In recent years, there has been a surge in both the volume of data generated every day and its role in firms and organizations.
This has led to the rise of a data-driven culture, with more firms basing their operations and management on analytics and objective insights rather than intuition and going by their gut.
In practice, this is done by processing the vast volumes of data at your firm's disposal and using it for planning, strategizing, and driving workflows, processes, and operations.
In most cases, this approach helps uncover otherwise unknowable insights that provide your firm with a competitive edge and make it more responsive to a rapidly transforming operating environment.
How do organizations embed this into the way they do things? Perhaps the most important component of building a successful data-driven culture is DataOps.
The Role Of DataOps: Powering a Data-Driven Culture
DataOps, short for data operations, refers to a modern way of approaching data-related tasks and functions in an enterprise manner. It draws from various disciplines including DevOps, lean management and agile development.
At its core, DataOps is a set of various best practices, approaches, techniques, and tools for making data operations easier to create, manage, deploy, and modify. It consists of data management, data pipelines and workflow management.
Combined, all of this is what makes DataOps crucial for a data-driven firm when it comes to management and day-to-day operations. Check out our recent post on DataOps to learn more.
Quick Tips for Better DataOps
While data operations are critical to modern firms, they pose numerous challenges, especially for small teams. There are a ton of moving pieces to keep track of and a lot of hidden ways for things to go wrong.
In any DataOps approach, data pipelines are an essential component. They are responsible for moving data to a required destination from an available source and making the required transformations to it. These data pipelines need to be resilient, scalable, reliable and have high performance and throughput.
Creating and improving your data pipelines is essential for any organization that wants to start taking DataOps more seriously, and these tips will focus on showing you key areas you can improve on.
Tip 1: Create Collaborative, Cross-Functional Teams
For DataOps to be effective within any firm, you need to involve various stakeholders in their deployment. Among other considerations, this ensures that:
- Requisite domain knowledge is incorporated in developed solutions
- Feedback is provided faster
- Solutions serve their process and workflow needs.
Doing it right requires including people with different backgrounds, people with different use cases, and people from different departments. Gather them all up and force them to work collaboratively to iteratively improve data operations.
For starters, we recommend ensuring that the different segments of your data team are building cross-functional workflows. Instead of having teams build workflows independently, you should encourage Data Engineering to build workflows that the Data Science team can directly connect to. Or have your BI Developers build workflows directly off workflows deployed by Analytics Engineers. With this setup, every touchpoint of your data starts to become intertwined, revealing full end-to-end visibility of how your data is actually being used.
Over the years, while working with countless firms, we have found again and again that creating a centralized environment where teams can collaborate on workflows can help your firm drive better value from your data with fewer errors.
Tip 2: Centralize Your Data Sources
Firms typically have data from different sources and formats located in disparate data stores. That doesn't do you any good.
In the early stages, this data should be integrated to ensure your firm has a single, holistic, source of truth. You don't necessarily need to clean the data - just make sure the raw information all lives in one place. Along with other benefits, this ensures data consistency, helps to overcome siloed data challenges, and greatly simplifies data management. Data Ingestion tools like Fivetran can greatly help with this process.
Once the data is centrally located, you can use orchestration tools to help connect the loaded data to downstream processes.
Tip 3: Design Data Pipelines Flexibly
DataOps should be designed to be highly flexible, scalable, fault-tolerant, and composable. There should always be clearly marked boundaries and, optimally, each task in workflow should have single responsibility.
Loose coupling between data pipelines ensures they can be modified, substituted, and composed easily for maximum composability and flexibility. Failure and errors in upstream steps should be corrected at the point of failure and shouldn't result in bad data deployment. Scalability is typically achieved by adopting a distributed and parallel architecture in the development of data operations.
By placing a focus on flexibility an composability, you set yourself up for success in the future as your data needs and capabilities scale. Need to process more data? Simply spin up as many workers as needed and know that automatic, dynamic, high-performance scalability and throughput will be simple to attain. Need to adjust an old process with new logic? Add one more step to the middle of your existing workflow.
Tip 4: Log Everything (and store it)
Logging is a core requirement in developing and improving data pipelines. To know what needs to be fixed, you need to be able to track its state over time. The biggest problem is not knowing what log data you'll need access to... so it's better to over-log. Storage is cheap!
Effective logging includes capturing information such as:
- Status, timing, and resource usage of every job and workflow
- State of the data before/after each step (# of columns/rows, column names, data types, etc.)
- Metadata of responses from external services and files/data accessed
By initially capturing as much log data as possible, you enable your team to rapidly manage, change, and improve your firm's data pipelines in the future.
Tip 5: Containerize your Efforts
Data pipelines usually have many external package dependencies required to provide needed functionality. Managing these dependencies increasingly becomes a nightmare as the number of pipelines and workflows increases.
To ensure that your jobs don't run into potential conflicts, you have to start effectively managing multiple different virtual environments, with different versions of package installations across all of them. Each workflow has to run its jobs in separate virtual environments, now requiring you to keep tabs on which virtual environment is right for every job!
As you scale your orchestration efforts, you get forced into a situation where you have to keep multiple servers in sync with all of your different virtual environments and package versions. If you don't, updating a single package can cause unintended breakages across your workflows.
It's much easier to resolve the issue upfront by initially running your pipelines in containers. Whether you choose a DataOps platform like Shipyard that automatically containerizes jobs, or you orchestrate your own Docker containers, it's important to implement before the complexity ramps up.
Tip 6: Automate Version Control
Version control is highly important for the enterprise management of data, code, and data operations. It allows your team to work collaboratively on data operations efforts without interfering with each other's work. Beyond that, it helps keep track of changes that are being made to data, pipelines and workflows so you can verify why things break and revert the changes.
Trying to build out effective DataOps without some form of version control is asking for a disaster. You should be able to pinpoint the exact code that runs at any given time and the state of the data when it ran. This reproducibility makes identifying and resolving errors a speedy process.
We strongly recommend using git and ensuring that your DataOps platform can stay in sync with specific branches, tags, or commits, so you can gain better visibility into how your workflows evolve over time.
Learning to Use DataOps to Your Advantage
Data pipelines are critical to data operations, running of a data-driven firm, and cultivating a data-drive culture. These days, if you're not embracing all of these concepts, you are probably going to start losing out to your competitors who do.
While it's easy to get lost in the weeds at times, the most important aspect of all this is to remember one simple thing: Constantly improving your DataOps will increase your firm's effectiveness and efficiency. Choosing a modern data orchestration platform like Shipyard will help accelerate your ability to get DataOps right.
Want to learn even more? Start your DataOps journey by signing up for a free 14-day trial here. Shipyard helps data teams orchestrate their data, connect their tools, and build workflows in a matter of minutes.
Shipyard is a modern data orchestration platform for data engineers to easily connect tools, automate workflows, and build a solid data infrastructure from day one.
Shipyard offers low-code templates that are configured using a visual interface, replacing the need to write code to build data workflows while enabling data engineers to get their work into production faster. If a solution can’t be built with existing templates, engineers can always automate scripts in the language of their choice to bring any internal or external process into their workflows.
The Shipyard team has built data products for some of the largest brands in business and deeply understands the problems that come with scale. Observability and alerting are built into the Shipyard platform, ensuring that breakages are identified before being discovered downstream by business teams.
With a high level of concurrency and end-to-end encryption, Shipyard enables data teams to accomplish more without relying on other teams or worrying about infrastructure challenges, while also ensuring that business teams trust the data made available to them.