Real-time analytics has become a hectic task for organisations looking to make data-driven business decisions. The data pipeline is at the heart of the company's operations. It allows organisations to take control of the data and use it to generate revenue-driven insights. However, managing the data pipeline involves tasks like data extractions, transformations, loading into databases, orchestration, monitoring and much more. As data becomes more and more accessible, the need to draw inferences and create strategies based on current trends has been essential for survival and growth. The task is not just about data processing and creating pipeline, but doing it in real-time. Immediate data streaming has become prominent in the field of big data analytics, and so are the real-time data streaming tools. According to Fortune Business Insights, the growing demand for data streaming tools is reflected in the fast-growing demand for big data technologies, which is expected to grow from US$36.8 billion in 2018 to US$104.3 billion in 2026 with a CAGR of 14% during the forecast period. Henceforth, Analytics Insight brings you a list of data streaming tools that work best to take data-driven decisions.
Apache Spark is an open-source and flexible in-memory framework which serves as an alternative to map-reduce for handling batch, real-time analytics and data processing workloads. It provides native bindings for the Java, Scala, Python, and R programming languages, and supports SQL, streaming data, machine learning and graph processing. From its beginning in the AMPLab at U.C Berkeley in 2009, Apache Spark has become one of the key big data distributed processing frameworks in the world. Spark is also fast, flexible and developer-friendly.
Keboola is a Software-as-a-Service (SaaS) data operation platform, which covers the entire data pipeline operational cycle. From ETL (extract-transform-load) jobs to orchestration and monitoring, Keboola provides a holistic platform for data management. The architecture is designed modularly as plug-and-play allowing for greater customization. In addition to all of the expected features, Keboola surprises with its advanced take on the data pipeline, offering one-click deployments of digital sandboxes, machine learning out-of-the-box features and more. The engineering behind Keboola is extraordinary. It is resilient, scales effortlessly along with user's data needs and utilizes advanced security techniques to keep the data safe.
Hadoop is an open-source framework that allows to store and process big data in a distributed environment across clusters of computers using simple programming models. It is designed to scale up from single servers to thousands of machines, each offering local computation and storage. Rather than relying on hardware to deliver high-availability, the library itself is designed to detect and handle failures at the application layer, so delivering a highly-available service on top of a cluster of computers, each of which may be prone to failures. It can handle big data volumes, performing complex transformations and computations in no time. Over years, other capabilities have been built on top of Hadoop to make it truly effective software for real-time processing.
Apache Kafka is also a leading technology that streams real-time data pipeline. It is an open-source distributed streamline platform which is useful in building real-time data pipelines and stream processing applications. Enterprises use Apache Kafka for the management of peak data ingestion loads and also as a big data message bus. The capabilities of Apache Kafka to manage peak data ingestion loads are a unique and formidable advantage over common storage engines. The general application of Kafka is in the back end for the integration of microservices. Besides, it can also support other real-time data streaming portals such as Flink or Spark. Kafka can also send data to other platforms for streaming analytics for the purpose of analysis.
Apache Storm is an open-source distributed real-time computational system for processing data streams. Similar to what Hadoop does for batch processing, Apache Storm does for unbounded streams of data in a reliable manner. Built by Twitter, Apache Storm specifically aims at the transformation of data streams. Storm has many use cases like real-time analytics, online machine learning, continuous computation, distributed RPC, ETL and more. It integrates with the queueing and database technologies that people already have. An Apache Storm topology consumes streams of data and processes those streams in arbitrarily complex ways, repartitioning the streams between each stage of the computation however needed.
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