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Posted By:  Adeptia Inc. on 10/04/2018 in Technology

Key Concepts of Data Ingestion

Key Concepts of Data Ingestion

Data ingestion can be defined as the technique of extracting and importing data for instant usage or storage within a database. The literal meaning of the term ‘ingest’ is to "absorb or take something in." So this technique is all about streaming data in real time or ingesting it in batches.

The process of data ingestion may be any of the following depending on the characteristics of the original source and the destination:

- Incessant

- Asynchronous

- Real-time

- Batched

Under most circumstances, both the source and the destination may not include similar protocol, data timing, or format. This may even need a certain type of conversion or transformation to be efficaciously used by the final destination system.

With consistent growth in the number of IoT devices, there has also been a continuous expansion of volume as well as the variance of data sources. These sources require being accommodated in real time. Still, data extraction is a huge challenge for organizations especially with respect to time and resources.

Extracting the data and moving it to a destination system is a significant challenge in terms of time and resources. Enterprises scramble to make data ingestion efficient. 

Enterprises need to restructure their strategy for efficient data streaming and analysis. A good strategy avoids mundane efforts made towards the preparation and transformation of data.

The idea is to plan and place efforts toward easier data ingestion. It is crucial to convert complex, disorganized, and time-consuming job of moving the appropriate data into a streamlined, efficient, less time consuming, and easier process.

Common Data Ingestion Challenges

Data ingestion becomes complicated, very expensive, and time-consuming with tools that are over-engineered and purpose-built. Another issue is creating customized scripts and merging multiple products together for acquiring and ingesting data which consumes a lot of time and inhibits timely decision making critically required for the present day business environment.

Decision making and access to data are restricted by command line interfaces for prevailing streaming data processing tools that create dependencies on professional developers.

Security of Data

The present security capabilities of transport layer data are not compatible with the demand for sharing discrete bits of data. This further restricts access at the role or group level. It is also very difficult and expensive to adhere to regulations related to compliance and data security. Verifying access to and usage of data is a tough process and consumes a lot of time. It also involves a manual process of piecing varied systems together and reporting to verify the original source of data, its usage, and the frequency of usage.

IoT Data Ingestion Issues

It is quite tough to balance restricted power resources, computing, and bandwidth with data volume signals generated from big data streaming sources. Data loss may be caused due to unreliable connectivity that disrupts communication and causes outages. Businesses and their safety are at risk due to lack of security on deployed sensors of the world.

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