Analytics and business intelligence (BI) based on data from a variety of sources enables employees in data-driven organizations to make effective decisions. More data from more sources means better insights and decisions.
Data ingestion is the process of moving different sets of data from databases, SaaS platforms, and other sources into a centralized destination — such as a data warehouse, data mart, database, or a document store — where it can be accessed, used, and analyzed by an organization.
Self-service data ingestion refers to the use of a tool that makes it easy for nontechnical employees to connect data from assorted sources to a destination where they can then use it with self-service analytics and BI tools.
Years ago, when data warehouses ran on purpose-built hardware in organizations' data centers, data ingestion — also referred to as data integration — called for an ETL procedure in which data was extracted from a source, transformed in various ways, and loaded into a data warehouse. The transformation processing took place in the data pipeline, to preserve limited CPU cycles for data analytics.
Businesses used to create and maintain their own ETL code, which was expensive, time-consuming, and prone to human error. But today, with cloud data warehouses that can scale to handle any processing load, businesses can skip preload transformations and load raw data into the repository. Data analysts can define transformations in SQL and run them in the data warehouse at query time. This new sequence, ELT, is ideal for replicating data cost-effectively in cloud infrastructure.
A self-service ELT tool makes data ingestion easy and performant, and it removes the burden of building and maintaining a tool from the IT department.
Self-service analytics empower everyone in an organization to make data-driven decisions, and self-service data ingestion makes a broad range of data sources available for that purpose, leading to better analytics.
Self-service data ingestion allows nontechnical employees to add data sources and select a destination into which to replicate data, which means faster time to business insights.
A self-service ELT tool should be able to ingest data as fast as the source API provides it, write it as fast as the destination API allows, and handle a high volume of transactions when overall load increases, ensuring the data pipeline never falls behind.
With a self-service data ingestion tool, data professionals and the IT department won't have to create and maintain custom ETL jobs; they'll be able to focus on improving customer service or optimizing product performance instead.
Of course, data engineers could build an in-house ETL tool for nontechnical users. But that takes time, and leaves the burden of system monitoring and maintenance on the IT department.
Stitch — a self-service ELT tool — makes it simple to integrate data from multiple sources to cloud data warehouse destinations where employees can use it for business intelligence and data analytics.
Sign up for Stitch for free and get self-service data ingestion for more than 100 data sources in minutes, not weeks.