Data Ingestion 101: How Your Business Can Benefit

by Rick Bump

 

 

Defining a data ingestion framework for strategic data management

Most companies have a data problem. Not only is new data being created every second of every day, but there is legacy data stored on CDs, optical discs, or hard drives. Often uncategorized and unorganized, this data might be from an acquisition or merger or was simply put in a box in storage and hasn’t been touched since.

Managing and preserving this data in an organized and secure way is important to business operations. EPA regulations and fines related to the discovery of optical media are on the rise and the EU has been known to give major fines for discovered or abandoned data as part of GDPR. Companies need a way to efficiently and systematically understand what old data exists and categorize it in an effective way.

What is Data Ingestion

To provide a standardized and automated way to read and organize data that is reliable, scalable, and efficient, data ingestion is a set of tools and processes to collect and integrate data from various sources into a centralized repository or data lake.

 

A data ingestion solution makes it easy for businesses to extract, track, and manage unstructured or abandoned data on optical media while also providing secure destruction of optical media that is no longer needed. Data ingestion includes the following components:

 

• Image tracking of the original disc to identify what data is stored on it
• Reliable technology and process to quickly access original data
• Secure destruction of the original discs with a certificate of destruction
• Comprehensive data migration report detailing each file and folder migration to ensure all data is successfully transferred and accessible if needed in the future

 

Data ingestion is becoming even more advanced with the addition of AI. As AI technology is being applied to data ingestion products, it gives the ability to create an advanced summary of what data is stored using metatags. This ability to have an advanced, contextual search option layered onto your storage landscape makes it easy for an organization to quickly find data by project type, case number, creation date, and much more.

 

Data Ingestion Framework

 

Many components make up a data ingestion framework, including data connectors, data parsers, data transformation tools, and data quality checks. These components work together to extract data from its source, transform it into a standardized format, and load it into a target database or data lake. A data ingest framework is a software tool or set of tools that helps organizations acquire, process, and store large amounts of data from different sources. The main functions of a data ingest framework include:

 

• Data Acquisition: Organizations can acquire data from various sources such as files, databases, APIs, sensors, or streaming platforms. It provides connectors and adapters to establish connections with different sources and fetches data from them in real-time or batch mode.

 

• Data Transformation: Once the data is acquired, it is transformed into a format that is suitable for analysis or storage. It can apply various transformations such as data cleaning, data enrichment, data normalization, data aggregation, or data masking.

 

• Data Validation: The data is checked for quality, consistency, and completeness. Data profiling, data verification, or data enrichment can be performed to ensure that the data is accurate and reliable.

 

• Data Storage: Data is stored in a repository such as a data lake, data warehouse, or data mart. It can be partitioned, compressed, or encrypted to optimize storage and retrieval.

 

• Data Governance: Data governance policies such as data security, data privacy, or data compliance are enforced. Access controls, audit trails, or data lineage are applied to ensure that the data is used appropriately and ethically.

 

• Data Integration: Data is integrated from multiple sources into a unified view. Data mapping, data modeling, or data enrichment can be performed to create a comprehensive view of the data.

 

• Data Scalability: A data ingest framework is designed to scale horizontally or vertically as the volume, velocity, or variety of data increases. The workload can be distributed across multiple nodes or the use of resources can be optimized to handle the data efficiently.

 

A well-designed data ingestion framework can help organizations to accelerate their data integration processes, reduce errors and inconsistencies in data, and enable better decision-making through access to high-quality and timely data.

 

Learn more about Rimage’s suite of products that help organizations manage and protect their data.