Big Data is one of the most currently demanded niches in the development and supplement of enterprise software. The high popularity of the Big Data technology is a socially-technological phenomenon provoked by the rapid and constant growth of the volumes of information. In order to solve business tasks, huge data massives must be reviewed, structured, and processed. The point here is that if you want to become a good developer with ambitious employment prospects, you need to get familiar with at least a couple of Big Data processing frameworks. How to select the most relevant in 2019 option to consider?
Top Big Data Frameworks: What Will Companies Choose In 2019?
We have conducted a thorough market analysis to compose this list of the best Big Data frameworks to most definitely be popular among the trendiest projects of 2019. Take a look:
- Apache Hadoop. Hadoop is a project with an open source code managed by the Apache Software Foundation. Hadoop is used for reliable, scalable, distributed calculations, but it can also be exploited as common-purpose file storage that can store petabytes of data. The solution consists of two key components: HDFS responsible for the storage of data in the Hadoop cluster; and MapReduce system intended to calculate and process large volumes of data in the cluster. How does exactly Hadoop help to solve the memory issue of modern DBMSs? Hadoop used as an intermediary layer between an interactive database and data storage increases the speed of data processing performance grows in accordance with the increase of data storage space. In order to grow it further, you can simply add new nodes to the data storage. Generally speaking, Hadoop can store and process many petabytes of info. On the other hand, the fastest processes in Hadoop still take a few seconds to operate. It also forbids any customization of the data already stored in the HDFS system. Last but not least, the solution supports transactions. So, despite this solution’s definite popularity among user for years to come, new, more advanced alternatives are gradually coming to the market to take its place (we will discuss some of the below).
- Apache Spark. Our list of best Big Data frameworks is continued with Apache Spark. It is an open-source framework created as a more advanced solution compared to Apache Hadoop – the initial framework built specifically for working with Big Data. The main difference between these two solutions is a data retrieval model. Hadoop saves data to the hard drive along each step of the MapReduce algorithm, while Spark implements all operations using the random-access memory. Due to this, Spark is about 100 times faster in performance and it allows processing data flows. The functional pillars and main features of Spark are high performance and fail-safety. It supports four languages: Scala, Java, Python, and R; and consists of five components: the core and four libraries that optimize the work with Big Data in various ways when combined. Spark SQL – one of the four dedicated framework’s libraries – serves for the structured data processing using DataFrames and solving of Hadoop Hive requests up to 100 times faster. Spark also features the Streaming tool for the processing of the thread-specific data in real time. Thus, Spark founders state that an average time of processing each micro-batch comprises only 0,5 seconds. Next, there is MLib – a distributed machine learning system nine times faster than the Apache Mahout library. And the last library is GraphX used for the scalable processing of graph data.
- Apache Hive. Apache Hive was created by Facebook to combine the scalability of one of the most popular and demanded big data tools MapReduce and the accessibility of SQL. Hive is, basically, an engine that turns SQL-requests into chains of map-reduce tasks. The engine includes such components as Parser (that sorts the incoming SQL-requests), Optimizer (that optimizes the requests for more efficiency), and Executor (that launches tasks in the MapReduce framework). Hive can be integrated with Hadoop (as a server part) for analysis of large data volumes.
- MapReduce. MapReduce is an algorithm for the parallel processing of large raw data volumes that was introduced by Google back in 2004. MapReduce sees data as kind of entries that can be processed in three stages: Map (pre-processing and filtration of data), Shuffle (worker nodes sort data – each worker node corresponds with one output key resulting from the map function), and Reduce (the reduce function is set by user and defines the final result for separate groups of output data. The majority of all values returned by the reduce() function are the final result of the MapReduce-task). Due to such simple logic, MapReduce provides the automated paralleling of data, efficient balancing of worker node stress-load, and fail-safe performance.
- Apache Storm. Apache Storm is another prominent solution focused on working with large data flow in real time. The key features of Storm are scalability (processing tasks are distributed by cluster nodes and flows in each node) and prompt restoring ability after downtime (thus, tasks are being redirected to other worker nodes if one of the nodes is down). You can work with this solution with the help of Java, as well as Python, Ruby, and Fancy. Strom features a number of elements that make it significantly different from analogs. The first one is Tuple – a key data representation element that supports serialization. Then there is Stream that includes the scheme of naming fields in the Tuple. Spout receives data from external sources, forms the Tuple out of them, and sends them to the Stream. There are also Bolt – data processor, and Topology – a package of elements with the description of their interrelation an analog of MapReduce job in Hadoop, basically). When combined, all of these elements help developers to easily manage large flows of unstructured data.
To summarize it all up, we can say that there cannot be a single best option among the listed data processing frameworks. Each has its pros and cons and the workflow provided by some solutions is strictly a subjective matter. If we research a bunch of dozens of positions descriptions, though, we can see that command in Spark is the most frequent requirement for the respective job candidates. This indicates that beginners should probably start their Big Data software development path from mastering exactly this advanced tool. Got any questions left? Perhaps, you know yet another Big Data framework solution that you believe should have been highlighted in the top list? Share your opinion in the comments!