We generate an incredible amount of data in our increasingly digital world, with more than 180 zettabytes expected by 2025. That’s great news for DevOps teams, since they rely on data to help them understand customer feedback and develop successful products.
But for most organizations, this expanding mountain of data is collected from many sources and stored in different locations. It’s also in different formats, and the quality and reliability is variable. Such data silos create barriers to the visibility and collaboration needed for DevOps.
Wouldn’t it be great to have a unified view of all this data? That’s where data integration comes in, and this post will explore what the concept means and why it’s so important to DevOps.
What is Data Integration?
Data integration is a process in which you take data from disparate sources and bring it together in a consistent structure. With this, you eliminate the problem of having data in different formats and locations, and ensure that your data is always accessible, accurate, and high-quality.
You can analyze and make data-driven decisions involving various techniques, tools, and strategies, from manual integration to data virtualization, to get a unified view. Here are the main types of data integration:
ELT (extract, load, transform): With this technique, you extract data from its source, load it into a database or data warehouse, and transform it into the desired format. Because it’s fast and flexible, it’s better to use it for big data projects.
ETL (extract, transform, load): In this case, you transform the data before loading it into the storage system. This process involves more robust data cleaning and validation, so it’s a good choice for high data quality and consistency.
Real-time integration: This is where you capture and process data within its source system as soon as it becomes available, and then integrate it into the target system. It’s typically used for fraud detection and real-time analytics.
Application integration: When you want different software apps to work together, you can integrate the data between them for a smooth flow of information.
Data virtualization: Here, you leave the data in its original locations, but create a “virtual layer” that makes it appear as a single data store for a unified view.
Federated integration: Again, data remains in its original sources, but you can execute queries across disparate systems in real time.
What steps are involved in data integration?
Data integration involves the following series of processes:
Identify: Make a list of all the sources and types of data you need to integrate.
Extract: Use extraction tools or processes, such as querying databases or retrieving data through APIs.
Map: Create a mapping schema showing how data from different sources is represented in terms of structure and terminology.
Transform: Convert the data into a single format for consistency and compatibility, using techniques such as data cleansing and normalization.
Load: Load the data into the relevant destination, such as a data warehouse, for further analysis or reporting.
Validate: Check for errors and inconsistencies, and use quality assurance (QA) processes to ensure data accuracy and reliability.
Synchronize: Use real-time synchronization or regular updates to make sure the integrated data is kept up to date.
Manage the metadata: Metadata provides extra information about your integrated data, so it’s more discoverable and understandable by users.
Govern: Data governance ensures that you stay compliant when integrating sensitive or regulated data, using security measures to protect the data during integration and storage.
Analyze: Use data analytics, reporting, and business intelligence (BI) tools to analyze your integrated data for deeper insights.
Why is Data Integration Important for DevOps?
DevOps (development and operations) is all about shortening the software development lifecycle through close collaboration and continuous feedback. The ability to easily access all relevant data—and to know that the information is accurate—is essential to this process.
Data integration provides (or virtualizes) a single location in which DevOps teams can access and analyze the data flowing through the organization. Instead of viewing multiple data sources and formats in silos, integration combines them to show you a 360° view.
Data integration also helps to improve data quality, as the process highlights any errors, duplications, or inconsistencies. Your integration tools will flag these issues as the data is combined, while transforming it into a standard format. Automated integration eliminates manual data entry and the risk of human errors.
Developers can build data integration flows directly into their applications by coding them at the start of the DevOps process. This facilitates seamless data exchange between apps and storage locations.
Importance for collaboration
As we mentioned earlier, data integration reduces information silos. This is crucial for DevOps with its emphasis on cross-functional collaboration, not just between the development and IT operations teams but also other departments around the organization.
For example, developers often work with QA teams to check the data security of a product, and team up with marketers to analyze customer intelligence data and figure out which features are most important to the end users.
DevOps teams might also collaborate on an SEO strategy for enterprise website development, integrating SEO performance data with development metrics to make sure that coding changes aren’t harming the website’s search rankings. Accessing this data, and keeping it in mind when building a site, helps to shorten the development lifecycle.
Importance for visibility
In DevOps, everyone in the team takes responsibility for the overall development process. That means everyone needs centralized access to all relevant data. Real-time access is important because the continuous delivery model relies on quick feedback from testers and end users, as well as performance data from apps and websites.
By enabling full visibility, data integration ensures that you’re not making decisions based on inaccurate or obsolete information. It enables fast access to reliable data for analysis (including predictive analytics), revealing trends and patterns that you wouldn’t have seen if the data was dispersed.
For example, DevOps requires customer data and feedback to improve the UX design of software products and websites. This might include customer surveys, social media comments, and SEO data on user interaction, all gathered from multiple channels. When integration pulls it into a single view and a consistent format, it’s easier to draw insights.
Data integration also encompasses visualization through business intelligence (BI) tools, which helps teams to envisage data in a simple way and explain ideas or opportunities to others.
Tips for Successful Data Integration
We’ve seen why data integration is so important to DevOps. But is there anything you can do to make sure the process runs smoothly?
Focus on relevant data
Your organization likely generates a ton of data every day. Integrating it into a unified view will definitely make life easier for all teams, including DevOps—but it’s important that you focus on the data that’s relevant to the current project. Otherwise, you’ll get overwhelmed by information.
For example, if you were developing a website for a SaaS product, you’d want to zero in on data from your SaaS technical SEO services experts and pair it with customer feedback on user experience.
Use the right tools
There are plenty of data integration tools on the market, so do your research and choose one that particularly supports DevOps practices. Look for tools that enable you to set up automated data pipelines for seamless integration, transformation, and ETL/ELT processes.
Consider implementing DataOps
DataOps is a collaborative data management practice that complements DevOps. DataOps uses technology to automate the design, deployment, and management of data delivery across the organization, helping you to improve data flows between the people who manage the data and those who consume it.
Like DevOps, it’s about constant collaboration and feedback with a view to increasing improvement. When you combine DataOps and DevOps, you can streamline your development and data pipelines to deliver valuable insights and accelerate the delivery of quality products to end users.
Learn more about data integration
If you’re part of a large enterprise, you’ll already have skilled teams in place. But if you’re a smaller business, it could be worth learning more about data integration and how it relates to the DevOps methodology before you get started. Consider taking a course in data analytics, or even web development and SEO if you’re planning to create your own site.
Final Thoughts
For DevOps teams, collaboration and continuous feedback are key. Data silos, inaccurate information, and non-standard data formats are common challenges, but you can avoid them all by implementing data integration.
Whether you’re building an app or a website, data integration gives you easy access to relevant and reliable information; you can share and analyze it with colleagues and use the insights to develop higher-quality products in a shorter time.
Author:
Nick Brown is the founder & CEO of accelerate agency, the SaaS SEO agency. Nick has launched several successful online businesses, writes for Forbes, published a book and has grown accelerate from a UK-based agency to a company that now operates across US, APAC and EMEA.
