In today’s data-driven world, organizations are increasingly turning to big data analytics to gain insights, make informed decisions, and stay competitive. Big data analytics refers to the process of examining large and complex datasets to uncover hidden patterns, correlations, and valuable information. It has applications across various industries, from healthcare to finance, marketing to manufacturing. In this comprehensive guide, we will explore the top seven big data analytics techniques that are transforming businesses and driving innovation.
1. Airflow
Airflow is a workflow management platform for scheduling and running complex data pipelines in big data systems. It enables data engineers and other users to ensure each task in a workflow is executed in the designated order and has access to the required system resources. Workflows are created in the Python programming language, and Airflow can be used for building machine learning models, transferring data and various other purposes.
The platform originated at Airbnb in late 2014 and was officially announced as an open source technology in mid-2015; it joined the Apache Software Foundation's incubator program the following year and became an Apache top-level project in 2019. Airflow also includes the following key features:
- A modular and scalable architecture built around the concept of directed acyclic graphs, which illustrate the dependencies between the different tasks in workflows.
- A web application UI to visualize data pipelines, monitor their production status and troubleshoot problems.
- Ready-made integrations with major cloud platforms and other third-party services.
2. Delta Lake
Databricks Inc., a software vendor founded by the creators of the Spark processing engine, developed Delta Lake and then open sourced the Spark-based technology in 2019 through the Linux Foundation. Delta Lake is a table storage layer that can be used to build a data lakehouse architecture combining elements of data lakes and data warehouses for both streaming and batch processing applications.
It's designed to sit on top of a data lake and create a single home for structured, semistructured and unstructured data, eliminating data silos that can stymie big data applications. Delta Lake supports ACID transactions that adhere to the principles of atomicity, consistency, isolation and durability. It also includes a liquid clustering capability to optimize how data is stored based on query patterns, as well as the following features:
- The ability to store data in an open Apache Parquet format.
- Uniform Format, or UniForm for short, a function that enables Delta Lake tables to be read in Iceberg and Hudi, two other Parquet-based table formats.
- Compatibility with Spark APIs.
3. Drill
The Apache Drill website describes it as a low-latency distributed query engine best suited for workloads that involve large sets of complex data with different types of records and fields. Drill can scale across thousands of cluster nodes and query petabytes of data through the use of SQL and standard connectivity APIs. It can handle a combination of structured, semistructured and nested data, the latter including things such as JSON and Parquet files.
Drill layers on top of multiple data sources, enabling users to query a wide range of data in different formats. That includes Hadoop sequence files and event logs, NoSQL databases, cloud object storage and various file types. Multiple files can be stored in a directory and queried as if they were a single entity.