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Einat Orr, PhD
Einat Orr, PhD Author

Einat Orr is the CEO and Co-founder of lakeFS, a...

Last updated on June 27, 2024

The software engineering world has profoundly transformed in the past decades. This was possible thanks to the emergence of methodologies and tools that helped establish and apply new successful engineering best practices. 

The leading example is the move from a waterfall software development process into the concept of DevOps: At each moment, there is a working version of the software that can be instantly shipped and deployed to end customers. 

This approach, also known as the DevOps approach, became the leading way for software to be developed thanks to the development of frameworks and tools that enabled this change. In this we refer to tools such as Git – which enabled collaboration between teams and continuous development, Jenkins – which enabled continuous software integration, Docker – which enabled easy testing and shipment of the software to its consumers and many more.

Source: PagerDuty

These tools enabled the DevOps approach to take over software development processes, with CI/CD being a key ingredient: Continuous integration and continuous deployment of the software.

Source: Synopsis

What is data engineering?

Data engineering is the field that develops data collection, storage, transformation, and analysis methods for vast volumes of raw data, structured data, semi-structured data, and unstructured data, allowing data science specialists to get important insights from it.

Data engineering includes data quality and access assurance. Before loading data and initiating data processing processes, data engineers must ensure that data sets from various data sources, such as data warehouses, are complete and clean.

Furthermore, they must guarantee that data consumers (such as data scientists and business analysts) may readily access and query the prepared data using data analytics and data scientist-preferred tools.

How data engineering world changed over time

The world of data engineering has undergone quite a few revolutions over time as well, and tools and technologies emerged in order to cater for the growing needs of this domain.

Source: lakeFS

Some of the changes this domain has undergone are:

  • The emerging variety of data types that need to be stored led to the move from tabular, structured data to unstructured data and data storage. The growing demand for AI in almost every digital application drove the need to develop complex algorithms and analytics with compute and analytics engines.
  • The variety of sources and the need for infinitely fast distributed data ingestion led to the development of advanced technologies that enable ETLs and streaming from every data source one could imagine.
  • The need to develop and maintain complex AI algorithms for production systems at scale led to the emerging world of AI and ML tools that enable operationalizing the algorithm development process as much as possible. These are only some of the advancements this field has seen.

What is missing in data engineering?

Despite all these technological advancements, data products are still slow to develop, ship, and maintain. Something is still missing in data engineering: the ability to enable the rapid development of data products at a scale similar to that of software product development.

In software engineering, this was the DevOps approach that made the difference. It broke the engineering silos between teams. It ensured there was always a working quality version of the software ready to be shipped to consumers. It also enabled the fast deployment of these software products into the hands of consumers, closing the rapid feedback loop.

Then, what is missing in data engineering? Can it be that by understanding these best practices and applying them to data engineering, we can revolutionize the entire process of developing data products? We argue that this is indeed the case.

15 Data Engineering Best Practices to Follow

What is required from a data engineering team to rapidly build and ship quality data products? What principles are at the heart of the software engineering best practices framework and should be adopted by data engineers as well?

Working with hundreds of data teams worldwide, listening to their pains and challenges, helped us establish a list of assumptions and behaviors that can serve as the foundation for data engineering best practices. Here are the 15 data engineering best practices:

1. Adopt a data products approach

A data product is any tool or application that processes data and generates insights. These insights help businesses make better decisions for the future. Stored data can then be sold or consumed by users internally or by customer organizations, which process the data as needed. 

To adopt a data products approach, we need to apply the following principles:

  1. Product Management Methodology – including the people and processes that are in charge of building the definitions, requirements, and KPIs
  2. Appropriate product delivery methods – including all the other engineering best practices that are required to deliver quality data products continuously
  3. Measurement and improvement processes – including relentless monitoring and validation of data quality, in all its aspects, and SLA agreements that include not only the interface’s availability but also the data’s freshness.

2. Collaborate while building data products

This is very much like the case in software engineering. Teams that develop, test, ship, and maintain complex data products are usually composed of several team members and members from different teams that consume and change the data.

This requires the team members to collaborate and contribute to each other’s work, but nevertheless – keep their ability to work independently at their own pace. This requires tools that enable safe development in an isolated environment and the ability to merge each other’s work together continuously so there is always a working version ready to be consumed.

3. Be resilient with quick recovery from errors

Resilient, high-quality products are usually not born this way. Errors and bugs do happen, even in the most experienced teams. Rapidly shipping high-quality data products means fast root-cause analysis, followed by fast recovery from quality issues and fast deployment of fixed versions.

