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Data Ingestion Pipeline: Best Practices to Design It

May 10, 2022

10 min read

Summarize with AI:

Data ingestion may just be the most important step in the ETL/ELT process. After all, you can’t do any transformation without data! Data ingestion pipelines connect your tools and databases to your data warehouse, the hub of your entire data stack.

The processes you set up to ingest data into your warehouse set the standards for all other processes within your data team. Your transformations and analyses that follow are only as good as the quality of data you ingest. This is why it is imperative that you follow best practices from the start, documenting them along the way.

When you document best practices from the beginning, you are forced to follow a set structure. Without this structure, things can start to get sloppy. You create a new data connector that doesn’t follow the naming convention you wanted, but you already paid for the data to be ingested and it is too late to change it.

You sign a contract with an ingestion company who doesn’t offer integration with the connector most important for your business. These are all nightmares of the modern data stack that you will avoid. So, what are the best practices to create a data ingestion pipeline? In this article we're going to share the best practices that we've learned while building out a data stack from scratch.

What is a Data Pipeline?

A data pipeline is a structured framework or process designed to efficiently move, transform, and manage data from its source to a destination, typically a data warehouse or analytics platform.

Key Characteristics of a Data Pipeline

Workflow Automation: Automates the flow of data, reducing manual intervention and human errors, thereby enhancing operational efficiency.
Modular Design: Comprises distinct stages, such as extraction, transformation, and loading (ETL), each optimized for specific tasks, allowing for easier maintenance and scalability.
Scalability: Built to handle large volumes of data, ensuring seamless performance and reliability even as data volumes grow over time.
Data Quality Assurance: Incorporates mechanisms for data validation and cleansing, ensuring that the data is accurate, consistent, and reliable for analysis and decision-making.
Real-Time or Batch Processing: Offers flexibility to process data in real-time for immediate insights or in batches for periodic analysis, catering to diverse business needs and requirements.
Flexibility: Adaptable to different data formats, sources, and destinations, providing versatility and ease of integration across various systems and platforms.

Benefits of utilising a Data Pipeline

Unlocking Business Potential with Data Ingestion Pipelines

Efficient Data Extraction: Streamline extraction from diverse sources, saving time and resources.
Enhanced Data Quality: Cleanse and validate data for more accurate insights and decision-making.
Real-Time Processing: Stay agile with quick and responsive data processing capabilities.
Centralized Integration: Simplify data integration across systems for better collaboration and consistency.
Scalability: Handle growing data volumes effortlessly, ensuring seamless growth and adaptability.

What are the types of Data Ingestion?

Understanding different data ingestion methods is crucial for efficient data management. Let's explore:

Batch Ingestion: Process data in predefined batches at scheduled intervals.
Real-Time Ingestion: Immediately process data as it becomes available, ideal for up-to-the-minute insights.
Incremental Ingestion: Capture only new or modified data since the last ingestion, reducing redundancy.
Streaming Ingestion: Continuously ingest and process data in real-time, suitable for instant insights.
Parallel Ingestion: Distribute ingestion tasks across multiple nodes simultaneously for faster processing.

Data Ingestion Pipeline Best Practices

Let's begin to understand and uncover the top-notch industry proven advices to streamline a swift designing of a data pipeline. These practices include choosing an ingestion tool, documenting sources, orchestration, testing, and monitoring. By sharing what we've done in the past, you can learn from both successes and the failures before embarking on the journey yourself.

We will dive in-depth to explain & learn about each of the below mentioned practice:

Compare data ingestion tools
Document your data ingestion pipeline sources
Keep a copy of all raw data in your warehouse at all times
Run syncs and models synchronously
Create alerting at the data source

1. Compare data ingestion tools

When we first embarked on deciding on the tools we would use for our data stack, the manager at that time had some awesome systems in place. When we first stepped into analytical engineering, never having worked full-time for a startup. It was just learning the ropes! Luckily, had a lot of experience in deciding on the right tools and putting frameworks together.

Had not put together a fancy Excel spreadsheet to help compare data ingestion tools, we were deciding between, who knows where our ingestion would be today. So, what exactly was this framework for deciding on an ingestion tool?

