Do you ever reproduce your transformed data and get different results? Do you share code snippets over Slack or email? Do you spend a lot of time developing data pipelines? Are you tired of not having automated deployment?
If you answered "yes" to some — or all — of these questions, you probably already recognize that you aren't operating with an ideal process. The best way to avoid these issues, and the other common problems highlighted below, is to adopt the latest agile operations methodology.
What you need is DataOps.
What Is DataOps?
DataOps is an agile and automated methodology for developing and delivering analytics in a faster, more efficient way. In a practical sense, you can think about DataOps methodology as a combination of agile development, DevOps, and statistical process controls.
Larger organizations like Facebook and Netflix have both adopted this methodology, and it has helped their teams work together efficiently and effectively. But DataOps isn't restricted to just big business. It can help any organization:
- Maintain better data management practices and processes to get data analytics faster
- Improve data quality
- Improve data access
- Maintain quality control
- Automate processes
- Integrate all teams across the company
- Improve model deployment and management
Do You Have These Problems in Your Data Team?
Many companies are innovating rapidly, but they aren't using DataOps. If data teams want to reduce cycle time and generally optimize their workflow, using an agile methodology is a great place to start.
Overall, there are a wide range of problems facing SMBs that can be eased — or eliminated altogether — by adopting the right approach. Here, we break down some of the most common problems and how DataOps can help.
1. You don't monitor the quality of your data
DataOps helps you to control and monitor the data pipeline to have high-quality data. By testing your data during transformation steps, using dbt, or implementing data tests at every step of the cycle, using Great Expectations, you can rest easy knowing that the data being used abides by preset rules. If something ever goes wrong or changes about your data, you'll know instantaneously.
You should always test your data to verify things like:
- Are all the IDs unique?
- Should a column contain null values?
- Are there less rows than expected?
- Do all of the column names line up?
All of the data that goes production must be validated with automated tests to ensure that when the data gets used, it's as accurate as possible.
2. You don't have a platform for data movement or orchestration
DataOps helps you to orchestrate your data pipeline so you can see the end-to-end movement of all your data. For example, an orchestration platform can help you ingest and load your data, transform it for use, update BI extracts, deploy machine learning models, and activate marketing campaigns all from one centralized location.
Having an orchestration platform in place allows you to not only see the movement of data, but to also build ad-hoc branching solutions off the data being generated.
3. Your code isn't templatized
Many teams build quick-and-dirty scripts to get the job done for their business. These scripts get passed around and tweaked slightly by each user, causing a mess of code where everyone uses slightly different variations and no one knows where and when those variations are being used.
With DataOps, it's advantageous to take a step back and abstract the components of any script you build to be parameterized so that templates can be created. For example, if you're building a script to convert a file before it gets uploaded to an external platform, there's a few questions you can ask that will ultimately need to be parameterized:
- Will the source of data always be the same?
- Will the input file name change?
- Will the input file format change?
- Will the contents of the input file always be the same?
- Will the size of the input file always be the same (should it be chunked)?
- Will the output file name change?
- Will the output file format change?
By building scripts with templates in mind, you make it easier to share and reuse your code for commonly repeated tasks, reduce the time it takes to work with data.
4. Your data is not reproducible
Many data teams still perform ETL (Extract Transform Load) operations instead of ELT (Extract Load Transform). In this new era of DataOps, it's recommended to store raw data first and then transform the data later. With this methodology in place, you can store different transformation logic with version control and "re-run" tables and views as they existed in the past because the underlying raw data will never change. You want your transformation logic to be reproducible so that you can always track how the data being used downstream looked at any given moment in time.
5. You don't have data access controls
Data access control isn't only for big tech companies. Part of a good DataOps setup involves creating protocols to control data access, ensuring that each user of your data can only access specific schemas, tables, views, or even columns of information. In the same way that cloud services are controlled by IAM roles, your data should follow the same principles.
Putting access controls in place ensures that new data isn't readily accessible by every team member. Instead, the creation and permissioning of that data is explicit so you know who is gaining access. This prevents sensitive data, like PII, from getting in the wrong hands, helping you avoid business and legal problems.
6. You don't have data democratization
With DataOps in place, and the right access controls created across the board, everyone in your team should have access to the data they need at any time to create new solutions, build new visualizations, and ultimately be productive with the data. By ensuring that everyone can access the right data, you help enhance the ability for teams to work together, get feedback, and make data-driven decisions.
