Organizations need fresh data from multiple sources in a single place to gain timely business insights and supercharge their decision-making process. To ensure that, you need to maintain smooth data flow through your data pipelines. Based on your business use case, you can use data ingestion or a data integration pipeline. But what is the difference between data ingestion vs data integration processes?
No worries! We have got that sorted for you. We have compiled a complete list of differences between data ingestion and data integration to help you quickly select the best one for your business.
Table of Contents
What is Data Ingestion?
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Data ingestion is the process of extracting raw data from various sources and loading it into a database, data warehouse, or data lake for further analysis or processing. It acts as the primary stage in developing a data delivery pipeline. Data ingestion is a subset of data integration that focuses little on data transformations, standardization, or quality checks.
Based on the nature of the data being ingested, the volume and velocity of the data, and the requirements for processing and analysis, data ingestion can be carried out using the following techniques:
- Batch data ingestion: Data is collected and loaded in the form of large chunks in regular intervals into the destination. You may face higher data latency as it is not done in real time.
- Streaming data ingestion: Here, the data transfer from source to target happens near-real-time. This method offers data latency as low as milliseconds, which is great for time-sensitive tasks like stock market analysis, industrial sensors, or application logs.
- Hybrid data ingestion: Combining both of the above approaches, this method includes a batch layer and a streaming layer that can easily handle both types of data ingestion requests.
Key Features of Data Ingestion
Applying data ingestion for your business allows you to take advantage of the following salient features:
- Scalability: Data ingestion processes can effectively scale to meet the organization’s needs as the volume and complexity of the data increase.
- Data Variety: It can easily handle a wide range of data types, including structured, semi-structured, and unstructured data from multiple sources.
- Data Security and Privacy: Data ingestion processes ensure the security and privacy of the data being ingested via encryption and support for protocols such as Secure Sockets Layer and HTTP over SSL.
- Centralized Data: You get all your data available from multiple sources in a single destination after applying the data ingestion process.
What is Data Integration?
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Data integration combines data from several sources into a central repository that provides a single and unified view of a business. This can involve extracting data from different sources, transforming it into a consistent format, and loading it into a destination of your choice, such as a data warehouse or data lake.
Based on your business needs, there are several approaches to data integration that you can follow, including:
- Data Consolidation: Combining data from various sources into a centralized data store that acts as a single source of information for the organization with some data latency.
- Data Federation: This involves accessing and querying data from multiple sources in real-time from a virtual database without physically moving the data.
- Data Propagation: It uses applications for data transfer from enterprise data warehouses to multiple source data marts on an event driven-basis.
- Middleware Data Integration: It uses a middleware application to transfer data from multiple applications and source systems into a central repository.
- Data Warehousing: Here, data is replicated from the source and stored in a data warehouse.
- Manual Data Integration: Using hand-coding, organizations can develop their data integration strategies and custom code for organizing and integrating data.
Key Features of Data Integration
A simple data integration offers the following eye-catching features:
- Data transformation: Data integration processes often involve transforming or cleansing the data to make it more usable, such as by standardizing data formats or removing errors or duplicates.
- Better Data Quality: Data quality checks at each stage of the data integration process ensure high accuracy and completeness of the integrated data.
- Data Governance and Security: With established data governance policies and procedures, as well as privacy protocols, your data remains encrypted at all times.
- Flexible: It offers the ability to scale on demand and handle large volumes of data efficiently and in a timely manner without affecting performance.
Data Ingestion vs Data Integration: Key Differences
When comparing data ingestion vs data integration, data ingestion quickly transfers data from your data sources to a single source of information. In contrast, data integration applies many transformations, providing data in an analysis-ready form. Let’s check out the complete list of differences between data ingestion vs data integration:
| Data Ingestion | Data Integration |
| Refers to the process of importing data from various sources into a central repository, such as a database or data lake, for storage and further analysis. | It involves extracting data from different sources, transforming it into a consistent format, and loading it into a central repository, such as a data warehouse or data lake. |
| It doesn’t automatically maintain data quality as it focuses on efficiently moving large volumes of data into a central repository. | It places a greater emphasis on ensuring the accuracy and consistency of the data by performing tasks such as data cleaning, merging, and filtering. |
| Data ingestion pipelines focus only on replicating data with little to no data quality checks. Hence, they are fairly simple when compared to data integration pipelines. | As the data integration process involves multiple data quality checks, data cleansing, ETL, metadata management, governance, etc., it becomes quite complicated to develop and maintain. |
| Since it is not a complex process, it doesn’t require much expertise, and your engineering team can develop it much faster. | Data Integration ETL/ ELT Pipelines need proper planning, expert data engineers, and a lot of time and effort to write custom scripts. They have to monitor for any data leakages continuously and maintain a smooth data replication process. |
Key Takeaways
After going through the differences between data ingestion vs data integration, you can now easily pick the one right for you. If you only need to replicate data from various sources to a central hub, then data ingestion is the way for you. However, if you also need to ensure data quality and need analysis-ready data in your data warehouses, then data integration will do the trick!
The second step would be to ask your engineering team to build custom data connections and the pipeline for you. If you rarely need data transfers, and that too from a couple of sources, then this is an effective choice. Whereas if you frequently need complex raw data from multiple sources to be transformed in usable form into your data warehouses, then you can simply use no-code cloud-based ETL tools like Hevo Data, which offers 150+ plug-and-play integrations.
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Saving countless hours of manual data cleaning & standardizing, Hevo Data’s pre-load data transformations get it done in minutes via a simple drag n-drop interface or your custom python scripts. No need to go to your data warehouse for post-load transformations. You can simply run complex SQL transformations from the comfort of Hevo’s interface and get your data in the final analysis-ready form.
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