It was basically a scorecard with the three different categories we mentioned in the title- must-have’s, nice-to-have’s, and dealbreakers. Then, under each category were certain qualities/features which we scored 1-3 depending on the tool’s capabilities.

There are a lot of important things to consider when you design your data ingestion pipeline, here are just a few that we included:

Data connectors available (Shopify, Azure Blob, Facebook, NetSuite, etc.)
Capabilities of your team (time to set-up, time to maintain, skillset, etc.)
Budget (monthly costs to ingest expected volume of data)
Support (Slack communities, dedicated support agents, etc.)

These are all critical to deciding on the right ingestion tool for your team. You may not find one tool that satisfies all of your needs but you do want to find the one that satisfies most of your needs. If you can’t find what you are looking for in one tool, strategize on how you can use a few different ones and still achieve the same results.

For example, maybe you have an analytics engineer who can set up and maintain connectors for most of your sources with Airbyte. However, there are a few that don’t have integrations so you need to have your engineering team build something out.

2. Document your data ingestion pipeline sources

We can never stress the importance of documentation enough, especially if you work on a small data team. Documentation is your lifeline when something goes wrong and the person who owns it is sick, on vacation, or left the company. Everything in your modern data stack should be documented to the point where anyone else on the team can take it over in a time of need. The same goes for data ingestion.

Make sure you keep a document of the various ingestion tools you are using and the connectors you have set up within that tool. This should also include newer sources such as UGC platforms, which generate large volumes of unstructured contentIt can be easy to lose track of where your raw data is coming from if you don’t do this. In addition, note any special requirements or changes you had to make for that connector to work.

For example, for one of our connectors we had to enable change data captureon the database in order to properly capture the data. Write these things down so it’s not a guessing game for your team.

3. Keep a copy of all raw data in your warehouse at all times

We wrote about this extensively in Snowflake Data Warehouse Architecture: How to Organiza Databases, Schemas and Tables, so be sure to check that out if you haven’t. One of the most basic best practices of data ingestion is the protection of your raw data. Your raw data should always be in a separate database in your data warehouse. No transformation tools or people should have write access to this database. It should be strictly read-only.

This is the database that will ingest all of your raw data from different sources. This database will act as a “backup” of all of your data in case something goes wrong in your data modeling. Let’s say you accidently run a data model that deletes random rows in your table and mixes column values.

You can no longer trust that data. You don’t want to delete it because then it is gone forever, but you can’t use it otherwise. You can delete all of the data saved in your data models and re-run the models by referencing your raw data sources. Raw data to the rescue!

4. Run syncs and models synchronously

Depending on how often you run your data models, your raw data should always be synced to your warehouse right before your models are run. Whether you are using dbt for your transformations like we, or something else, one transformation should occur right after the other.

This ensures that your dbt models that depend on one another aren’t running in parallel, giving inaccurate results in different analyses. By running syncs and dbt models synchronously, you also allow for more accurate validation of your data.

Luckily, data ingestion tools like Airbyte have been partnering with powerful deployment tools like Airflow, Prefect and Dagster. Personally, we use Prefect to deploy the data models. They make synchronicity easy by offering “Airbyte Connection Tasks” which allow you to sync your data connectors directly within your data pipeline.

By syncing data directly within your pipeline, you can create dependencies downstream between models and these syncs. This means you will always have the most accurate and fresh data in your data models.

You simply specify where your Airbyte server is hosted, the post, api version, and connection id. It’s as simple as that! Then, you use these tasks within your Prefect flows and set them as upstream dependencies for your dbt model runs. You can read this tutorial to create a data ingestion and tranformation pipeline with Airbyte, Prefect, and dbt.

5. Create alerting at the data source

It is imperative that you create data alerts and testing at the source. Many people think testing and alerting should be done on the final data model, but really it is quite the opposite. When you focus on the downstream rather than the upstream, it becomes a 10,000 piece puzzle that you will never solve. You’ll have to do LOTS of digging just to find the problem was never actually the data model.

