What Is DataOps?

Data ops, or data operations, is the automated process used by data engineers to improve and automate the data management process. Data Ops is an agile approach encompassing many different tools such as ETL/ELT, system monitors, cataloging and data curation. It is the development, maintenance, and support of these tools that make data operations successful.

What Is Data DevOps?

DevOps is the development and operations collaboration together to produce and release a product to the customer. A DevOps engineer will work together with other developers to create systems that automate the delivery, processing, and automation of the data architecture. This allows for smoother, more efficient data systems that meet the customer’s goals. Here at TidyData that is what we aim for in the solutions we offer such as data pipeline management offered as a Software as a Service solution.

Why Is Data Ops important?

DevOps are important because they integrate the automation process with the scope of the architecture to meet what the customer wants. Without data devOps, the data systems would remain more rigid and less scalable as the customer’s needs changed over time. By modularizing the different steps in the development process we can better adapt to necessary changes where needed instead of re-inventing the whole process. The following are some reasons why devOps are important.

What are the Benefits of DevOps?

Many of the benefits are those that have been listed earlier such as: Frequent releases, Faster delivery, Increased security, Fostering collaboration, Better reliability, Improved processes. These benefits all come about by the formation of a streamlined, modularized software development architecture. By dealing with data development and operations in separate processes, the development teams can split up tasks more effectively to deliver quicker results and respond better to changes.

How do You Implement Them??

Many organizations have tried their own ways of implementing devOps and we fortunately are able to pull from their mistakes, successes and lessons learned. Learning through mistakes and adapting to changes is one of the primary ideas behind data devOps. The following are some general steps that have been designed to be broad for a variety of industries.

  1. Start Small

    By starting with the smaller projects an organization will be able to more quickly implement devOps in a modular fashion. Find out what processes work best for your specific teams and then scale up to the larger projects after implementing any lessons learned from the smaller ones. It's almost impossible to try to change everything all at once and many organizations that have tried large scale implementations right off the bat experience longer delays according to a study by IEEE.

  2. Foster a Learning Environment

    Experimentation is a large part of developing new processes and here at TidyData we're no strangers to the process of learning through trial and error. By encouraging your teams to have an open mindset and open communication, you can learn more quickly what works best for everyone at each step of the way.

  3. Prepare to Adapt

    Adaptability is not only a strength of the devOps itself, but also required to implement it. Teams must learn to change their approach and be resilient to failures. The best way to do this is to prepare them ahead of time. Be sure they know that while perfection is the goal, it is not expected especially in the earlier stages.

  4. Contribution

    From individual developers all the way up to top management contribution goes hand in hand with developing that learning environment. The transformation of DevOps needs input from teams at all levels to be successful. Management should have an open door policy ready to accept suggestions even from individual workers who might have the best ideas.

  5. No Finger Pointing

    When something goes wrong the best solution is to figure out how to prevent it in the future and not whose fault it is. It is necessary to know where the problem began so that it can be prevented but individuals do not need to be blamed. Blaming others for problems creates an environment where no one wants to come forward to fix known issues which destroys contribution and the learning environment.

What Does Data Management Mean?

Data management goes beyond just storing, verifying and security. These are necessary parts of data management but the big picture incorporates much more pieces of the puzzle to complete. At TidyData we understand that data management includes data transformation, extraction, loading, and analysis and can help your organization in it's data management journey.

What is the Goal of Data Management?

The overall goal of data management is to store raw data, transform that data to something the system understands, and deliver an analysis of the data that is useful to the customer. Just storing the data is something that anyone can do, but to make sure that data is transformed into useful information is our goal here at TidyData.

How Can it Be Managed?

Data can be managed in a variety of ways but the two biggest proponents are ETL and ELT. These stand for Extract, Transfer, Load and Extract, Load, Transfer respectively. Before we explain each let's talk about the individual terms.

  1. Extract

    Data extraction is simply pulling the data from a source whether it's a data source or multiple data bases. This provides a temporary staging area to deal with the data.

  2. Transform

    The transformation of the data changes the structure in order to be useful to the staging platform. The transformation process allows for data from many different sources and different types to be loaded into the same database.

  3. Load

    This is the actual loading of the data into that database for later analysis. The loading process can be a bottleneck if not set up efficiently.

  4. Data

    Data is just the raw statistics, numbers, or characteristics by themselves. Alone data is useless to us because data is non-relational. For instance the number 1921681101 is useless by itself as a piece of data.

  5. Information

    When data becomes readable to users in a way that they can relate it to their needs, it becomes information. When we transform the data above and relate it to become information it can be represented as something like Computer A Now we can see that this is Computer A’s IP address and not someone’s phone number. Now that the basic data management terms are covered, let's talk about the different methods of implementation.

What is ETL?

Extract, Transform, Load first pulls the data from required sources as discussed above. The next step is to transform that data through data manipulation processes such as formatting and data mapping. ETL utilizes OLAP (Online Analytical Processing) data warehouses to work with SQL-based databases. This requires the data to be transformed before loading into the data warehouse.

What is ELT?

Similarly to ETL, ELT stands for Extract, Load, Transform. ELT utilizes data lakes instead of OLAP data warehouses which allows any type of data to be loaded without transformation whether raw data or not. The data will be transformed and analyzed within the data lake unlike ETL which transforms data before storage.


As we're sure you've noticed by now, the main difference is when in the process the data transformation takes place. To put it simply ETL transforms and manipulates the data before storage and ELT does the data transformation and data manipulation afterwards.

ETL relies more heavily on services applied before the data reaches the Data warehouse whereas ELT relies on processes within the data lake. Why this matters depends on your specific requirements and how you want to improve the quality of your data management.

How Can You Improve the Quality of Data Management?

To answer this question you must first ask where quality needs to be improved. Does the load or transfer speed need to be improved, or do the processes applied to the data manipulation need to be improved? Furthermore, you may need the analysis improved in such a way to make the data more usable to yourself or a client.

Lastly, the presentation of the data to the customer may mean that the customer’s requirements need to be better understood. All of these functions can be improved by implementing devOps and at TidyData where here to help you figure out your data management solution. devOps helps streamline the improvement of quality data management by breaking down the pillars of development into more manageable pieces.

Why is Data Difficult to Manage?

Data is difficult to manage because there are so many of those processes involved. From ELT to analysis to presentation data management as a project encompasses a wide area that pulls in people with specialties in those diverse areas. Listed below are some of the steps to data warehouse management.

How TidyData Helps Its Customers

TidyData helps customers by taking the data pipeline management off the company’s hands. This allows the company to focus on process improvement, development, better customer relations, or whatever processes may be necessary.

We offer Software as a Service (SaaS) solutions to our customers so they can send the data through our secure data pipelines and have the peace of mind knowing that their data is secure and safely on its way to its destination. On our data pipelines at TidyData we monitor the data to ensure it stays error free, we monitor data security, and we check for data accuracy.

Contact us Today For Management

If you need a solution to monitor your data over a secure data pipeline, TidyData can help you. Register today by filling out our form today or email us by visiting https://www.tidydata.io/ to set up an appointment. We can help you get set up after learning what you need for a data management solution.