Gone are the days when businesses could process their data once a week or once a month to see past trends and predict the future. As data becomes more and more accessible, the need to draw inferences and create strategies based on current trends have become essential for survival and growth.
It is no more only about data processing and creating data pipelines, it is about doing it in real-time. This has created a need for technologies that can handle streaming data and enable a smooth, automated flow of information from input to output as needed by different business users. This growing demand is reflected in the fast-growing demand for Big Data technologies, which is expected to grow from 36.8 billion in 2018 to 104.3 billion in 2026 at a CAGR of 14 %, according to Fortune Business Insights.
Features of a Streaming Data Pipeline
The key elements for a good pipeline system are:
- Big Data compatibility
- Low latency
- Multiple options to handle different use cases
To make it cost-effective and meet the organizational needs, the Big Data pipeline system must include the following features:
- A Robust Big Data Framework with a high volume of storage Apache Hadoop.
- A publish-subscribe messaging system
- Machine learning algorithms to support predictive analysis support
- A flexible, backend storage for result data
- Reporting and visualization support
- Alert support to generate text or email alert
Tools for Data Pipeline in Real-Time
There are several tools available today for creating a data pipeline in real-time, collecting, analyzing and storing several millions of pieces of information for creating applications, analytics, and reporting.
We at Indium Software, with expertise and experience in Big Data technologies, recommend the following 5 tools to build real-time data pipeline:
- Amazon Web Services: We recommend this because of its ease of use at competitive rates. It offers several options such as Simple Storage Service (S3) and Elastic Block Store (EBS) to store large amounts of data which is supported by Amazon Relational Database Service for performance and optimization of transactional workloads. AWS also offers several tools for data mining and processing data. AWS Data Pipeline web enables the reliable processing and moving of data between different AWS compute and storage services. This is a highly available and scalable platform for your real-time data processing needs.
- Hadoop: Hadoop can be effectively used for distributed processing of huge data sets across different clusters of servers and machines parallelly. It uses MapReduce to process the data and Yarn to divide the tasks, responding to queries within hours if not seconds. It can handle Big Data volumes, performing complex transformations and computations in no time. Over time, other capabilities have been built on top of Hadoop to make it a truly effective software for real-time processing.
- Kafka: The open-source, distributed event streaming platform Apache Kafka enables the creation of high-performance data pipelines, data integration, streaming analytics, and mission-critical applications. Kafka Connect and Kafka Streams are two components that help in this. Businesses can combine messages, data and storage using Kafka whose other valuable components such as Confluent Schema Registry allows them to create the appropriate message structure. Simple SQL commands empower users to filter, transform and aggregate data streams for continuous stream processing using ksqlDB.
In addition to being used for batch applications and real-time, Kafka helps integrate with REST, files and JDBC, the non-event-streaming paradigm for communication. Kafka’s reliable messaging and processing with high availability makes it apt for small datasets such as bank transactions. The other two critical features, zero data loss and exactly once semantics, makes this ideal for real-time data pipeline creation along with streaming data manipulation capabilities. On-the-fly processing is made possible with Apache Kafka’s Streams API, a powerful, lightweight library.
- Spark: A popular open-source real-time data streaming tool promises performance and lowers latency. Spark Streaming enables the merging of streaming and historical data and supports Java, Python, and Scala programming languages. It also provides access to the various components of Apache Spark.
- Striim: Striim is fast becoming popular for streaming analytics and data transformations because of it being easy to implement and user-friendly. It has in-built messaging features to send alerts, ensures secured data migrations between, ease of data recovery in case of failures and agent-based approach for highly secured databases.
Indium has successfully deployed these technologies for its various data engineering projects for its customers across different industries, including banking, mobile app development and much more.
We have the experience and expertise to work on the latest data engineering technologies to provide the speed, accuracy and security that you desire for building data pipelines in real-time. Contact us for your streaming data engineering needs.