To enable this, we need a system that enables us to identify root causes as quickly and easily as possible and test and deploy a fix just as fast – or, in other words – reproducibility during development and testing

4. Enable continuous delivery of quality data products with CI/CD for data

Solving data quality issues in data products is very important, but being able to continuously ensure the quality of these products is the best practice to achieve fast development and deployment cycles. This is the way data engineers can detect and prevent errors before they even appear.

To achieve that, software engineering applied the concepts of hooks and pre-merge validations. This can and should be applied to data as well by creating hooks that test the new data before it becomes production data and preventing erroneous data from becoming part of production. This concept is at the core of the CI/CD approach, which, with the right tooling, can and should be applied to data.

5. Leverage data versioning

Data versioning is a core enabler for the best practices we listed above. It refers to a system that holds versions of the data that are created with every change that is applied to the data.

This enables collaboration in teams because each member of the team can get a branch – their own copy of the data – to safely develop and test without impacting the work of the other team members. It allows for reproducibility because data engineers can always travel time to the version of the data as it was at the time of failure.

And it ultimately enables CI/CD because whenever new data is generated, a new version of the data is created and tested, and if it fails to pass a certain quality test, it does not become the main version until the issue is resolved.

Data versioning needs an appropriate tooling – Data Version Control

Data versioning is indeed a convincing concept, but without appropriate tooling, it will stay at the conceptual level. With tools and services that provide capabilities of branching data to work in isolation, time travel to enable reproducibility, and hooks to enable full CI/CD on the data – this entire mode of work comes to life. Data version control tools are the implementation of the data versioning methodology for data engineering, and there are various such tools which you can choose from.

The standardized approach to version control is git

The most straightforward approach for a data version control system would be git, since it created and established a standard taxonomy to approach version control capabilities. The git interface allows straightforward actions for all of the above-mentioned features: branching, merging, and moving between versions and hooks.

Its UI has become very intuitive for developers and is easy to integrate into almost any existing stack. Several solutions provide git for data—they vary in features such as scalability, supported file formats, support for tabular and unstructured data, the volume of data supported, and more.

6. Design efficient and scalable pipelines

Efficient data pipelines are critical for big data because they allow enterprises to manage the size and complexity of data successfully.

Efficient pipelines guarantee that data travels smoothly from its source to the point of analysis while preserving its integrity, quality, and relevance. This enables teams to gain precise information, make sound decisions, and adapt quickly to market developments.

7. Automate data pipelines and monitoring

Ensuring data flows through the pipeline is critical to the organization’s capacity to consume the data. Today’s data engineers are overwhelmed with cleaning and fixing data, debugging pipelines, upgrading pipelines, managing drift, ensuring the pipeline’s technologies work well together, and other data-related responsibilities. As a result, they devote a lot of time to tiresome jobs. Data quality might suffer as a result.

Automation improves efficiency and productivity by reducing the effort necessary to transport and process data in the pipeline and update data columns. Without having to execute these data-related activities manually, the process can be speedier and involve less manual work, resulting in increased efficiency and fewer data point mistakes.

Standardization is the result of automation and a key enabler in any data engineering process. Standardizing how data is transferred through the pipeline, regardless of source or format, decreases the danger of mistakes, oversights, and drift. This makes the data more consistent, accurate, and up-to-date, improving quality.

Automated data pipelines are simpler to scale because they can be built to scale horizontally or vertically in response to workload needs, and resources may be optimized for efficiency. This enables the pipeline to readily accommodate rising data quantities and processing demands without needing considerable manual intervention or reconfiguration.

8. Keep data pipelines reliable

Consider your evolving data requirements. Honestly assess your present and future requirements and compare them to the capabilities of your current architecture and data processing engine. Don’t let outdated technologies restrict you; instead, look for ways to simplify.

How many distinct services do you have operating in your data stack? How easy is it to obtain data from these services? Do your data pipelines need to work around barriers between distinct data silos? Do you have to repeat efforts or use several data management utilities to achieve proper data protection, security, and governance? Determine which procedures require an extra step (or two) and what it would take to simplify them. Remember, complexity is the enemy of keeping your data pipeline reliable.

9. Avoid data duplicates with idempotent pipelines

Designing your pipelines for self-healing using idempotence and clever retries is a good approach. Retry policies mitigate transitory problems, such as temporary network outages, by resubmitting unsuccessful tasks after a predetermined number of tries with backoff delays. This guarantees that temporary failures do not disrupt the entire pipeline.

