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Data Ingestion is the process of collecting and importing data from various sources into a storage system for processing and analysis. The collected data is validated, enriched, and transformed to ensure that it is usable by the applications that need it. In general, data ingestion in a data lakehouse environment is used as the first step in the ETL (Extract, Transform, Load) process.
Data ingestion is a crucial part of any data pipeline as it ensures that all necessary data is collected and made available for processing and analysis. With the increasing amount of data being generated by businesses, data ingestion has become more complex and challenging. Data sources range from traditional databases and flat files to semi-structured and unstructured data sources such as social media, logs, and videos.
Data ingestion works by extracting data from one or more sources and ingesting it into a storage or processing system. The process usually involves three stages:
Data ingestion is important because it allows businesses to collect, process, and analyze data from different sources to gain insights and make data-driven decisions. Through data ingestion, companies can gain a better understanding of customer behavior, optimize business processes, and identify trends and patterns to stay competitive in their respective markets. Data ingestion also ensures that all necessary data is collected for compliance purposes and other regulatory requirements.
Data ingestion has various use cases, some of the most important ones include:
Some other technologies and terms closely related to data ingestion include:
Dremio users would be interested in data ingestion because it is an essential part of any data pipeline and is necessary for effective data analysis. Dremio's data lakehouse platform provides a high-performance, self-service, and scalable data infrastructure that enables businesses to easily ingest and analyze data from various sources. With Dremio Data Lake Engine, data ingestion becomes an efficient and streamlined process with improved performance and reduced costs. Dremio's platform provides a unified SQL interface to access data in real-time from any source, including Hadoop, cloud storage, and relational databases, which simplifies data ingestion and processing.