Currently, enterprises can churn out data analytics from large-sized data streams from a variety of sources. To make better decisions, they need access to all of their data sources for improved analytics and business intelligence (BI).
When a company has an incomplete picture of available data, it can result in misleading reports, erroneous analytic conclusions, and ultimately inaccurate decision-making. For a business user to correlate data from myriad sources, data must be stored in a single location – a data warehouse or data lake.
However, to employ information to make important business decisions, users must ingest it before digesting it. It’s important for managers, analysts, and decision-makers to comprehend data ingestion and associated technologies, simply because a strategic approach helps them design the data pipeline and drives value.
Data Ingestion Before Digestion
Data ingestion involves the process of absorption of data from different sources to a data warehouse or data lake where it can be accessed, leveraged, and analyzed by an enterprise for decision-making. Sources can include almost anything – SaaS data, in-house apps, spreadsheets, databases, and more.
In terms of structure, the data ingestion layer is the backbone of any analytics architecture. And the quality of insights extracted will actually depend on the way the data is ingested. The way this process is executed is often founded on myriad models or architectures.
Data ingestion platforms allow users to ingest large streams of data into a data lake, where it can be stored for analysis or use. They use several ways to do that but, in general, there are two ways:
Batch processing is the most common way business users are using today. Here, the ingestion layer garners and groups source data on a periodic basis and subsequently sends that data into the destination system.
In this type of data ingestion, users made groups to process data, and the processing is done on the basis of any logical ordering, the activation of certain conditions, or a simple schedule. In cases wherein, real-time is not important, batch processing is typically used, since it’s generally easier and more affordably implemented than other types of ingestion.
Real-time processing, also known as, stream processing or streaming doesn’t involve groupings at all. In this type of processing, data is collected, changed, and finally loaded as soon as it’s created or recognized by the data ingestion layer.
This kind of ingestion is highly expensive, simply because it requires systems to constantly keep track of sources and accept new information. However, real-time processing can prove useful for analytics that requires continually refreshed data.
Point to note: Some “streaming” platforms employ batch processing. During such a business scenario, companies ingest information in groups at shorter intervals. However, the processing still doesn’t happen individually. This particular type of processing is often known as micro-batching.
Challenges Of Data Ingestion
With data volume and variety of data increasing, it has become difficult for organizations to ingest that data without complexity. Plus, information can come from a multitude of data sources, from transactional databases to SaaS platforms to other devices.
Now, the sources constantly are evolving while the ones come to light, making the data ingestion process even more difficult to execute. Coding and maintaining an analytics architecture that can ingest this much amount of data is apparently time-consuming and costly.
Speed is also a big challenge for both the data ingestion and data pipeline. With data complexity increasing each day, it takes a lot of time to develop and maintain data ingestion pipelines, especially when it comes to “real-time” data processing, turning the process slower than ever.
Self-Service Integration Platforms Is A Game Changer
Companies can rely on self-service-powered data ingestion platforms to ingest large volumes of complex customer data feeds into a data lake with ease and precision. These solutions can empower every business user in the company (and not just the technical ones) to execute this task, making it faster than ever.
And the technical/ IT teams can focus on more high-value tasks to drive growth and innovation. Thus, employing self-service platforms can enable business users to handle the data ingestion challenges and drive value faster – ultimately making organizations easier to do business with.
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