Your data is your business. If it is inaccurate or constantly delayed because of delivery problems, you can’t make timely and well-informed decisions.
In constantly changing, complex data environments, maintaining a solid understanding of data assets can be a challenge. Tracking data origin, analyzing data dependencies, and keeping documentation up to date is resource-intensive—but critical to a data-driven organization.
A high-performing DataOps practice helps your company accelerate the data lifecycle—from developing data-centric applications through delivering accurate business-critical data to your end users and customers.
SolarWinds SentryOne not only helps you speed data delivery—but also helps ensure the data is right.
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DataOps is a collaborative practice that improves integration, reliability, and delivery of data across the enterprise. It builds on the foundation of strong DevOps processes. Like DevOps, DataOps fosters communication between business functions like data platform, IT operations, business analytics, engineering, and data science. It focuses on streamlining and automating the data pipeline throughout the data lifecycle:
DataOps paves the way for effective data operations and a reliable data pipeline, delivering information that people trust with shorter development and delivery cycles.
Data professionals have been working on data integration processes and techniques for decades. Deriving value from data requires bringing data from multiple sources together and relating it in meaningful ways. Common examples of data integration include ETL/ELT processes, data warehouse batch jobs, and multidimensional and tabular model processing.
Data can be sourced from nearly anywhere. Some examples are:
Some companies also still have legacy data sources in use, such as mainframes or flat files, and unstructured sources, such as websites, email, and various documents.
Data integration is a core DataOps concept. It is arguably what first comes to mind when many people consider DataOps strategies.
Ensuring that business decisions are made with accurate data starts with practicing data validation. Testing procedural code and user experience is a ubiquitous concept in the software industry. Testing data throughout analytics pipelines—or anywhere else data is moving between platforms—is still catching on.
One reason data testing can be difficult to adopt is that there aren't many options for comprehensive testing frameworks. Some are simple comparison tools that still require a lot of manual planning and documentation. Others are massive and cost prohibitive data quality platforms meant that require constant maintenance and grooming.
Getting value and insight from data requires an understanding of what the data represents and how to translate it. Metadata is data about...data. It allows us to describe data structures and properties of data values without exposing the data itself.
Without managing metadata, you wouldn't be able to generate documentation for analytics solutions. Generating documentation from metadata enables you to understand and react to changes in data models and structure. It also ensures that you can describe where the various parts of the solution come from and why. A map of where data starts, how it changes, and where it's viewed is also important for compliance regulations and auditing purposes.
The key to continuous improvement of automated systems is observability. DataOps pipelines are designed and deployed with varying degrees of automation. Some will employ manual touch points at various pipeline stages while others continuously cycle with complete autonomy. Detailed activity and performance measurements should be captured and analyzed consistently.
This approach empowers the DataOps team to:
Gathering operational and performance data for observability is called monitoring. Monitoring solutions can take many forms, but there are some core concepts to consider in planning:
The monitoring solution needs to grow seamlessly as your business and data platform grow. It should be able to expand as needed with little effort. It should also continue to perform as workload and capacity needs increase.
DataOps pipelines will include components across multiple platforms. These include private, public, and hybrid cloud configurations. The monitoring solution should consider modern deployment models to provide support wherever the pipelines lives.
Flexibility also refers to how readily we can adapt the solution to specific business needs. The monitoring solution should empower the DataOps team to extend base functionality as needed.
Many monitoring solutions tend to treat all event and performance measurements the same. The best monitoring solutions are designed with extensive research into the volatility of the data being collected.
Performing a full collection at longer intervals of 5 minutes or more is a common practice. This method comes with high risk, because many problems and opportunities can surface quickly in modern data platforms. An intelligent approach to monitoring achieves a reduction in observer overhead by gathering highly volatile measurements more frequently and more static measurements less frequently.
Providing for observability should not interfere with operating the pipeline itself. Observer overhead happens when monitoring solutions claim resources the pipeline needs to achieve performance objectives. A solution that introduces high observer overhead is no solution at all. Instead, it becomes part of the problem by directly sapping performance and introducing a less-than-obvious variable to consider during troubleshooting procedures.
In adopting and maturing your DataOps discipline,you'll encounter some hurdles. There are several benefits to enjoy that should make overcoming these obstacles worth the effort. You need to determine whether the benefits outweigh the barriers. Some barriers and benefits will be proprietary to your business and situation. We've outlined four common benefits and barriers of mature DataOps processes.
Determining whether to move forward with building a DataOps practice is similar to a buy-vs-build or pros-vs-cons analysis. If you're starting with little or no experience with process automation or Agile practices, and have few resources to dedicate to the effort, this project will be a challenge. Even if you're well situated in these areas, it will still be work. Review the benefits and barriers discussed here, then consider other factors specific to your situation. This exercise will give help you start with a strong basis to justify investment in DataOps. You'll likely discover that the benefits far outweigh the barriers in terms of long-term value for your business.
Terms that refer to effective collaboration are alignment, tearing down silos, "synergy," and a newer term—interlock. These terms are prevalent in business because getting them right creates a force multiplier across departments. Imagine being in a rowboat with 10 other people, and none of them are rowing in the same direction. You might never get to where you're trying to go.
A mature DataOps practice promotes up-front planning and construction, then automated ongoing execution. In other words, teams work together to define what will happen, and various software tools ensure that it happens the same way every time.
Similar to the benefit of collaboration, the automation of data and analytics operations removes a potential element of human unpredictability. We, as human beings, are capable of great things like free thought and reason. These abilities serve us well in many situations. However, they can introduce problems when dealing with repetitive processes that must always follow the same steps.
With a mature, documented, and automated DataOps process, plans to introduce change require fewer hands, less time, and a lower probability of introducing errors. Using this approach also makes it easier to adapt testing procedures. This effectively reduces the time it takes to move from development to production for changes.
DevOps and DataOps have emerged from Agile project management practices. Because of those roots, agility becomes table stakes in DataOps processes. Data teams that already practice Agile methodologies will find it easier to define, implement, and mature their DataOps practice.
Intelligent DataOps is usually a way to reduce the impact of departmental silos. At the same time, the existence of silos can become a hurdle in establishing and maturing these processes.
Planning is the key. Include stakeholders across departments in planning. Keep discussions open and allow input from contributors.
The pool of potential great ideas will be multiplied, and the overall solution will become more thorough and accurate. The downside is a bit more time in planning, which should be anticipated up front.
Implementing DataOps will inevitably lead to build-vs-buy discussions. There could also be a mix—build some and buy some. An important concept to keep top of mind will be sticking with tools from the same vendor or ones that provide extensibility to help interact with other tools. A good example of extensibility would be Advisory Conditions in SQL Sentry.
Many data professionals have been working under high-stress requirements for years —some for decades. Taking time to proactively build skills isn't always an option. A lack of skills can present a barrier to implementing intelligent DataOps because team members have to learn and adapt as they go. Settling on a high-level approach will reveal technical skill needs. Training should then become a key component of the DataOps maturity plan.
Similar to stakeholder silos, you might find it difficult to win universal buy-in for an Agile approach to analytics. Agile is a mature practice with numerous documented benefits. The success we've seen with Agile practices in technology are often associated with software development and deployment. Only in the last several years have we seen a large positive impact emerge from DataOps. Achieving a level of maturity for these processes might require research and finesse. This additional effort will be instrumental for convincing the organization to fully commit and invest in the project.
SentryOne is an ideal choice for DataOps tooling. Products from SentryOne help with process implementation for each component of intelligent DataOps: