Hadoop’s development from a batch-oriented, large-scale analytics tool to an entire ecosystem comprised of various application, tools, services, and vendors goes hand in hand with the rise of big data marketplace. It is predominantly used for large scale data analysis by companies such as Facebook and eBay and although Hadoop is considered to be synonymous with big data analysis, it’s far from being the only suitable option. Although Hadoop might be an adequate tool for large data sets, it is far inferior to SQL when it comes to expressing computations.
Additionally, Hadoop does not offer a viable indexing option and only offers full table scans. Not to mention it’s the tendency for leaky abstractions including cluster contention, file fragmentation, and java memory errors. Analyzing large data sets is a piece of cake for Hadoop, but when it comes to streaming calculations in real time, it falls a bit short. Fortunately, there are solutions available that deal with this particular issue, including Apache’s very own Spark and Storm, Google’s BigQuery and DataTorrent’s RTS tools, to name a few.
1. Apache Spark
Hailed as the de-facto successor to the already popular Hadoop, Apache Spark is used as a computational engine for Hadoop data. Unlike Hadoop, Spark provides an increase in computational speed and offers full support for the various applications that the tool offers.
Although Spark is normally associated with various Hadoop implementations, it can actually be used with various other data stores. Not only does Spark not have to rely on Hadoop, but it can also be run as a completely independent tool.
Numerous companies, including IBM, have started to align their analytics around Spark. It offers flexibility with different data stores that is useful and more importantly, practical when compared to Hadoop.
This is reflected particularly well in the fact that Spark is an open-source platform which offers data processing capabilities in real time at speeds 100 times faster than what Hadoop’s MapReduce tools can currently offer, which makes it a perfect option for machine learning based processing. It can be executed on virtually any platform, including Apache Mesos, EC2 and Hadoop, either from a cloud or an independent cluster mode.
2. Apache Storm
Apache Storm is another excellent, open-source tool used for processing large quantities of analytics data. Whereas Hadoop can only process data in batches, Storm can do it in real time. The biggest difference between Hadoop and Apache Storm is the way it handles and distributes data.
Hadoop enters the data in an HDFS file system which is later distributed through nodes in order to get processed. Once this task is accomplished, the information is pulled back to HDFS and can finally be used.
Storm, on the other hand, is a system where processes don’t have a clear beginning and ending. This type of data processing is based largely on the Big Data topology construction, which is later transformed and further analyzed using a continuous stream of various information entries. This makes Storm a system which can be used for CEP, or complex event processing.
Apache Storm can be viewed as a useful solution for companies which allows them to react appropriately to both continuous and sudden influxes of data. Let’s not forget that Apache Storm is fault-tolerant, fully scalable and extremely easy to set up and operate.
Apache storm was developed in Clojure, a Lisp dialect which is normally executed using a JVM or Java Virtual Machine. This is one of its greatest strengths, as it offers compatibility with applications and various components written in programming languages such as C#, Java, Python, Perl, PHP and Scala.
Since its being made by Apache, Storm is also compatible with Flink, which can be used as a support for state management and event-time processing. This means that software development has more room for flexibility when compared to other frameworks which aren’t nearly as versatile.
3. Google BigQuery
Google’s BigQuery is a fully-fledged platform used for big data analysis. It allows its users to use SQL without being bothered with database or infrastructure management. The web service relies heavily on Google storage in order to provide users with an interactive analysis of large sets of data.
This means that you don’t have to invest in additional hardware which would otherwise be needed to process such large quantities of data. Its data mining algorithms are extremely useful for discovering specific user-behavior patterns in the raw data which are normally very difficult to discern using standard reporting.
What makes BigQuery such as strong contender against Hadoop is the fact that it works very well with the MapReduce tool. Not to mention that Google has a rather proactive approach when it comes to improving the existing features and adding completely new ones, all in order to provide its users with a superior data analysis tool. This is particularly evident in their efforts to make importing custom data sets and using it with services such as Google Analytics a walk in the park.
4. DataTorrent RTS
Another open-source solution for the analysis and processing of big data, both in batches and in real-time. This all-in-one tool which is designed to completely revolutionize the workings of Hadoop’s MapReduce environment and further improve the performance offered by Apache’s Spark and Storm tools.
DataTorrent RTS can process billions of individual events every second and recover node outages without losing data and without human intervention. It is completely scalable, easy to execute and offers guaranteed event processing and significantly higher in-memory performance.
5. Hydra
The last addition to our Hadoop alternatives list is Hydra, a task processing system which provides its users with real-time analytics. This system was created out of the necessity for a scalable distributing solution and is currently being licensed by Apache.
Hydra’s tree-based configuration allows it to easily perform both batch and streaming operations by storing and processing user data across multiple clusters, some of which could potentially have thousands of individual nodes. It features a management system which automatically distributes new jobs between clusters, balances out all the existing jobs, replicates data and even handles node failures.
With all tremendous power and numerous benefits, Hadoop has to offer, it still has some significant drawbacks. Its data distribution process is far too complex and it lacks efficiency when processing both unstructured and big data. Fortunately, there are some available alternatives, some of which offer significant speed increase and utilize hardware more efficiently by relying on advanced streaming operations. Make sure to thoroughly test them out before deciding on one tool that best meets your specific requirements.
