The last decade saw an unprecedented rise in the number of organizations that base their decisions and operations on data. The number of digital products that collect and process data and use it to fuel decision-making algorithms for enhancing future services is also growing at a very fast pace. That’s why data and data quality has become the most valuable asset for organizations across practically every sector, from automotive to retail.
Over the years, these companies developed new data management capabilities using various specialized tools. Data engineering ecosystems are now based on solutions such as data lakes and data pipelines that allow storing and analyzing data securely and cost-effectively.
Despite the many advancements around data tools and methodologies, engineers are still facing a messy and cumbersome process that leaves a lot of room for optimization.
Companies looking to unlock the value of their data – especially ones where data scales fast due to rapid growth – can reap many benefits from better management of their data engineering operations.
As data scales, so scales the overhead
Data engineering is a hybrid role that emerged from the need to store, organize and integrate data. Its original purpose was to support business intelligence and database maintenance, but it has expanded over time to include handling large data sets and implementing machine learning algorithms.
Data engineers work with more data than ever before, facing poor machine performance or legacy ETL technologies and struggling to keep data pipelines in shape.
Here are a few problems most data engineers experience today:
- Validating data quality and consistency before it goes into the lake is challenging – unlike for code, engineers don’t have staging or QA environments for data. Everything gets streamed into the lake, including potential bugs.
- Engineers can’t test and debug new data sets in isolation – whether it’s in the pre-production phase, deployment, or final QA before landing in front of the end-users, data doesn’t get its dedicated environment for testing. All of it goes into one lake.
- Troubleshooting poses many problems – data engineers don’t have an easy method for detecting, analyzing, and troubleshooting issues in production.
- Resilience to changes is hard to achieve – engineers also lack a straightforward way to create data pipelines that are resilient to changes in data and code.
- Lack of version control – there are no effective version control tools that allow for a rollback in case of issues with the data. Engineers can only dream about reverting production changes automatically.
As you can probably tell, a lot of data engineering work is based on manual heavy lifting. Unlike software developers, data engineers don’t benefit from a rich choice of automation solutions that remove the need for low-level grunt work and eliminate errors. And let’s not forget that the cost of an error is pretty high, often preventing organizations from achieving velocity.
Is there a way out? You can find it just around the corner – in any modern software development team that runs Git-based operations.
Transitioning to Git-like operations to manage data at scale
The good news is that all these problems have already been solved on the application side. In a typical development team, you have multiple developers contributing to one repository without any misunderstandings. At the same time, different users use different versions of the software, but developers can easily reproduce a user error by using their exact version.
This is what DataOps tools are all about. They bring battle-tested industry practices from the world of software development to data.
Managing data just like you manage code makes a lot of tasks more efficient from the data operations perspective:
Data branching and versioning
Having different data versions available gives you a very clear version history from a lineage perspective. Engineers can easily follow versions of their repository or datasets and point customers to newly deployed data.
If you expose users to data in production and something happens, you can always roll back to the previous version in one atomic action.
Imagine running into a data quality issue that causes a drop in performance or spike in infrastructure costs. How to fix this bug? If you have versioning, you can open a branch of the lake from the specific part that introduced the changes to production. You can reproduce all aspects of the environment and the issue itself to start troubleshooting using the metadata.
Version control systems let you configure actions to trigger when predefined events happen. For example, a webhook can check a new file to ensure that it follows one of the allowed data formats.
By using a DataOps tool, you no longer face the typical issues haunting large data engineering teams working on the same data. And when you run into a problem, troubleshooting it is much faster.
A good example of that is the open-source tool lakeFS that allows engineers to manage data like code, benefitting from all the best practices and Git-like operations used by software developers today.
Scaling of data usually comes hand in hand with business growth, and it’s a sign of prosperity. However, data quality and consistency should also evolve to support this scaling process.
Organizations that make conscious investments into their data engineering ecosystems will set themselves on the path to success and transform data into a competitive advantage.
The lakeFS project is an open source technology that provides a git-like version control interface for data lakes, with seamless integration to popular data tools and frameworks.
Our mission is to maximize the manageability of open source data analytics solutions that scale.
Read Related Articles.
Big Data Testing: How To Test Data Pipelines In The ETL World
When testing ETLs for big data applications, data engineers usually face a challenge that originates in the very nature of data lakes. Since we’re writing
Acceptance Testing For Data Pipelines: Expert Guide
What’s not to love about data pipeline testing? Adding acceptance tests to your data pipelines makes them less likely to make mistakes and makes sure
Data Lake Governance at Scale with lakeFS
No time for the full article now? Read the abbreviated version here Introduction Often, data lake platforms lack simple ways to enforce data governance. This