5 Real-Time Data Processing and Analytics Technologies - And Where You Can Implement Them

5 Real-Time Data Processing and Analytics Technologies – And Where You Can Implement Them

No matter your industry, you’ll often need to make split-second business decisions in the digital age. Real-time data can help you do just that. It’s information that’s made available as soon as it’s created, meaning you don’t need to wait around for the insights you need.

Real-time data processing can satisfy the ever-increasing demand for immediacy in data analytics. This practice differs from traditional batch processing because it produces data insights almost instantaneously rather than at scheduled intervals.

Let’s deep dive into real-time data processing, how it works, its real-world applications, and the best practices for using it.

Real-Time vs. Batch Processing

Batch data processing doesn’t happen in real time. It gathers and stores data in batches and then processes it at a specific time, which might be hours or even days after it’s collected. While batch data processing can’t provide real-time insights, it’s still beneficial. It allows for more complex data analysis by generating insights based on a larger and more comprehensive data set.

Real-time processing is a good idea if you need immediate insights. For example, you want to find out what customers are saying about you right now on social media. Batch processing might be a better idea if you want to analyze data over a longer period, which might reveal more valuable insights.

There’s been a huge shift toward real-time data pipelines in the last few years, especially in specific industries. For example, many customers want up-to-the-second shipping updates from transportation companies after purchasing products online. Social media companies also use real-time data processing to provide customized content on user feeds, allowing them to see what people are saying about a topic at any given moment.

Technologies Used in Real-Time Data Analytics

Data streaming, or streaming data processing, is the continuous flow of data from one or more sources at a high speed. It’s a huge part of real-time data analytics and allows for almost instant insights. Event streaming platforms process data events as soon as they occur.

Apache Kafka is an example of an event streaming platform that you can use for real-time data processing. It provides a data store for ingesting and processing streaming data, helping you create data pipelines that generate near-immediate insights. Businesses in various industries use it to analyze the data they need for decision-making.

A processing engine is also important for producing real-time insights. Apache Storm, for example, processes streaming data and identifies patterns and correlations in this information within as little as a few milliseconds. That makes it different from traditional tools that store data in batches and then analyze it at a later date. Apache Storm features include fault tolerance, which ensures data processing even if failures occur. You can also scale this tool up and down depending on how much data you want to analyze.

Apache Flink is another stream processing framework you should know about. It handles complicated event processing and can analyze data quickly. It has more advanced features than Apache Storm, such as windowed aggregations, which means processing data at different intervals. Business use cases include detecting fraud, optimizing supply chains, and tracking customer interactions from data sets in real time.

Incorporating Real-Time Data Analytics Into Your Business

One of the biggest challenges of real-time data processing is integrating event streaming platforms and stream processing frameworks into a tech stack. These technologies require a steep learning curve, so you’ll need to spend time training your team so they can use tools effectively.

You’ll also need to be aware of data governance legislation in your area and industry when processing large amounts of potentially sensitive information. However, you can overcome this issue by creating a data governance framework that lays down standards for processing, analyzing, and sharing information. Doing so can prevent fines for not complying with data privacy laws.

Choosing the right tools for real-time data analytics can result in more accurate insights. Consider technologies based on your business needs, the amount of data you want to process, and how quickly you want to process that information.

5 Real-Time Data Solutions

There are multiple methods your team can use to implement real-time data into your data infrastructure. For example you could use a data solution that sits on top of your data sources such as Rockset, or you can look into ETL and ELT solutions like Estuary which allow you to ingest and transform data into your data warehouses and data lakes.

So here are 5 real-time data solutions all offering different implementations that you could find useful.

Druid

Druid is a high-performance, real-time analytics database that allows companies to instantly gain insights from large volumes of event-driven data. Designed for sub-second queries, Druid has found its niche in scenarios where timely analytics is not just a luxury, but a necessity.

It also has several unique features that allow it to be used as a real-time OLAP. Everything from its various nodes and processes that each have unique functionality that let it scale to the fact that the data is indexed to be pulled quickly and efficiently.

In particular it’s architecture is designed to balance the demands of real-time data ingestion, fast query performance, and scalability. It achieves this balance through a combination of several core components:

Broker Process: Query routing nodes that parse queries, distribute them to the relevant data nodes, and aggregate the results.
Historicasl Process: This process stores and serves segments of immutable, historical data.
Middle Manager Process: This process is responsible for stream ingestion and handoff, managing the ingestion tasks, and indexing data in real-time. It creates and manages a Peon prorcess for each incoming stream.
Coordinator Process: This process manages cluster metadata and oversees the distribution of segments across Historical Processes.
Overlord Process: The orchestration layer that manages locks and tasks.
Router process – This process is used to route queries to different Broker processes, based on Rules set up to managed workload optimization, such as creating “lanes” for higher- and lower-priority queries.

But I have a future article on Druids architecture coming out, so I wont’ dive any deeper for now!

