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We’re living in the age of Big Data. 

Bursts of information inundate business IT ecosystems continuously from multiple sources, whether it’s a series of recent Tweets, log files from mobile web app activity, or measurement data from IoT sensors.

This information uncovers essential insights. But to harness analytics capabilities, organizations must overcome hurdles in capturing, processing, storing, and analyzing these always-moving, high-velocity data streams. At the core of everything is visibility. 

Since data comes from so many different source systems, regulating its structure is impossible. In fact, 80 percent of data fed into enterprise systems is now unstructured. Another factor to consider is scalability—as companies grow, the volume of data streams they ingest expands, and the data architecture often can’t keep up.

Even if the architecture is robust and scalable, having so much data runs the risk of inaccuracies and shadow data issues, affecting compliance and regulatory requirements. 

Giving your data teams a helping hand with automated tools can help you gain a competitive advantage, which we’ll discuss in this blog.

What is Data Streaming? 

Data streaming is the process of collecting data as it’s generated, then moving it to a destination to leverage it for real-time intelligence. This contrasts with the traditional approach in which data engineers build pipelines and use solutions to ingest, process, and structure data in batches before it’s ready for analysis. 

The latter approach is known as batch processing, and it involves processing data in intervals after a specific triggering event, such as collecting a sufficiently defined amount. Batch processing is suitable for historical analyses and yearly reviews where real-time processing and analysis are unnecessary. Otherwise, data streaming is the way to go.

Data streaming architecture combines various tools and frameworks. The need to architect a particular kind of solution reflects the complexity involved in consuming large volumes of data in flight. Typical architectural components of streaming data pipelines include:

• An event streaming tool that listens for events from various sources and streams them on an ongoing basis.
• Real-time ETL tools that process streaming data and prepare it for analysis.
• Streaming data analytics tools that enable businesses to uncover insights.

What are the Benefits of Data Streaming? 

Gartner predicts that by 2025, more than 50% of enterprise-critical data will be created and processed outside the data center or cloud, reflecting the popularity of streaming data. Businesses increasingly turn to dedicated tools to help query or analyze their continuous data flows for insights, and here are just a few ways these tools can be beneficial.

Improved Customer Experience

Having up-to-date insights into customers’ behaviors and preferences helps businesses rapidly adapt how they serve customers. These insights can come from analyzing website activity (e.g. blog post engagement, clicks, downloads) or social media mentions. You can offer more timely, targeted, and personalized interactions by tailoring customer experiences based on real-time insights.

Find and Fix Problems Faster

Real-time insights immediately warn businesses about impending or current systems issues. With these warnings, it’s possible to identify and fix problems faster to minimize downtime, remediate cyber threats, and control financial risks from sudden fluctuations.

Capture Instant Insights

There’s often a lack of visibility over business operations when data is spread over multiple sources, especially as your company manages increased data volumes through acquisitions, mergers, and growth. Data streaming access to real-time insights that you can use for monitoring capabilities, data-driven decisions, and replication scenarios. 

Top 9 Real-Time Data Streaming Tools and Technologies 

Here are nine top real-time data streaming tools and technologies to consider (listed in no particular order). 

1. Hevo

Hevo is a no-code platform for streamlining and automating data flows. It enables users to merge data from 150+ sources using plug-and-play integrations in near real-time. 

One user describes their Hevo experience as, “Extremely easy to use and flexible – it has all the features you could want, including pre and post-load transformations.”

Pros

• Intuitive user interface.
• Generous free tier.

Cons

• Deduplication could be better.
• Replicating existing pipelines for re-use is time-consuming.

2. Equalum

Equalum provides continuous data integration and real-time data streaming via a single, unified platform. It’s an end-to-end solution for data streaming tasks, enabling you to collect, transform, manipulate, and synchronize data from any data source to any target. Apart from its native data ingestion capabilities, Equalum also enables the move from on-prem to cloud-based by leveraging streaming ETL and built-in CDC capabilities. 