Idempotence ensures that an operation yields the same result even when repeated several times owing to retries. This is accomplished using approaches such as maintaining track of processed data IDs and utilizing database transactions. Together, these techniques ensure that data pipelines are fault-tolerant, gently managing mistakes and preventing unintentional duplicate data insertions.

10. Enable data sharing for your data pipelines

One of the best ways to share knowledge about data pipelines is to properly and consistently document them. Each data pipeline should be documented with its purpose, design, inputs, outputs, dependencies, assumptions, limits, and performance metrics.

The documentation should also provide instructions for running, testing, monitoring, and troubleshooting the pipelines and how to access and use the data products. Documentation with a consistent structure and style should be easy to discover, read, and update.

11. Ensure data quality

Ensuring data quality inside a data pipeline is about more than just correctness. It entails ensuring that data is comprehensive, consistent, trustworthy, and timely as it progresses through the collection, processing, and analysis phases.

Poor data quality can result in incorrect judgments, sluggish work, and missed opportunities. For example, full data sets may result in biased analytics, whereas consistent data might lead to consumer misunderstanding and mistrust.

Furthermore, the speed with which data is processed and made available for decision-making, also known as timeliness, is critical in fast-paced commercial contexts where real-time data quickly becomes the standard. Understanding these aspects of data quality is the first step toward ensuring that your data pipeline is more than simply a conduit for data but also a dependable source of meaningful business insights.

Before improving the quality of your pipeline’s data, you must examine its current status. Begin by reviewing the data to ensure correctness, completeness, and consistency. Use tools to examine data trends, detect anomalies, and highlight data that deviates from established standards.

12. Embrace DataOps

Data management is not a one-time project, but rather a continuous process. It entails constantly gathering, processing, and analyzing data in order to uncover patterns, trends, and important insights for making educated business decisions.

Companies that manage DataOps as a continuous process can increase their agility and responsiveness to internal and market changes. This calls for a culture shift toward data-driven decision-making, which involves breaking down conventional silos and encouraging cooperation across teams such as data scientists, developers, and business analysts.

The objective is to collaborate to identify needs, create pipelines, and verify that data is correct, timely, and relevant – whether it a data lake or data warehouse. By doing so, the organization may foster a culture that views data as a strategic asset, resulting in improved business outcomes.

13. Focus on business value

Data engineers, as a catch-all data function in many businesses, are accustomed to rolling up their sleeves and getting things done. They create the underlying infrastructure and pipelines that allow data to be delivered smoothly to downstream consumers while also laying the foundation for analytics. One of the core data engineering tasks is allowing for effective data analysis. And the more data engineers learn, the better the business results.

14. Maintain documentation and proper naming convention

Establish data catalogs and dictionaries that explain the metadata and semantics of your data sources and deliverables. Data catalogs and dictionaries help consumers locate, understand, and trust the data generated by your pipelines. Data catalogs and dictionaries should be searchable, interactive, and up-to-date, with a consistent schema and terminology.

15. Set security policy for data

A data security policy is a set of principles, regulations, and standards that businesses use to manage and safeguard their data assets. It establishes a framework for ensuring that data is handled, stored, communicated, and accessed in a manner that protects its confidentiality, integrity, and availability. The primary purpose of such policies is to prevent unauthorized access, use, disclosure, change, or destruction of data while adhering to applicable laws and regulations.

To guarantee that the data security policy is properly executed, the business must create an action plan that includes staff training and awareness programs, integrating security measures into day-to-day operations, and implementing monitoring and enforcement methods for policy compliance. Regular audits and reviews are carried out to evaluate the efficacy of current controls and suggest opportunities for improvement.

The future of data engineering

Efficiency, adaptability, and accessibility are critical pillars for most data engineers .
As more effort is devoted to self-service analytics, the gap between data consumers and data producers will narrow. Tools that assist teams in consolidating their understanding of data will become required for all data teams.

Data teams will increasingly use product-like processes for measuring, managing, and developing data. This might include shifting to data tools that provide cross-organization collaboration, version control, and monitoring. We feel that innovation in the field of data analytics will be exciting.


Adopting and applying proven best practices from software engineering can help the world of data engineering keep pace with the rhythm that is needed in digital products. As soon as organizations start to shift into a continuous integration and delivery mindset, with the necessary cultural and behavioral changes, we will start seeing smarter digital products powered by resilient, high-quality data products.

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