Enterprises are hungry for data. However, it doesn't take an enterprise long to learn that its appetite for data is often much bigger than its ability to digest huge amounts of information. The reality is that collecting data is a continuous effort that requires a truly impeccable plan for data stream processing. Enterprises need to be able to control, edit and personalize how and where data is collected if they want to create truly useful results.
Collecting Data From Multiple Sources
Enterprises today need to be able to collect data from all angles. Many business operations constantly take in data from multiple sources. For instance, a retail brand may need to be able to process and monitor transactions, customer browsing habits, and ad feedback. All of this data will pour in to help enterprises get clear pictures of how their brands, services, and offerings are performing. The real-time data that is received can then be used to make adjustments on a short-term level. What's more, that data can also be viewed as a whole to help an enterprise make long-term operational plans. Enterprises that deal with healthcare settings or medical records often rely on data systems to access and update patient information. Financial enterprises rely on data streaming to create real-time risk assessments or monitor activity. Why enterprises rely on data isn't important. What is important is that enterprises need to have reliable, cutting-edge systems that can capture, store, protect and display data in useful ways. However, this isn't always what happens.
The Challenges of Today's Ingestion Tools
Data ingestion is one of the big topics on the minds of tech insiders these days. It seems that the ingestion abilities of many mainstream products simply aren't keeping up with the pace of data growth. Enterprises often experience bumps in the road when it comes to getting the most out of data once it has entered a system. One big issue enterprises face is that poor-quality data can cause complications or lead to faulty results. What enterprises should be focused on are solutions that offer simple filtering and classification to reduce processing times and cut down on disparities. Another issue that enterprises face when it comes to data ingestion is a lack of flexibility. The reality is that different enterprises have different goals when it comes to consuming data. However, many mainstream stream processing solutions have been slow to drift away from cookie-cutter solutions with rigid rules. This can often cause different departments within an organization to use different data tools. For instance, marketing and business intelligence may need data systems that do vastly different things. As a result, there are many conflicting technologies being used in one place. This can make it hard for an enterprise to get a clear picture of what data is actually revealing in terms of the big picture.
What Enterprises Really Need to Solve Data Ingestion Issues
What every enterprise needs is a real-time data pipeline that has the ability to collect data from multiple sources and direct it to the proper channels. Companies that provide big data applications and platforms can answer this need by providing specified ingestion pathways to convert data from multiple sources. Enterprises can streamline their processes by using templates that ingest data from a variety of sources. That data can then be backed up and directed to specific clusters and databases. The result is a data assembly line that can ingest data in a multitude of formats without creating a fractured stream processing system.
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