One reviewer’s summary of Equalum noted that “Equalum is real-time. If you are moving from an overnight process to a real-time process, there is always a difference in what reports and analytics show compared to what our operational system shows. Some of our organizations, especially finance, don’t want those differences to be shown. Therefore, going to a real-time environment makes the data in one place match the data in another place. Data accuracy is almost instantaneous with this tool.”

Pros

• Rapid deployment and end-to-end system monitoring with just a few clicks.
• Data streaming pipelines are fully orchestrated and managed for a true end-to-end solution.
• High levels of data accuracy.
• Offers unlimited scale to increase data volumes.

3. Striim

With Striim, you can monitor business events across any environment. The platform has sub-second latency from source to target and helps you make decisions in real time. 

One user says of their Striim experience, “It’s easy to build apps and start moving data to cloud.”

Pros 

• Wide range of target destinations available.
• Point and click wizard makes it straightforward to connect to data sources.

Cons

• The user interface is not the most intuitive.

4. IBM Stream Analytics

IBM Stream Analytics is a tool for the IBM cloud that enables users to evaluate a broad range of streaming data. Analysts can uncover opportunities in real-time thanks to the built-in domain analytics. 

“Its capacity, flexibility, and scalability…is excellent and meets the business needs,” says one IBM customer. 

Pros

• You can use existing code to build streaming applications without starting from scratch.
• Easily analyzes unstructured data.

Cons

• The quality of speech-to-text insights generated from the tools’ integration with IBM Watson is sometimes poor.
• The user community seems sparse and not actively encouraged.

5. Informatica

Informatica provides a scalable architecture for streaming analytics with real-time data ingestion and management. The platform works in 3 stages: 1) ingesting the data, 2) enriching it, and 3) operationalizing it. 

According to a review, “the platform accommodates large volumes of data…and we can extract a lot of data in a fraction of seconds.”

Pros 

• Data enrichment features add more value to raw streams of data.
• Can process almost any data format and type you can think of.

Cons

• Lack of user community or forums makes it harder to discuss queries about the platform or find answers to common problems.
• The user interface can be sluggish at times.

6. Talend

Talend aims to make data streaming more accessible by letting users interact with many sources and target Big Data stores without needing to write complicated code. The application is more akin to middleware, making streaming data pipelines work faster and easier.  

One user review pointed to Talend’s “easy integration with the cloud and quick manipulation of datasets”. 

Pros

• Good connectivity to over 130 data sources.
• Good support and help are available.

Cons

• The user interface is somewhat clunky to navigate, and the platform runs slow compared to other similar tools.
• The documentation is below par and can result in much trial and error in finding out how to perform certain tasks.

7. Fivetran

Similar to Talend, Fivetran is a data streaming tool best described as middleware. Fivetran  integrates events and files into a high-performance data warehouse in minutes by connecting with event streaming tools like Apache Kafka.   

One reviewer’s opinion of Fivertran was that it’s “super easy and just a matter of a few clicks to create a connection between and traditional DB to Cloud. The loads are real-time and can be scheduled on a timely basis.”

Pros

• Easy to use and implement.
• Good metadata support.

Cons

• Costs can quickly add up even if you leave connectors open unintentionally.
• Support can be slow to respond to queries or issues.

8. StreamSets

StreamSets is a data integration tool that aims to provide a flexible and intuitive approach to build all kinds of data pipelines. You can build streaming, batch, and machine learning pipelines from a single user interface.

Pros

• Very user-friendly interface.
• Comprehensive user documentation enables self-service troubleshooting and learning.

Cons

• Latency can be an issue at times resulting in lag or data loss.
• Manually debugging errors can be tricky due to unclear logs.

9. Qlik

Qlik’s data streaming capabilities come from its data integration, which efficiently delivers large volumes of real-time, analytics-ready data into streaming and cloud platforms, data warehouses, and data lakes.

An online reviewer said that Qlik “allowed us to ingest data in near real-time from our ERP landscape and use it for various data lake and data science initiatives.” 

