Data-driven organizations harness the power of Data Analytics and Business Intelligence to make competent decisions. The greater number of data sources, the better insights are obtained.
Data ingestion is the process of transferring data from multiple data sources such as SaaS platforms, and databases to a destination from where it can be accessed, used, and analyzed. Automated data ingestion refers to the same process but using a self-service tool that will replicate data to data warehouses or data lakes, where they can then use it for data analytics. Self-service platforms like Apache Spark and Apache Kafka are popular among businesses.
Data ingestion and ETL
Data ingestion can also be termed data integration which involves ETL tools for data extraction, transformation in various formats, and loading into a data warehouse. The data transformation process generally takes place in the data pipeline. Earlier, companies used to create and maintain their data pipeline, which demanded a lot of time and effort and a chance of human error. But modern businesses with cloud data warehouses can scale up to handle any processing load. So, they skip preload transformations and carry the raw data into any destination without making any significant change. Data analysts can transform a specific use case and run them in the data warehouse at query time. This new method: ELT, is perfect for cost-effective data replication in cloud infrastructure. ELT techniques allow for the application of transformations at run time and only the relevant data needed for analysis.
5 Benefits of Automated Data Ingestion
A self-service ELT tool will make data ingestion easier and faster. Additionally, it eliminates the trouble of building and maintaining a data pipeline:
- Automated data ingestion enhances self-service analytics, enabling all an organization’s employees to make informed decisions. Self-service data ingestion also makes different kinds of data sources available to the data analysts for better analysis.
- Automated data ingestion is simpler even for non-technical employees. They can easily handle the ETL tool to add or remove data sources and select a destination for data replication. As a result, better business insights will be available in a lesser amount of time.
- Automated data ingestion is a scalable process. The ELT tool used for the process will be able to ingest data as fast as the source API provides and load it as fast as the destination API allows. It will also manage a high volume of transactions when the overall load increases, ensuring the speed of the data pipeline.
- Automated data ingestion helps employees to focus on productive jobs because data professionals do not need to invest time and effort in creating and maintaining custom ETL jobs. Hence, they will be able to focus on improving customer service or optimizing product performance. In many organizations, data engineers build an in-house ETL tool for non-technical users. But that process won’t be faster and will require maintenance periodically.
- Automated data ingestion helps in data profiling and cleansing. Most data warehouses are structurally complex with complicated transformation requirements. Self-service ETL tools provide an elaborate set of advanced cleansing functions which simplifies the data transformation process. As a result, data analysts can complete their analyses effectively and faster.
Daton Simplifies Data Ingestion
Daton is an automated ELT tool that makes data ingestion from multiple sources to data lakes or cloud data warehouses like Snowflake, Google Bigquery, and Amazon Redshift. Employees can use it for business intelligence and data analytics. The best part is that Daton is easy to set up without any coding experience, and it is the cheapest data pipeline available in the market. Sign up for a free trial of Daton today!!
