Big Data: Go Big or Go Home

Enterprise giants such as Google or Microsoft generate data on numerous fronts. They deal with website user traffic and image and video uploads on cloud platforms. They also help stream audio and video feeds during live broadcasts. All this information is a nightmare to manage for traditional databases, which is why engineers came up with the Big Data infrastructure. Big data allows users to dump all of their data into a central Data lake. This is done using various tools, different pipelines, and multi-node computing techniques, but more on these later. Big data is a relatively newer concept defined by the following concepts: Volume: Data is generated in Terabytes every day. Big data concepts specialize in dealing with such bulk quantities. Velocity: The use of IoT devices is increasing exponentially. With more devices and hence more users, people interact online every second, which means data generates every second. Big data infrastructures utilize robust ETL pipelines that are purpose-built to handle such cases. Variety: Data varies in types, including structured, semi-structured, and unstructured information. Veracity: With multiple data sources and the volume and velocity of generation, it is vital to ensure the correctness of data and maintain its quality throughout the lifecycle. Big data requires special care for correctness and quality because if erroneous data enters the lake, it becomes challenging to debug. Variability: Data can be used for several applications, such as user traffic analytics, churn rate prediction, or sentiment analysis. The general rule is that if your business requirements and data state fulfill the above conditions, you need a different ETL pattern. Many cloud providers, including AWS and Azure, offer services for building data lakes. These services provide a smooth setup and an easy-to-manage interface; however, Apache Hadoop remains the most common data lake-building tool. Apache Hadoop is a Data lake architecture that comes bundled with many tools that help with Data Ingestion, Processing, Dashboarding, Analytics, and Machine Learning. Some of its key components are: HDFS The Hadoop Distributed File System (HDFS) is a specialized storage system for Hadoop. HDFS stores data in a fault-tolerant manner by replicating it over multiple nodes. This allows protection against data loss in case one of the nodes fails. HDFS also offers easy integration with tools such as Spark for data processes. Sqoop HDFS is only a file system and requires an ETL pipeline to gather and store data. Apache Sqoop allows users to connect to an RDBM system and transfer relational tables into the distributed storage. With Sqoop, data engineers can configure several worker nodes for parallel processing and schedule jobs for timely ingestion. Flume While Sqoop connects structured data, Flume does the same for unstructured files. It uses distributed computing power of the Hadoop cluster to move extensive unstructured data into HDFS, such as images, videos, data logs, etc. Flume allows the processing of data in batches of streams in real-time. Recently real-time data streaming and analytics have gained much traction, and we’ll discuss those later in this article. Spark Apache spark is a data processing engine that supports multiple programming languages. It supports setup in mainstream languages like Java, Scala, Python, and R. It is a famous framework that uses distributed computing to perform tasks quickly. It can be used for data querying and analytics. Spark mainly utilizes advanced libraries for machine learning training, leveraging the power of distributed computation. Piece Them Together All these tools combined lay the groundwork for a successful data lake. Data within this lake can be accessed by multiple users across the organization, who can use it for their own individual goals.