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Big Data Tools

Companies around the globe have begun to value the potential of their data. Revenues from Big Data analytics is expected to reach $274.3 billion by 2024, according to a report from the IDC. IT services and business services are projected to account for half of all revenues.

Many companies are embarking on data science initiatives to develop innovative ways of leveraging value. That’s why big data tools have become a necessity and why data engineering has become one of the most in-demand IT disciplines today.

The Role of Data Engineering

Data engineers build the information infrastructure required for data science projects. At its core, a data engineer's mission is to design and manage data flows in support of analytical initiatives.

The challenge is in developing a data flow that integrates information from a variety of sources into a data warehouse or other common destination. From there, data scientists can analyze the information using Big Data tools.

Often, data engineers utilize data ingestion tools and implement data pipelines following the ETL (Extract, Transform, and Load) model.

Data engineers depend upon a wide range of programming and data management tools for implementing ETL, managing relational and non-relational databases, and building data warehouses.

To execute the Big Data concept, data scientists and data engineers must leverage the right tools to complement their data platforms or systems.

Some Popular Big Data Tools

  • Apache Spark is a data processing platform which, unlike MapReduce, can be used for real-time stream processing as well as batch processing. It is up to 100 times faster than MapReduce. One of the top Hadoop alternatives, Spark features APIs for Python, Java, Scala, and R, and can run as a stand-alone platform independent of Hadoop.
  • SQL and NoSQL (relational and non-relational databases) are foundational tools for data engineering applications. Traditionally, relational databases such as DB2 or Oracle have been the standard. But with modern applications increasingly handling massive amounts of unstructured, semi-structured, and even polymorphic data in real time, non-relational databases are now coming into their own.
  • Python is a very popular general purpose language. Widely used for statistical analysis tasks, it could be called the lingua franca of data science. According to a recent Cloud Academy survey, fluency in Python is the #1 desired skill for data engineers.
  • Qubole is a cloud-based data platform that provides Big Data as a Service. Users can focus on their data rather than infrastructure wrangling. Qubole provides a foundation for developing and deploying AI and machine learning models.

Big Data tools cover data storage and management, data cleaning, data mining, data analysis, data visualization, and data integration, and can overlap with data software.

SNOWFLAKE AND BIG DATA

Snowflake, the data warehouse built for the cloud, supports modern data and applications. Snowflake provides the platform that data scientists can rely on for analytical initiatives.

Big Data tools are a separator in the technology arms race. Explore the many ways Snowflake can improve your company's bottom line.

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