Pros 

• Easy to use.
• Solid performance gives low latency and maintains data integrity.

Cons

• Initially setting up and configuring the tool can be time-consuming.
• Troubleshooting is difficult due to the complex documentation. 

Better Accuracy, Visibility, and Scalability With Data Streaming

Real-time data streaming tools and technologies provide a useful foundation for deriving insights from high-velocity, continuous influxes of data. Improved data accuracy, real-time visibility, and the scalability to handle growing volumes of data are just some of the benefits you get from data streaming. The next step is to choose the tools that deliver these benefits. 

With Equalum’s built-in CDC capabilities, you can continuously access real-time data, track changes, and apply transformations before ETL. Enjoy better visibility over your data with real-time decision-making and fast deployment. 

Get your Equalum demo here.

Real time, streaming data is king in today’s modern, data driven business world. Industry leaders have harnessed the power that reliable, performant and highly sophisticated, real-time data architectures can bring. Immediate and even predictive response to customer behavior is made possible. Strategic decision making for future innovations is enabled with real-time data for analytic and BI systems. Operational efficiencies and cost reduction opportunities are quickly identified before spiraling into millions lost. More importantly, data teams are crushing it – moving quickly past labor intensive remediation and stitching of systems to strategic initiatives and testing new approaches to data integration.

For many organizations, however, the road to real time data hasn’t been as smooth or fruitful. Over time, as various initiatives took precedence, data integration use cases were implemented with a slew of different tools specific to those needs. As data architecture complexity grew, so did data silos, the number of source systems and the units across the business needing access to different pools of data. As Cloud migration started gaining steam, additional pressures were being added onto the shoulders of over-taxed IT teams trying to dig a way out.

MODERN DATA INTEGRATION REQUIREMENTS

With the numerous tools that most companies have acquired over time to perform their data integration needs, data architectures are often overly complex. All of these technologies facilitate critical, necessary use cases (replication, batch, streaming ETL, streaming ELT, change data capture) but in many cases, they are stand alone solutions. A replication specific tool may not offer complex transformations. A tool providing streaming ETL might not offer comprehensive, modern Change Data Capture. You also can’t forget about batch. Not every data pipeline needs to push real time data to the source, and many companies still rely on batch data processing for important business data without the real time imperatives for delivery. The unintended consequence of acquiring these tools over time is high TCO plus significant expertise required to support, install, integrate, operate and maintain.

What’s worse is the pressure on teams to deliver accurate, reliable data in real time. Many existing streaming tools cannot meet throughput and latency requirements that are imperative to the business. Additionally, legacy data integration tools can struggle when connecting to more modern sources, leaving data behind as it attempts to capture changes in real-time. Data being pushed into analytic systems is stale, giving AI and BI yesterday’s news and sometimes data duplicates. As IT teams scramble to try and fix the issues, they get stuck in lengthy and complex configurations, and a sea of patch and fixes.

PRIMARY CONSTRAINTS ON IT TEAMS TRYING TO MODERNIZE DATA ARCHITECTURES

There are a number of common constraints that prevent organizations and IT teams from optimizing their data architecture towards a real-time, streaming first approach.

• Their IT team is too taxed get to everything needed
• They have a shortage of deep data integration & coding skills (Python, Spark, Kafka, etc)
• They don’t have enough time to appropriately deal with everything they should
• There isn’t enough budget for tools PLUS skilled IT and/or outside help
• Their existing systems are complex with expensive licenses and lots of data silos/formats, etc

There is often a lack of end to end visibility when trying to evaluate system health across multiple tools. Managing 3 separate solutions with 3 different monitoring systems and no UI is a true challenge. As data volumes grow, new company acquisitions occur and systems merge, and the velocity of data coming into the system accelerates, data silos can plague organizations without performant, real-time streaming.

WHAT TO LOOK FOR AS YOU MODERNIZE DATA INTEGRATION

When streamlining your data architecture and data integration approach towards real time, there are a few, core elements that should guide you along the way.

