Top 3 Big Data Frameworks to Choose From

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Through the years, Artificial intelligence (AI) has progressed to such a level that many of the expectations from years back have already been met. Big data is today's AI, and it becomes more relevant each day as businesses, organizations, and even individuals continue to gather, create, and process huge amounts of data. Big data is such a complex monster that traditional data processing tools have all but become obsolete.

Big data is so prevalent that many question to what extent companies are gathering and processing data. A number of forward-thinking businesses rely on big data for a variety of reasons, and the way they use it depends on many contextual factors. In order to use gathered data, however, they must be abstracted to a form that can be processed by available big data processing frameworks, the most common of which are discussed below.

Hadoop

A classic in its own right, Hadoop is one of the top big data frameworks in use today. It enjoys wide adoption today due to its reputation as being the first ever big data framework ever released, now having its ecosystem of tools that include Flume, Pig, Hive, and HDFS. Despite the slew of tools that work with it, Hadoop is arguably the simplest of all the frameworks. It will work for you if you process data in a logical manner, e.g., your data is processed in batch, split into smaller jobs, spread across a cluster, and all these tasks recombined.

Additionally, there are Hadoop tools that go beyond the MapReduce algorithm, which is what Hadoop started with. The most notable of these tools is YARN, the Apache Hadoop ecosystem resource management layer. Systems beyond Hadoop itself can use it, including Apache Spark, which is discussed in the next section.

Spark

When it comes to big data, Spark is considered one of the biggest names together with Hadoop. Commonly used as a replacement for the MapReduce paradigm, Spark speeds up processing times by working in-memory. It also works around the limitations of the imposed linear data flow of the MapReduce engine, which helps in the construction of a more flexible pipeline. The Spark framework is a good option when you require tightly integrated machine learning, MLib, and Spark's machine learning library. You can also exploit Spark's architecture for distributed modeling, so that's another thing to consider.

Spark is commonly considered a competitor to Hadoop, but this perspective completely misses the greater picture. In practice, the Hadoop ecosystem can actually work in conjunction with the Spark processing engine to completely replace MapReduce. This setup allows for a multitude of environments and tool combinations not possible by using Hadoop or Spark alone. Spark also doesn't come with a distributed storage layer by default and, as such, can make use of the Hadoop Distributed File System (HDFS). Using Spark and Hadoop together frees you from the limitations of the aging MapReduce paradigm and provides the advantage of new technologies and quicker data processing.

Storm

The Storm data framework is specifically designed to handle unbounded streams, with applications built as directed acyclic graphs. It's a distributed real-time computation system that can be used with any programming language. It's the framework of choice when it comes to scalability, processing job guarantees, and data processing speed. It has been known to process over one million 100-byte messages per second per node.

Storm is useful for real-time analytics, distributed machine learning, and other cases of high data velocity. A reliable framework, it's also fault-tolerant, automatically restarting workers when they die. Workers are restarted on another node if a node dies. Storm also guarantees that data is processed at least once, with messages only replayed in case of failures. It's also easy to operate but doesn't natively support state management.

Storm is a good choice if you're looking for something that's almost instantly ready once deployed and provides parallel calculations that run across machine clusters.

As seen above, comparing frameworks is like comparing apples to oranges; they all have different systems and capabilities that can benefit a variety of use cases. They are not mutually exclusive, and combining them to address an organization's system requirements is at times the practical option. The grandfather of big data processing Hadoop is a great example-its popular component YARN has been adopted by numerous applications through the years, and its adoption has been done by systems beyond the Hadoop-only ecosystem.

There are no cut-and-dried rules when it comes to choosing a data processing framework. All one can do is provide suggestions or guidelines, and even so, it will ultimately still depend on the context in which you use big data.