What DataOps solves

We already made entries of #DataOps (data operations), but to refresh the memory we say: it is the combination of people, processes and technology that allow us to handle data that is useful for #developers, #datascientist, #operations, applications and tools (eg #artificial #intelligence) , allowing to channel the data, keep them safe during their life cycle and configure a #governance over them.

The faster we manipulate and deliver the data, the faster the #growth for the business will be due to the use of the information, therefore, its objective is to promote data management practices and procedures that improve the speed and accuracy of the analysis.

The idea of ​​this post is to make a short-list with 5 basic problems that are solved with the implementation of DataOps in an organization.

Let’s see what DataOps solves:

#Bug fixes: In addition to improving the agility of development processes, DataOps has the power to boost time to respond to errors and defects by significantly reducing times.

#Efficiency: in DataOps, data specialists and developers work together and, therefore, the flow of information is horizontal. Instead of comparing information in weekly or monthly meetings, the exchange occurs regularly, which significantly improves communication efficiency and the final results.

#Objectives: DataOps provides developers and specialists in real-time data on the performance of their systems.

#DataSilos: DataOps faces the data silos that are generated in different departments or management of a company, many groups see their operations as inviolable “fifths” in which each silo is a barrier to success to implement better management strategies of data. The implementation of a correct governance is crucial for obtaining all the data sources that the organization requires to meet its business objectives.

#Skills: It is a fact that data professionals do not abound. The lack of availability of the right people to manage #BigData & #BI projects means that the projects are not executed in a timely manner, or worse, that they fail. It is a mistake to put more data on a computer that does not have the knowledge and resources to handle it.

We invite you to join our Linkedin Group of “DataOps in Spanish”

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It is the time of DataOps. Know the details

DataOps, is a methodology that emerged from Agile cultures that seeks to cultivate data management practices and processes to improve the speed and accuracy of the analysis, including access, quality, automation, integration and data models.

DataOps is about aligning the way you manage your data with the goals you have for that data.

It is not bad to remember part of the Manifiesto DataOps:

  1. People and interactions instead of processes and tools
  2. Efficient analytics solutions instead of comprehensive documentation
  3. Collaboration with the consumer instead of contractual negotiations
  4. Experimentation, interaction and feedback instead of direct extensive design
  5. Multidisciplinary ownership of operations instead of isolated responsibilities.

We are going to give a clear example of #DataOps applied to the reduction of the customer #turnover rate. You can take advantage of your customers’ data to create a recommendation engine that shows products that are relevant to your customers, which would keep them buying for longer. But that is only possible if your data science team has access to the data they need to build that system and the tools to implement it, and can integrate it with your website, continuously feed new data, monitor performance, etc. For that you need a continuous process that will require you to include information from your engineering, IT and business teams.

In order to implement solutions that add value, it is necessary to manage healthy data. Better data management leads to better data, and more available. More and better data lead to a better analysis, which translates into better knowledge, business strategies and greater profitability.

DataOps strives to foster collaboration between data scientists, engineers and IT experts so that each team works synchronized to leverage data in the most appropriate way and in less time.

DataOps is one of the many methodologies born from #DevOps. The success of DevOps lies in eliminating the silos of traditional IT: one that manages development work and another that performs operational work. In a DevOps configuration, software implementation is fast and continuous because all the equipment is linked to detect and correct problems as they occur.

DataOps is based on this idea, but applying it throughout the data life cycle. Consequently, DevOps concepts such as CI / CD are now being applied to the data science production process. Data science teams are taking advantage of software version control solutions such as GitHub to track code changes and container technology such as Kubernetes and Openshift to create environments for Analysis and deployment of models. This type of data science and DevOps approach is sometimes called “continuous analysis.”

However. So far the whole theory. But … How do I start implementing DataOps?

This is where you should start:

  • #Democratize your data. Remove bureaucratic barriers that prevent access to the organization’s data, any company that strives to be at the forefront needs data sets that are available.
  • Take advantage of #opensource platforms and tools. Platforms for data movement, orchestration, integration, performance and more.
  • Part of being agile is not wasting time building things that you don’t have to do or reinvent the wheel when the tools your team already knows are open source. Consider your data needs and select your technology stack accordingly.
  • Automate, automate, automate. This comes directly from the world of DevOps, it is essential that you #automate the steps that unnecessarily require a great manual effort, such as quality control tests and data analysis pipeline monitoring.
  • Enable self-sufficiency with #microservices. For example, giving your data scientists the ability to implement models such as #APIs means that engineers can integrate that code where necessary without #refactoring, resulting in productivity improvements.
If you want to know more, we recommend entering our Linkedin group, DataOps en Español.
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NoSQL Governance

NoSQL databases have grown significantly in recent years and now almost all companies have #NoSQL installations as part of their business data program. #Gartner estimates that 90% of the data is ‘unstructured’. In an increasingly #Agile / #DevOps / #DataOps world, the use of NoSQL bases for application development is considered a great advantage to accelerate time to market time for software.

Developers can create the scheme and design the database through their own code without the participation of traditional #DBA teams. But the lack of formal design and inadequate processes can generate problems for the application and can affect the general governance of company data. For example, it is difficult to determine what is stored in what place. It also poses a great challenge for auditing and compliance reports for companies.

As there are usually possibilities for more than one type of NoSQL bd to be used in conjunction with an RDBMS db, a more robust data governance framework is required to understand the data that is stored in such a variety of database technologies.

Finally, DBAs and other data professionals may now have to review the application code to understand the scheme and determine if the problem is in the data, the scheme or the infrastructure, which makes troubleshooting more complex.

It is really very important that ‘data-driven’ or ‘data-driven’ organizations adopt a new thought that involves the challenge of taking advantage of the latest NoSQL database technologies and also trying to maintain the integrity, quality and governance of the underlying data.

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