1. Look for a solution that offers all of the core data integration use cases under one, unified platform. Combining Change Data Capture, Streaming ETL / ELT / EtLT, Batch ETL and Replication under one umbrella means easier management and monitoring, visibility into what’s working and what isn’t, and consolidation of tools. That translates into less cost and more time leveraging your data.
2. Simplicity is paramount (i.e. ease of use, rapid deployment and seamless maintenance). If you are trying to consolidate, then another underlying goal is building simplicity into your data integration. Look for a solution that puts the user first with an easy to navigate and well designed UI. Power and performance don’t have to live hand in hand with heavy coding and manual intervention. When designed well, the right data integration solution will do the heavy lifting for you through automations, built in frameworks and pre-configurations.
3. Flexibility offers future-proof protection. Your data is constantly evolving, as are the sources you pull from and the targets where data lands. Don’t land in the same trap of tools that have limited capability and are specific to just one use case. Look for a data integration solution that can be flexible where you need it most. On-prem, on-cloud, a hybrid approach or SaaS is a necessity as you build a multi-cloud framework, or explore moving some data to cloud but keep sensitive business data on-prem. Ensure your solution can meet your needs, and a future-proof data integration strategy.
4. Don’t sacrifice performance. A well designed solution should be able to offer best in class performance across the board that can grow with your business, not buckle under pressure. IT teams should be able to set expectations high, knowing that various business units will continue to demand data from all corners. A highly performant system can support these requests and more without a hitch.
5. Scalability – Grow with your data. If our current moment is any indication, data volume, velocity, and formats will continue to shift and expand. With successful business growth comes growing pools of data that need to be processed in real-time. Make sure that your solution can scale as you need it to. Your data integration framework must be able to grow quickly and seamlessly with your business.
6. Rapid Time to Value. Don’t waste time waiting weeks, months or even years for your project to head to production. Find a solution that offers all of the above WITH rapid deployment. Your streamlined, modern, real-time data architecture should be a few clicks away.

Migrating to the cloud used to be considered an opportunity for the enterprise – an area of business growth and potential marked as something to pursue, but not necessarily an immediate imperative. With the advent of COVID-19 and the exponential pressure on digital transformation and real-time response to ever changing consumer behavior, cloud migration and ingestion has become more of a business risk than future plan. There are many drivers pushing the enterprise and businesses towards Cloud Migration.

A few common use cases are the need for:

1. Zero-downtime migration from on-prem to the cloud
2. Enabling Real Time Analytics
3. Enabling Cloud BI

These new use cases come with challenges to overcome including:

1. Migrating data from older, legacy technologies to new state-of-the-art-technologies which are nothing alike. You will have to deal with data type conversions, different syntax, drivers, etc.
2. Extracting data in real-time in a non-intrusive way that will not negatively impact your operational systems.
3. Managing and monitoring hundreds and, in some cases, thousands of ingestion processes and then find and treat any possible errors.
4. Working with and managing multiple tools to accomplish the desired data ingestion/migration scope of work.

For example, in order to facilitate zero-downtime migration, you will need a change data capture powered replication tool. To enable real-time analytics, you will need a stream processing tool. For traditional batch ETL, you will need a dedicated tool, ending up with three tools to manage, monitor and pay for.

Many organizations are feeling the pressure of either legacy, proprietary tools feeding their architectures, driving up costs and locking them in. Others are looking for avenues to upgrade and/or streamline for reduced cost, real-time insights and ease of use.

Check out Equalum’s answer to these three Cloud Ingestion & Migration Use Cases in these brief platform Demos.

USE CASE #1: Zero Downtime Migration from On-Prem to Cloud

Equalum Example: Replicate Changes from Oracle to AWS Postgres

USE CASE #2: Enabling Real-Time Cloud Analytics

Equalum Example: Stream, Transform & Load Data in Real-Time from On-Prem Kafka to Azure Data Lake

USE CASE #3: Enabling Cloud BI

Equalum Example: On-Prem Batch ETL into Snowflake Data Warehouse