Businesses can now leverage data analytics based on big data from different sources to kickstart decision-making.
However, an incomplete picture of available data can result in misleading reports and spurious analytic conclusions, ultimately making organizations difficult to do business with. To correlate data from a variety of sources, data ought to be stored in a unified, centralized location, often called a data warehouse or data lake – which is a database primarily architected for efficient reporting.
Data garnered from different sources can be in any form, structured or unstructured, in any format. This data has to be ingested before it can be digested. Analysts, apex decision-makers, and managers need to wrap their heads around big data ingestion and its associated technologies, simply because a streamlined approach to designing the data pipeline ultimately drives business value.
Data ingestion is a process of transportation of data from different sources to a unified database where it can be accessed, used, and analyzed for future purposes. The unified database is called a data warehouse, database, data mart, or a document store. Many types of sources come into play, from in house apps, SaaS data to spreadsheets.
Truth is, any analytics architecture’s foundational strength is its data ingestion layer. Downstream reporting as well as analytics systems bank on reliable data. There are myriad ways of ingesting data, and the framework of a data ingestion layer can be contingent on various models or architectures.
Batch Vs Streaming Ingestion
The structure of data ingestion layer depends on business requirements and constraints. The right data ingestion approach supports data strategy, and organizations usually pick the model that fits their standards; and considers the timeliness with which they’ll need analytical access to the data:
Batch Processing: In this type of processing, the data ingestion layer periodically collects and groups source data and sends it to the target repository. Groups may be processed on the basis of any logical ordering, certain conditions, or a plain schedule. When organizations do not need real-time data, batch processing is preferred as it easier and more cost-effective to implement.
Real-Time Processing: Also known as streaming, real-time processing technique involves no grouping at all. In this data is sourced, altered, and loaded when it’s identified by the data ingestion layer. Real-time processing is highly expensive since it needs all the systems to go through sources and embrace new data. But, it is a befitting option for analytics that require constantly refreshed data.
Point to note: It’s worth mentioning that some data ingestion and streaming platforms use batch processing. The ingested groups are relatively smaller, but they are not processed individually. This particular type of processing is called micro batching.
Self-Service Data Ingestion
Data has grown tremendously in the last few years, especially with digital transformation broadening its contours. With data volumes exploding and different data sources (from transactional to SaaS platforms) coming up, the ingestion process has become a hard row to hoe. Traditional big data ingestion tools find it difficult to handle such huge volumes of data soaked in complexity. Plus, these solutions put a lot of pressure on the IT teams as it becomes their job to onboard and ingest large volumes of data with ease. This strays them from focusing on the monitoring and control activities essential for innovation. So, relying on conventional solutions can be time-consuming, resource,-intensive and costly.
The solutions that have a self-service approach work outstandingly well in such cases. In fact, self-service data ingestion solutions act as a wonderful substitute, helping organizations ingest all the data into data lakes. With an agile data ingestion architecture, these modern solutions empower users deal with a wide range of complex data feeds without compromising quality or speed.
Apart from that, self-service based ingestion tools empower “all business people and not only the technically equipped ones” transport voluminous information into data lakes or data warehouses without seeking IT support. Meaning that IT need not intervene actively in the ingestion process, and so they become free to focus on governance and control. Hence, self-service data ingestion not only makes the process much simpler and efficient but also increases IT productivity.
Ergo, companies attempting to handle large volumes of complex data and store it in a reliable database need to rely on self-service data ingestion solutions.