Data streaming is the next wave in the analytics and machine learning landscape as it assists organisations in quick decision-making through real-time analytics. With the increased adoption of cloud computing, data streaming in the cloud is on the rise as it provides agility in data pipeline for various applications and caters to different business needs.
Understanding the importance of data streaming, organisations are embracing hybrid platforms in a way that they can leverage the advantages of both batch and streaming data analytics.
To assist firms in determining the best data streaming tools, Analytics India Magazine has compiled the most feature-rich tools for instant analytics.
Through Amazon Kinesis, organisations can build streaming applications using SQL editor, and open-source Java libraries. Kinesis does all the heavy-loading of running the applications and scaling to match requirements when needed. This eliminates the need to manage servers and other complexities of building, integrating, and managing applications for real-time analytics.
Kinesis flexibility helps businesses to initially start with basic reports and insights into data but as demands grow, it can be used for deploying machine learning algorithms for in-depth analysis.
Google Cloud DataFlow
Google recently purged Python 2 and equipped its Cloud DataFlow with Python 3 and Python SDK to support data streaming. By implementing streaming analytics, firms can filter data that is ineffectual and slackens the analytics. Utilising Apache Beam with Python, you can define data pipelines to extract, transform, and analyse data from various IoT devices and other data sources.
Azure Stream Analytics
IBM Streaming Analytics
It offers Eclipse-based IDE as well as supports Java, Scala, and Python programming language to develop applications. It also allows you to develop in notebooks for Python users to effortlessly monitor, manage and make informed decisions. The streaming services can be used on IBM BlueMix® to process information in data streams.
Built by Twitter, the open-source platform Apache Storm is a must-have tool for real-time data evaluation. Unlike Hadoop that carries out batch processing, Apache Storm is specifically built for transforming streams of data. However, it can be also used for online machine learning, ETL, among others.
Its ability to process data faster than its competitors differentiates Apache Storm in carrying out processes at the nodes. It can also be integrated with Hadoop to further extend its ability for higher throughputs.
Striim is an enterprise-grade platform that executes in a diverse environment such as cloud and on-premise. It provides users to mask, aggregate, filter, transform, and built-in pipeline monitoring to obtain operational resilience while moulding data for insights. Through Striiim, firms can effectively integrate with various messaging and other similar platforms to harness data for real-time visualisation.
SQL was transformed to build StreamSQL such that even a non-developer can create applications for manipulating streams of data and monitor networks, surveillance, and real-time compliance. Since it is built on top of SQL it is fast, easy-to-use and analytics-ready, thereby eliminating the need for data scientists for inspecting streamed information.
The benefits of real-time analytics include real-time KPI visualisation, demand sensing, among others. Data streaming allows organisations to make the most out of data and enable them to gain operational efficiency. Companies need to implement these tools in their business processes and harness the power of data in every way possible.