Data Ingestion
Data ingestion is the process of transporting data from one or more sources to a target site for further processing and analysis. This data can originate from a range of sources, including data lakes, IoT devices, on-premises databases, and SaaS apps, and end up in different target environments, such as cloud data warehouses or data marts.
Data ingestion is a critical technology that helps organizations make sense of an ever-increasing volume and complexity of data. To help businesses get more value out of data ingestion, we’ll dive deeper into this technology. We’ll cover types of data ingestion, how data ingestion is done, the difference between data ingestion and ETL, data ingestion tools, and more.
Types of Data Ingestion:
There are three ways to carry out data ingestion, including real time, batches, or a combination of both in a setup known as lambda architecture. Companies can opt for one of these types depending on their business goals, IT infrastructure, and financial limitations.
How Business Optima Handles the Processing Headaches?
Our customer base is strong support for us. While talking to them, we found that most of our large scale customers, which includes giants in the insurance and finance domain, were facing a similar challenge. They found it hard to process data in multiple formats, such as XML, CSV, or PDFs, or that ranged from a few KBs to 100s of MBs to 10s of GBs per file. Plus, storing such large chunks of data was back-breaking.
They needed a data ingestion software tool not only for processing large, flat, or hierarchical files but also for streaming data transformation in parallel to process the data and deposit in a data warehouse through a real-time ETL. The data needed to be cleansed of errors, validated with business rules, transformed by normalizing into a common format. The challenge was multifold.
Business Optima data ingestion solution is a fully managed, simple and extendable ETL-based model for efficiently extracting, and moving large amounts of data in real-time. Our solution supports many use cases: self-service integration, real-time analytics, continuous computation, data Lake, etc. It is scalable, fault-tolerant, and easy to set up and operate.
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