Businesses use data analytics to uncover valuable information, including unknown correlations, market trends, camouflaged patterns, and customer preferences, for making informed business decisions. Valuable insights put you ahead of potential competitors and trigger small but cardinal changes that can revolutionize your business.
To gain a holistic view of data from myriad sources, companies need to correlate and place it in a centralized location, a data warehouse, which is a repository architected for analytical reporting, structured and/or ad hoc queries, and decision making. This process of moving data from one or more sources to a database for immediate use or storage is called Data Ingestion.
In the era of large data sets and complex data structures, having a strong data ingestion strategy is essential. So what’re the top things to know while implementing a data ingestion system? Let’s find out.
Data ingestion is a process of transporting data from assorted sources including on-premise apps, SaaS apps, and spreadsheets to a single storage medium for analysis. The storage medium is typically a data warehouse, database, datamart, or a document repository. Being the backbone of an analytics architecture, data ingestion allows organizations to obtain data for supporting downstream reporting and analytics systems. Data ingestion can be of different types, each with varying uses and implementation demands.
Data can be ingested mainly in two different ways:
Batch: Data can be ingested in chunks. In this method, the data ingestion layer collects and groups source data periodically and sends it to the destination system. Based on logical ordering, activation of conditions, or a schedule, groups can be processed without minimum human intervention. This particular type of method is preferred in cases where access to near-real-time data is not a necessity. Batch processing is particularly easier and affordable than streaming ingestion.
For example, credit card companies use a batch processing method to process their billing process. The end-consumer receives a monthly bill for all the purchases instead of bills for each separate credit card purchases. To create the bill, data is ingested as a batch at the end of the billing cycle.
Streaming: Also called real-time processing, streaming ingestion method involves no batching at all. In this method, data is extracted, transformed, and loaded in real-time. Real-time processing is expensive as it requires systems to monitor data sources and accept new information constantly. The challenge is further compounded when the size of the data is large.
For instance, radar systems require real-time processing as sensitive data detected by radar needs immediate attention to act prudently at the right time.
Certain challenges can severely impact the data ingestion process and pipeline performance as a whole. Here are a few:
Back in the days, when data was considerably less diverse and resided in few-dozen tables, performing data ingestion manually was easy and effective. Today, however, with the increases of data’s volume, velocity, and variety, ingesting data manually can cost a huge sum of money and unnecessary effort. In truth, curating data manually can make the process slow and unproductive. So, one needs an automated data ingestion processor to effectively ingest large data sets for delivering useful insights. It offers a number of benefits such as:
Integration platforms can help organizations ingest large data streams to extract insights for making better business decisions. See how we can take your most voluminous, complex data and process it in record time with least vulnerability.