3 Alternatives to MapReduce Programming
Early on in the race toward taming the beast that is Big Data, Hadoop became the go-to framework for storing and processing these enormous data sets. Since then, Hadoop has achieved an impressive adoption rate, though finding hard statistics on this is not easy. Most organizations prefer to keep their data analytics and other competitive endeavors hush-hush so as not to tip competitors of their ticket to success nor to alert competitors to any in-house struggles.
For a while, the programming behind most Hadoop operations was MapReduce. While this Java-based tool is powerful enough to chomp Big Data and flexible enough to allow for good progress doing so, the coding is anything other than easy. The most mundane operations require significant coding. Even with recent improvements, MapReduce still requires highly skilled Java programmers to do even the simplest of operations.
Fortunately, some of the big names in Big Data have also taken on Hadoop, and have backed their initiatives with other platforms for getting the programming done without massive teams of expensive, hard-to-find Java programmers. Enter Pig, Hive, and Spark.
MapReduce Alternative 1: Pig
The folks at Apache have had a porking good time naming components of Pig. PigLatin and Pig Engine are just two of the oink-inducing monikers.
Pig was originally a development by Yahoo!, where teams needed a language that could maximize productivity and accommodate a complex procedural data flow. Pig eventually became an Apache project, and has characteristics that resemble both scripting languages (like Python and Pearl) and SQL. In fact, many of the operations look like SQL: load, sort, aggregate, group, join, etc. It just isn’t as limited as SQL. Pig allows for input from multiple databases and output into a single data set.
MapReduce Alternative 2: Hive
From porkers to buzzers, the world of Hadoop is never lacking in creative names. But if MapReduce is stinging, Hive can sweeten it up like honey.
Hive also looks a lot like SQL at first glance. It accepts SQL-like statements and uses those statements to output Java MapReduce code. It requires little in the way of actual programming, so it’s a useful tool for teams that don’t have high-level Java skills or have fewer programmers with which to produce code. Initially developed by the folks at Facebook, Hive is now an Apache project.
MapReduce Alternative 3: Spark
Perhaps the most momentum has been achieved with Spark, which has widely been hailed as the end of MapReduce. Spark was born in the AMPLab at the University of California in Berkley.
Unlike Pig and Hive, which are merely programming interfaces for the execution framework, Spark replaces the execution framework of MapReduce entirely. One of the most celebrated qualities of Spark is that it’s super smart about memory and resource usage. It’s a solid general-purpose engine that allows you to run more Hadoop workloads and to run them faster. Spark also packs an impressive list of features, including stream processing, data transfer, fast fault recovery, optimized scheduling, and a lot more.
While each alternative to hand coding Java comes with pros and cons of its own, all are easier to manage than MapReduce, unless you are the proud owner of a team of Java experts. Of course, some organizations decide to leverage 3rd-party tools that help users to avoid hand coding all together. Syncsort’s DMX-h is one popular “no-coding” choice to simplify the entire data pipeline, whether you are using the MapReduce or Spark execution framework – because it runs on both.
Using its simple GUI, you can access data from across your enterprise (including hard to manage mainframe sources), bring it into Hadoop, and then leverage it – instead of Pig or Hive – for processing the data on the cluster. Organizations leveraging DMX-h say they are up and running faster – and can make changes quicker – compared to hand coding.
Check out Syncsort’s survey report, 2018 Big Data Trends: Liberate, Integrate & Trust, to see what every business needs to know in the upcoming year about Big Data, including 5 key trends to watch for in the next 12 months!