Estuary – Real-time ETLs and ELTs

Estuary is a real-time ETL/ELT and data integration platform. ETL—extract, transform, and load—is a critical component of data management and warehousing.

Estuary’s comprehensive feature set simplifies integration by way of “data pipelines.” This eliminates the hassle of consolidating data from multiple locations in various formats, providing a foundation for data accuracy. From there, the consolidated information is available for whatever your team needs it for, from building data-driven apps to enhanced analytics.

As you look around the Estuary website, you’ll find they refer to their product as a DataOps platform geared toward software engineering teams. While it is indeed well-suited for development teams delivering SaaS and streaming applications, Estuary’s flexibility and functions make it a compelling option for any organization dealing with lots of data that comes from disparate sources.

VentureBeat praised Estuary for providing a real-time data integration platform that offers both “batch” and “stream” data processing pipelines. Batch data processing, as its name implies, performs data integration in batches at specified intervals. Meanwhile, stream data processing integrates bits of information from various sources in real time. It’s this flexibility, in tandem with Estuary’s easy-to-manage data pipelines, that makes it a compelling integration solution for any business that needs to harness lots of data.

Rockset

Rockset is a real-time analytics solution that boasts the fact that it provides low latency search, aggregations, and joins on massive semi-structured data, without operational burden. What do all those fancy buzz words mean?

Unlike some of the other real-time databases that are on this list. Rockset is a combination of a database plus a sort of SQL engine that allows you to query across multiple data sources in real-time. For example, you can sit Rockset on top of your DynamoDB, Kafka stream, and MongoDB databases and query/join across all of them.

In real-time.

It automatically indexes your data — structured, semi-structured, geo, and time-series data — for real-time search and analytics at scale.

Also, Rockset provides a great UI for running your queries and several other features that are geared more towards developers.

Like Materialize, Rockset has also received a new round of funding and is currently hiring heavily. All great signs in terms of progress.

AWS Kinesis

Kinesis is a managed streaming service on AWS. AWS Kinesis being managed provides several advantages compared to some of the other tools on this list. It allows your team to spend less time managing infrastructure components and services and instead focuses more on development. Kinesis allows you to ingest everything from videos, IoT telemetry data, application logs, and just about any other data format live. This means you can run various processes and machine learning models on the data live as it flows through your system, instead of having to go to a traditional database first.

AWS Kinesis also has clear support from companies like Netflix. They use Kinesis to process multiple terabytes of log data every day. This is made easier by the fact that Kinesis is a managed service.

But that’s enough about technology, let’s talk about use cases.

Flink

https://flink.apache.org/

What is Flink?

Apache Flink is another popular open-source distributed data streaming engine that performs stateful computations over bounded and unbounded data streams. This framework is written in Scala and Java and is ideal for complex data-stream computations.

With continuous stream processing, Flink processes data in the form or in keyed or non-keyed Windows.

Applications of Real-Time Data Processing

Almost every sector now uses real-time data analytics for decision-making. Take the finance sector. Companies like Visa, HSBC, and MasterCard have successfully implemented real-time processing to analyze transactions and identify fraud.

In telecommunications, British Telecom uses Confluent’s Kafka platform to provide better experiences for customers and unlock revenue streams from real-time data events and we have worked with telecoms to implement Estuary to provide access to real-time analytics on their CDR(call data records). Even government agencies around the world use real-time data processing to produce accurate insights about the services they provide.

Experts predict that the real-time data processing market will grow at a compound annual growth rate of 21.5% from 2022-2028. The need for instant analytics is just one of the reasons for this growth. With new real-time data technologies, businesses can make more informed decisions and improve operational efficiency.

Real-Time Data Analytics Best Practices

You can make real-time data processing more effective by following these tips:

Data management

Great insights are only possible with high-quality data. Choose a real-time data processing platform that cleanses and validates data, removes duplicate data sets, and gets rid of inaccuracies.

Performance optimization

Boost the performance of data processing systems by training your team to utilize these tools properly. You can also use parallel processing to distribute large data sets across different nodes, which can result in faster insights. Tools like Apache Flink automatically process big data in parallel.

Scalability and reliability

Using technologies like Apache Kafka, which scales horizontally, lets you manage large data volumes over time. You can also improve the reliability of real-time platforms by making use of their monitoring tools. Doing so will help you learn about latency, errors, and other factors that could impact the quality of data analytics.

Other best practices include testing real-time streaming platforms to ensure they perform correctly. As previously mentioned, we also recommend creating a data governance framework when processing and analyzing potentially sensitive information.

Why Is Real-Time Data Processing So Important, and What Does the Future Hold?

Real-time data analytics provide almost immediate insights, helping you make better decisions about your business. Technologies like event streaming platforms and stream processing frameworks make this process possible, with several Apache products letting you process and analyze data in the moment.

The future of real-time data processing looks even more promising. AI and machine learning will generate even more accurate insights for business intelligence and reduce the latency that can occur when processing data. We also expect developers to create more tools for real-time data analytics as demand increases for up-to-the-second information.