on the basis of transactional data stored in relational databases. This is known as traditional data, which is in structured form and easy to analyze for getting business insights.

Apart from this critical business data, there is a huge potential treasure stored in the non-traditional and unstructured data which enterprises are continuously producing in the form of weblogs, emails, sensor generated data, and social media channels. This data is only as useful as the decisions it enables. Enterprises are looking for the best data science solution with high speed velocity to capture, process, and analyze the unstructured data in real-time.

56% of Enterprises Will Increase Their Investment in Big Data over the Next Three Years – Forbes

Spark and Hadoop: The big data processing platform for enterprises

Hadoop and Spark are both big data frameworks used in the data science projects to extract useful insights. Both the frameworks are not mutually exclusive and they can work together. Hadoop is a parallel data processing platform that uses open source software, a distributed file system (HDFS), and MapReduce to store, manage, and process huge data sets. This is being deployed by the businesses for a long time. Most MapReduce jobs are long running batch jobs which take minutes or hours to complete.

But, now we see relatively less market adoption since Spark is available. It is a useful and reliable platform with flexibility, scalability, and affordability.

Let’s understand the big data first before determining the best framework for big businesses. Big data spans across the three dimensions which are volume, velocity, and variety.

Volume: Big businesses are flooded with ever-growing data of all types easily accumulating terabytes or petabytes of information. Enterprises need a speedy system to analyze this bulky data to process each day. This is where Spark has major advantages over Hadoop as it can process stored as well as streaming data in real-time.

Velocity: The huge amount of data enterprises are receiving needs to process with high velocity as sometimes even a delay of 5 minutes can be too late. For time sensitive processes such as fraud and robbery, the data must be used as it streams into your organization for gaining maximum value. This is where Spark can play a crucial role by processing bulk data with speed.

Variety: Big data consists of structured and unstructured data such as text, audio, video, sensor data, log files, click streams, etc. Useful insights can only be gained after analyzing these data types all-together. To make your business more agile, enterprises need to process all new and emerging data faster. By adopting Spark it is possible to answer all those questions which were previously beyond your reach.

Spark is capable of managing all the big data processing requirements with a variety of datasets. The other advantage of Spark over Hadoop is the relative ease of use and flexibility. With Spark it is possible to capture, store, process, and analyze unstructured data from various sources. Apache Spark is an open source cluster computing framework with in memory processing which can speed up analytical app processing up to100 times faster than the other data processing frameworks available. This is the reason which makes Spark the ultimate choice for the enterprises when speed is their preference.

Spark Ecosystem

Some businesses may not require data processing quickly or in real-time. Also, one must take a note that Spark does not include its own system for organizing files in a distributed style and that’s why it needs a system provided by any third party. It runs everywhere- Hadoop, Mesos, standalone, or in the cloud. It can access diverse data sources including Cassandra, HDFS, HBase, and S3. Similarly, Spark has its own machine learning library MLib, whereas a Hadoop system needs a third party machine learning library.

However both frameworks do not perform the same tasks and they are able to work together. Ultimately, we can say that both the data science frameworks can be preferred by enterprises depending on their data processing requirements and values gained from the big data.

By getting armed with the right tools for the right tasks enterprises can ignite a firestorm of activities in the present data scenario to gain competitive advantages by creating values. Also, enterprises can use multiple tools instead of relying on just one.

Let’s prioritize what you want to achieve from big data of your business. Based on your priorities we can come up with the best solution for getting useful insights for your enterprise.

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