Trust me, we know because we’ve been there. When we had all of our data models written within a data visualization platform, it made it impossible to debug intermediate models. We had to spend weeks investigating a problem that turned out to be created at the data source itself. Your first alerts you ever build should be at the data source level.

If there is an issue with your data source you need to know right away before it impacts any downstream data models. Not to mention, it is much easier fixing issues with your data source than it is poking around your data models, testing every line of code to see if it’s performing as expected.

Errors at your data source can be as simple as fixing a human error in a spreadsheet, updating a credential, or retriggering a cron job. They are much more straight-forward than when there are large amounts of code involved.

Our favorite way to set up testing and alerts at the source is using dbt tests and re_data. With dbt tests you can check for null values, primary keys, and expected values. You can use these simple tests to ensure your data looks as expected. re_data is a dbt package that allows you to monitor metrics like freshness, row_count, and descriptive statistics of your data.

You can even set up Slack alerts to tell you when a metric is outside it’s typical z-score. Slack alerts in themselves are powerful. We are all on Slack every day to communicate with our company and team. Getting data alerts directly to a #data-alerts channel makes it so you don’t need to parse through your emails or manually visit a dashboard every morning. If something is wrong, your tools will tell you via Slack. Make sure you always set them up!

Data Ingestion: Challenges vs Solution

Navigating the complexities of data ingestion involves overcoming various challenges while implementing effective solutions is crucial for ensuring smooth and efficient data management processes. Let's explore some common challenges in data ingestion and corresponding solutions to address them effectively.

Navigating Challenges in Data Ingestion

Data Variety: Managing diverse data formats and structures from multiple sources poses a challenge for seamless ingestion.
Data Volume: Handling large volumes of data efficiently requires robust infrastructure and optimization strategies.
Data Velocity: Keeping up with the speed at which data is generated and processed, especially in real-time scenarios, demands high-performance data ingestion solutions.
Data Quality Assurance: Ensuring data integrity and accuracy during ingestion is crucial for reliable analysis and decision-making.
Data Security: Safeguarding sensitive data during ingestion processes against breaches and unauthorized access is paramount for compliance and trust.

Solutions for Seamless Data Ingestion

Unified Data Integration Platforms: Utilizing comprehensive platforms that support various data formats and provide integration capabilities streamlines the ingestion process.
Scalable Infrastructure: Investing in scalable infrastructure, such as cloud-based solutions, enables handling of large data volumes efficiently and cost-effectively.
Streamlined ETL Processes: Implementing efficient extract, transform, load (ETL) processes automates data ingestion, ensuring timely and accurate data delivery.
Data Quality Checks: Integrating robust data validation and cleansing mechanisms ensures high-quality data ingestion, enhancing the reliability of downstream analytics.
Encryption and Access Control: Employing encryption techniques and access control mechanisms secures data during ingestion, mitigating risks of data breaches and un-authorized access.

Conclusion

Following data ingestion best practices from the beginning stages of your data stack will set your team up for future success. Documentation of these best practices will make requirements clear from the very start, leaving little room for things to go wrong. When high standards are set for one piece of the puzzle, it is only bound to trickle down to every other piece.

To reiterate, remember the following when deciding on an ingestion tool and creating a data ingestion pipeline it into your stack:

Write down all of the most important connectors for your business, the skills and time your team has to offer, and your budget when deciding on the right ingestion tool.
Document all of your data sources and how they are being ingested into your data warehouse, including any special setup.
Always keep a database with your raw, untouched data.
Run data syncs and models synchronously so there are no gaps in data.
Create alerts at the data sources rather than downstream data models.

While this may be adding more work to your plate, following these best practices now will only save you trouble down the line. You are proactively solving for when something in your data ingestion pipeline breaks, finding a solution now before it negatively impacts the business. It is always better to think about these things from the start rather than when reacting to things going wrong.

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About the Author

Madison Schott

Madison Schott is an Analytics Engineer, author of The ABCs of Analytics Engineering book. Madison blogs about the modern data stack on Medium and writes a newsletter on Substack.