Beyond data access, DataOps helps ensure that technical teams have better visibility into the lifecycle of data so that they can understand how data is originally generated and how that data impacts downstream processes. With the right tooling, data teams can create collaborative workflows that share access to both the underlying code and the data being generated. This puts teams in a better position where they can easily pinpoint data issues as they arise.
7. Everything takes a lot of time
By implementing DataOps, you'll be creating a foundational data infrastructure that helps your data team deliver results in hours. You shouldn't have to wait for the DevOps or IT team to configure system resources for you to launch an experiment. It shouldn't have to take days to build out a data pipeline from scratch, due to complex workflow as code and custom development work.
Having the right systems in place ensure that your data team can provide value to the organization at a rapid pace, helping accelerate the ROI driven from you data. Many companies are developing solutions in less than a week; your data team should do the same. DataOps helps you to reduce the time it takes to actively use clean data from your pipelines, enabling your team to more rapidly iterate and experiment with ways to drive business value.
8. You have problems with your data
If you're continuously struggling with data accuracy or infrastructure scalability issues, DataOps will help you troubleshoot faster. Your tooling should alert the team if your scripts break, your pipelines stops, or you have poor-quality data. By having interconnected data workflows, these issues should prevent downstream data issues from ever occurring, proactively notifying the impacted teams about the issues in real time.
Troubleshooting the issue itself should also be relatively straightforward. You shouldn't have to dig through massive logs to find a specific issue. Instead, you should know exactly what broke, why, and what it impacts, saving hours of precious time to get to a resolution.
9. You don't have an automated data pipeline
You should automate your data pipeline to increase productivity. For example, you could automate a simple step like data assertion and data analytics pipeline monitoring. You could also employ automated complex processes, from ETL to charts outputs, or you could automate deployment.
10. You don't use source control
The principles of DataOps require data teams to use source control software for all of their code. Services like GitHub, GitLab, and BitBucket enable teams to store every version of code changes, work on code collaboratively, and implement approval processes for new code additions. Without these tools in place, changes your team makes to the code will be invisible to the rest of the team and non-reproducible in the future.
For a fully realized DataOps lifecycle, you want to ensure that your code from these platforms is synced to your automated data pipelines, using a process called CI/CD (Continuous Integration/Continuous Deployment). With this process in place, every approved and merged code change gets automatically deployed as part of your data workflows.
11. You don't work with separate environments
Is your data team making all of their changes directly to a production database? By implementing DataOps principles, your team should, at the very least, have a development environment that they can build off of for testing. This allows the team to make frequent queries and changes without impacting any of the mission critical jobs that might be running. Any code that's implemented can first be verified against development data to ensure that the changes are unlikely to have a negative impact to the overall state of data.
12. Your teams don't work together
Analysts, data scientists, data engineers, data architects, analytics engineers, developers, and other technical teams should all work together and understand how their work impacts each other. Too many teams operate by "throwing things over the wall". Once they've completed their work, they move on without verifying how their work impacts others.
By implementing DataOps, you increase the level of visibility and collaboration, knocking down any potential silos in the process. Data Engineers gain an understanding of how Analytics Engineers transform and test the data. Data Analysts can see the full picture of how their data is being generated before building a dashboard. Data Scientists can verify the ingestion process to ensure that data drift will be minimal.
This type of setup gives everyone on the data team the ability to understand the interconnectedness of their work and the impact it has on the overall business.
Getting Started with DataOps
As you can see, there is a minefield of potential problems out there for today's businesses. Getting it all right isn't easy, and many companies are struggling. So if you want to make improvements quickly — and permanently — it may be time for an overhaul to your process.
If you're tired of struggling with these and many other common problems, making this change to implement DataOps is a great way to see major improvements in minimal time.
How do you get started and make the change? Shipyard is the ideal solution to get started.
Shipyard is a serverless data orchestration platform that helps data engineers launch, monitor, and share data workflows 10x faster. It has been specifically designed to enable businesses to establish and optimize the DataOps lifecycle.
Want to learn even more? Schedule a demo or sign up for a free 14-day trial. With a trial, you can immediately build data workflows in minutes and see why Shipyard is the best way to implement DataOps.
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.