Consolidated business process with big data ingestion

Blog - Big Data, Data Analytics

Data and analytics have a new focus, and they are no longer just for crunching data. Now, they are increasingly used to guide business strategy. Before the invention of big data technology, we were unaware of the potential of data and the enormous volume of it available through several channels including the Internet of Things.

Maintaining the quality and completeness of data from any source becomes crucial to run any business intelligence activity on data. As it can be in a variety of formats and come from a variety of sources Such as RDBMS, S3 buckets, CSVs, or other types of databases

Data quality matters while ingesting data as one cannot use that data for BI on an ad-hoc basis so, data is sanitized and translated into a uniform format using an extract/transform/load (ETL) process.  The act of importing huge, mixed-format data files from many sources into a single, cloud-based storage medium—a data warehouse, data mart, or database—where they can be accessed and analyzed is known as data ingestion

Today’s businesses rely heavily on data. They require user data to develop future estimates and plans. They must comprehend the user’s requirements and behaviours. All of this helps businesses to build better goods, make better decisions, run ad campaigns, provide user recommendations, and obtain a better understanding of the market. It gives better customer-centric products and increases consumer loyalty in the long run.

Ingestion of data could be done in real-time or in batches at predetermined intervals. It is completely reliant on business needs.

Let us understand with examples how time, lives, and money are all intertwined. In systems processing medical data like a heartbeat or blood pressure with IoT sensors, where time is crucial, real-time data ingestion is often chosen. Financial data, such as stock market transactions, are handled with big data.

Ultimately, implementation of Data ingestion in the business process provides multiple benefits such as:

  • It allows data access among diverse departments and functional areas with varying data-centric requirements across the organization.
  • A simpler solution for gathering and purifying data from, with dozens of types and schemas, and converting it into a single, consistent format from hundreds of sources.
  • It can analyze large amounts of data quickly in real-time batches, as well as cleanse and/or add timestamps during the ingestion process.
  • Cost and time saving is achieved as compared to the manual data aggregation process, especially, if the solution is delivered as a service.
  • A startup service provider can collect and analyze massive amounts of data and manage its spikes with ease.
  • Large data volumes in the raw form are stored on the cloud and offer access to them wherever and whenever required.

When data is moved around, the risk of a breach increases and data transition will become risky. It passes through numerous distinct staging regions, so the development team of MSRCosmos devote additional work to ensure that their system meets all security requirements.

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