In today's world, data is all around us and it's growing really fast. Every time we use social media or buy something online, we create data. This data is growing exponentially. Data that is massive, diverse and increasing at great velocity is known as big data.
Big data can be described according to three vectors: volume, velocity, and variety
Volume: The term "volume" encapsulates the immense quantity of data generated, collected, and stored in the digital era. The exponential growth of digital devices and systems has led to an unprecedented surge in data production. Consequently, organizations face both challenges and opportunities in managing and analyzing large data volumes.
Velocity: "Velocity" describes the rapid generation, processing, and analysis of data. With real-time or near real-time data production from sources like social media, sensors, and transactional systems, it's vital to quickly process and interpret this data for timely decision-making and insights.
Variety: The term "variety" emphasizes the diverse types and sources of data. Data comes in various formats, including structured (e.g., databases), semi-structured (e.g., XML), and unstructured (e.g., text, images, videos). To effectively integrate, clean, and analyze this diverse data, advanced techniques and tools are essential.
Tech Stack to Handle Big Data
Big Data encompasses massive amounts of both structured and unstructured data that flood businesses daily. This data originates from diverse sources such as social media, sensors, and mobile devices. Due to its complexity and scale, traditional technologies struggle to handle Big Data effectively. Instead, specialized architectures and technologies are required.
Hadoop
Hadoop, a renowned distributed computing framework, is designed for Big Data processing. It consists of two primary components: the Hadoop Distributed File System (HDFS) for distributed storage and MapReduce for distributed processing. Hadoop enables the distributed storage and processing of large datasets across clusters of commodity hardware, facilitating parallel processing and fault tolerance.
Apache Spark
Spark is a popular distributed computing framework for Big Data processing, offering a faster and more versatile alternative to MapReduce. Spark supports in-memory processing and a wide range of data processing tasks, such as batch processing, streaming analytics, machine learning, and graph processing.
Apache Kafka
Kafka is a distributed streaming platform utilized for building real-time data pipelines and streaming applications. It excels in ingesting, processing, and storing high volumes of data streams from various sources in real-time. Kafka's distributed architecture ensures horizontal scalability and fault tolerance, making it ideal for handling Big Data streaming workloads.
Apache Flink
Flink is a distributed stream processing framework that provides low-latency processing and high throughput for real-time analytics and event-driven applications. It supports both batch and stream processing, allowing for unified processing of both historical and real-time data. Flink's distributed runtime guarantees fault tolerance and high availability for Big Data processing applications.
Cloud Platforms
Azure | Google Cloud | AWS
Cloud providers offer a range of managed Big Data services and platforms that leverage distributed computing technologies. These platforms provide scalable storage and processing capabilities for Big Data workloads, eliminating the need for organizations to manage their own infrastructure. Services like Amazon EMR (Elastic MapReduce), Google Dataproc, and Azure HDInsight enable organizations to deploy and manage Hadoop, Spark, and other distributed computing frameworks on the cloud with ease.