How Smart is Your Data Ingestion Process?
By Amalendu Tiwari
What is Data Ingestion?
Data ingestion can be thought of as the “entry point” for data into a system, where the data is “welcomed” and transformed into a format that is suitable for further processing and analysis. Think of it like a bouncer at a club, carefully screening and admitting only the guests who meet the criteria for admission, while turning away those who don’t fit the bill.
In the world of data, this process involves filtering out unwanted data, transforming the data into a standardized format, and enriching it with additional context or metadata. This ensures that the data is clean, accurate, and ready to be used by data scientists, analysts, and other stakeholders for insights and decision-making.
So basically, data ingestion is the crucial first step in the journey of data, where it is carefully guided into the system, groomed, and prepped for its ultimate purpose.
That brings us to the data ingestion pipeline.
What is a Data Ingestion Pipeline?
A data ingestion pipeline is a collection of processes working together to gather data from disparate sources, harmonize it, and transform it into insights that can drive business decisions.
The pipeline consists of multiple stages, with each stage performing a specific function in the data processing workflow. The first stage is data extraction, where data is gathered from sources such as databases, APIs, or data streams.
Next, the data is transformed into a standardized format, enriched with additional context or metadata, and cleaned to remove any inconsistencies or errors. Finally, the transformed data is loaded into a storage system where it can be easily accessed and analyzed.
Powering the Data Ingestion Engine
When you take a close look at how a poor data ingestion pipeline can negatively impact your content BI/large-scale system, it’s clear that the ingestion engine is where much of the time and effort goes.
A data ingestion engine is a software component that is responsible for executing data ingestion pipelines. The engine typically includes scheduling capabilities, fault tolerance, and error handling, and is designed to run pipelines at scale. The goal of a data ingestion engine is to provide a reliable and scalable way of executing data ingestion pipelines, enabling organizations to process large volumes of data quickly and efficiently.
Now, not all ingestion engines are the same. Some are smarter than the rest. In fact, you can immediately tell the difference between a good ingestion engine and a not-so-good one by asking yourself a few crucial questions:
- How much effort is currently spent on integrating a new source file to your existing load engine?
- How many places in your code would you need to make changes, if the source file path/connection string changes?
- The same as above, but for archiving the path or landing zone. How many areas would need to be changed based on this information?
- How often does a change in the schema of incoming data, break your load?
- Are the business rules for accepting/rejecting records stored in a referenceable format or is it directly implemented into the load engine?
- If a load fails how much effort is required in rolling back the inserted data?
If any of your responses included “a lot of effort” or “quite often”, then we have some not-great news for you – it’s time for an audit where you’ll need to take a much deeper look into your data ingestion framework.
One way to ensure efficiency in your data ingestion is to adopt a metadata-driven approach. With this, a good data ingestion framework should be fast, mostly self-managed, and should have repeatable processes. The standard characteristics of source, destination, and load should be well documented and utilized in the framework.
What do I mean by this? Let’s see some examples.
Source metadata: This is where we capture the characteristics of the source system(s). Some common metadata include:
- Source type (file, database, application, etc)
- File Details (Type -CSV, JSON, Parquet, Full file path, Delimiter type, etc)
- Source data owner/contact
- Attributes metadata (data type, length, nullability, etc)
Destination metadata: While it is very similar to source, this is where we capture the characteristics of the landing zone. Some common metadata include:
- Destination type (most likely a database)
- Destination Details (Connection details etc)
- Data owner/contact
- Attributes metadata (data type, length, nullability, etc)
Business rules: There are always different customer data scenarios and requirements. Here we capture the business rules on accepting/rejecting rows.
- Which records need to be accepted/rejected (id missing, invalid records, etc)?
- Which attributes need to be replaced for null values?
- Which attributes need to be within a valid range (e.g., phone number should be 10-digit numerical, etc)
- What is the threshold number of records for rejecting a load? (e.g., if over 10% of records are invalid then reject the file)
- Which team/BUs should be contacted for confirming invalid records?
What are the benefits of Metadata-based ingestion?
Uniformity: A metadata-driven framework approach yields a uniform generic way of data ingestion. It makes it very easy to review existing configurations and/or add new configurations, by understanding the ingestion pattern.
Agility: Updating the metadata is all you need for making changes to load behavior.
Easy to scale: The ease to scale is demonstrated by the ease of adding new sources, configurations, environments, etc. just by merely creating meta-data.
Maintainability: Since everything from business logic to data flow is in form of excel documents, it’s very easy to maintain this approach.
Acceleration: Extract, transform, and load (ETL) Frameworks do not need to replace one’s existing ETL platforms. It might help to assist as an accelerator or code generator for rapid development in the native ETL platform of choice. For instance, the Framework can be used to generate custom factory templates of XML’s which can be imported into Informatica custom repositories to generate ready-made ETL from the framework.
*ETL is a type of data integration that uses tech tooling to transform and move data while drawing from its source (database). ETL adds significant value to your data reporting, and BI strategy/solutions (Business Intelligence) by improving data flow processes.
If you’re building a new ETL engine then it is important to get off on the right foot by investing sufficient time and effort into building a proper ingestion framework. If you’re migrating legacy ETL then it is equally important to (re-)design the metadata-based ingestion. The long-term benefits of this approach outdo the short-term cost savings by a huge margin. ETL is the smart way.
The Art of Smart Data Ingestion
Maximizing your data’s potential starts with a smart data ingestion process. A well-designed pipeline, fueled by an intelligent ingestion engine, efficiently gathers, harmonizes, and transforms data from diverse sources.
With effortless integration of new sources, adaptability to handle changes, and robust rollback mechanisms, a smart engine goes beyond the basics. Leveraging a metadata-driven approach ensures uniformity, agility, scalability, maintainability, and acceleration.
Embracing a clever data ingestion process lays the groundwork for an intelligent data infrastructure, unleashing the full potential of your data for decisive insights and business growth.
If you’re not sure how to get started, read more about our Business Intelligence Consulting Services.
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