It was 1965, and a gentleman named Gordon Moore predicted that the number of transistors embedded in an IC would double, roughly every couple of years. Technology stood the best testament to it, and we saw Moore’s law materialize. Little did we know the law also holds for the consequence of this humongous development in the electronics world- this consequence being DATA. With the advent of such technologies, data piled up like never before to account for unimaginable highs. Below are some mind-tickling facts to suggest how huge data we have subscribed to,
- A 2020 research says that 1.7 Mb data was created by every person every second
- In the year 2020, 306.4 billion emails were sent per day
- According to Statista, the below graph shows the volume of data created each year since 2010
90% of the total data in the world has already been created in the last couple of years, making it an exponential increase.
Data in its raw form is inexplicable. The demand for tools to process these primary nonclinical data has never been more. As they say, necessity is the mother of invention. Thus, with the ever-increasing volume of data, umpteen resources have come up to make the raw data useful to us. Processed data is of immense benefit in data science, data analytics, machine learning and many more avenues. The tools have varying degrees of capabilities and utilities to provide the user with granular insights and trends.
The numerous resources have left end users spoilt for options. As we lay our eyes on the best car in the market, we do the same while going for a data processing tool. But, the best is subjective to only used cases. A needle is the best tool to stitch and a sword to fight battles. There might not be the one best library, but below are some of the most trusted and used libraries, in brief:
Apache Spark is an open-source framework used to log, process, stream and visualize data. It is self-sufficient and can run as standalone or in clusters. It works with environments like Java, Python, and Scala. It also has Yarn, Hadoop, Oozie, and Azkaban support at its disposal for scheduling. The distributed model of Spark is scalable and preferred for parallel computing of large volumes of data. It flexes ML extensions such as MLlib, XGBoost, and SQL compilers like Spark SQL and Hive SQL.
Dask is an open-source library used to read and visualize data. It works in a Python-based environment and boasts high scalability. It comes with the support of Yarn, Kubernetes, Luigi and Airflow for job and workflow scheduling. Dask uses machine learning algorithms like Dask-ml, Scikit-learn, and deep learning algorithms like Keras and TensorFlow. It does not have any SQL compilers.
Rapids is an open-source library that performs entirely on GPUs. Its USP is its sound performance because of these GPUs. Rapids GPU version of machine learning algorithms is cuDF, cuML, cuGraph and NetworkX. It works with Python and C/C++. Python is the most preferred environment because of its ease of use and enhanced performance.
There are numerous other tools to process and visualize data like Vaex, Apache Storm, Apache Flink, Apache Flume and FlinkML, to name a few.
Data collection was never a cause to address before as data processing and saving have been, since the last couple of years. Data in its raw form is an ore that needs polishing to evolve into a mineral and safe storing to become much more beneficial. Therefore, it calls for a proper means to store, process and visualize the data, and the above libraries and frameworks are amongst the best in business for this. But the brief introduction hardly justifies their capabilities. Thus, with heavy-duty arms at disposal, let’s start playing around with the omnipresent data without breaking the bank in the upcoming articles.