With Hadoop rising by leaps and bounds on the popularity charts, and MapReduce seemingly running out of friends, business owners and corporate managers are now looking for innovative tools for giving back quicker solutions. Recent trends are tracking important new developments in the maturing NoSQL space and Hadoop stack.. and lots more.
Read on for a closer look at some popular open source big data tools that are making their presence felt in the ever increasing world of big data.
The two very important elements of all scientific work, including the processes related to data science, are the validation and sharing of conclusions. IPython's Notebooks offer a progressive environment where users and researchers can automate and document their data analysis workflow to perfection. In this case, the notebook acts as a single point solution wherein researchers can share codes, documentations, ideas, as well as data visualizations; along with accessing the same from a well-defined, browser-based environment. Overall, IPython is a lot more than simple IPython Notebooks and includes parallel computing capabilities, multiple language support linked with data pipelines, and so forth (also consider checking out this perfect parcel of information for data science degree).
Pandas is basically a Python domain-specific language that is useful for effective manipulation and analysis of tabular data. With its origin in the ever-growing hedge fund industry, Pandas emphasizes ease of use and high performance. Even though it is relatively new to the scene, this open source big data tool boasts of a growing and large community. It is similar in many respects to the R language and comes in handy for data wrangling tasks. Pandas development incorporates several packages, the best ideas of R, and has an interesting future on is cards.
RCloud, accredited to AT&T Labs, was originally created for addressing the needs for a robust and collaborative data analysis environment linked to R. RCloud is quite similar to IPython and allows researchers to look into large data sets and share real-time information across organizations. Conceptually speaking, RCloud emulates Comprehensive R Archive Network package, and is skillfully augmented by certain collaboration features (wiki-like). Its codes and Notebooks are stored in GitHub. Even though RCloud offers little documentation and resources outside of AT&T, it presents a lot of promise.
Designed as a specialized computer language to take care of statistical analysis, R Project has been consistently advancing to meet all challenges of big data. After having displaced lisp-stat, R serves as the de-facto and reliable statistical processing language. It has numerous high-quality algorithms to its credit and they can be accessed from Comprehensive R Archive Network (CRAN)—a healthy ecosystem and vibrant community of supporting IDEs and tools. Project R’s 3.0 release eliminates the previous memory limitations of the language, with 64-bit builds allocating the optimum levels of RAM allowed by the host operating system.
Earlier, R laid focus on solving problems which were compatible with the local RAM and utilized multiple cores; however, with big data coming to the fore, several options are being used for the processing of larger volume data sets. Typically, these options include those packages that are installed into standard R environments or are capable of being integrated into large sized data systems like Spark (RHive and SparkR) and Hadoop (also consider checking out this career guide for data science jobs).
In recent memory, no other technology has managed to make as quick or big an impact on the contemporary scene as Hadoop. It encompasses topics like Pig, YARN, MapReduce, HDFS, HBase, Hive as well as a growing ecosystem of engines and tools. Whether you are attempting to offload ETL processing, mine data for other uses, spark information sharing, or replace expensive and redundant data warehousing technology, Hadoop is the platform that you should essentially look at today.
GridGain, HPCC, Storm, MongoDB, HBase, Hive, Hivemall, Cassandra, Neo4j, CouchDB, OrientDB, Terrastore, FlockDB, Hibari, Hypertable, Riak........ The list of open source big data tools is indeed endless!