Data Integration and Data Integration Techniques

Data integration is the process of gathering data from multiple locations and combining it into one view. It’s the process of consolidating data with the intent of providing consistent access and delivery of the information. Data integration can include the other processes of data cleansing, mapping and transformation as well since it can be incorporated in the data preparation process.

There are many techniques data analysts use to integrate data from multiple sources. These are some of the most prominent techniques used in data integration:

Data replication. One dataset is replicated across other datasets to keep the information in it synchronized and usable for backup.

Data virtualization. Data in multiple datasets is virtualized and loaded into a unified dataset. This technique makes it redundant to load the data into a new repository.

Streaming data integration. Multiple data streams are continuously integrated into a data analytics system instead of loading data afterwards. This is a real-time technique.

Data Integration and Cloud Data Analytics

Data integration isn’t done in a vacuum. The data will all be gathered into one location and analyzed, and different organizations have different methods for doing so. But one of the growing trends in the industry is organizations transitioning to the cloud for the analysis stage. The cloud offers new possibilities in data integration because it allows for more synchronous and effective integration. Streaming data integration and data virtualization become easier with the cloud’s storage and real-time capabilities. More and more organizations are transitioning to the cloud for data analytics and data integration. But even with the cloud, there are still data integration challenges.

3 Reasons Organizations Are Investing in Cloud Data Analytics with Data Integration

Read just about any article on cloud computing and cloud data analytics and you’ll find that the major takeaway is growth. Take the newly-released study from Harvard Business Review, which reports that over half (54%) of IT Managers expect the volume of data they store in public cloud servers to increase in the next 12 months and with it, cloud data analytics. Or the prediction from Gartner that the overall cloud market will increase to a staggering $331.2 billion in 2022. Organizations are investing in cloud data analytics with cloud data integration at a rapid pace, primarily for three main benefits:

  • Cloud data analytics is faster.
    Direct interconnections between data and analytics in the cloud can greatly reduce latency. Organizations also benefit from the flexibility of public clouds, which allows them to scale up and increase processing power as needed.
  • Cloud data analytics is cheaper.
    Architecting a workable analytics platform is a sizable undertaking for any IT organization that demands plenty of resources and time. Investing in the cloud means that the data processing and analytics setup is taken care of—no development required.
  • Cloud data analytics is the future.
    Data is increasingly being generated from cloud-based systems, which means cloud data analytics meets data where it lives. Organizations that analyze social media data or ad data, for example, are analyzing that data in real or near-real time with cloud data analytics.

Cloud data integration also provides new capabilities to the necessary process of data integration. With the faster and cheaper platforms, data integration also has the potential to be a more efficient and viable process.

The Challenges with Cloud Data Integration

Data integration, whether it’s in the cloud or not, comes with a variety of challenges that most data analysts are familiar with. Data integration is necessary for a streamlined analytics process, but the benefits come with drawbacks in efficiency. Data integration can sometimes be a time consuming and inefficient process. Data integration falls in with the data preparation process. Most analysts know that data preparation takes up a large chunk of an analyst’s time. Even with more synchronous capabilities from the cloud, data integration still requires time and effort to reap the benefits of having a unified dataset. What data integration needs is a data preparation tool to reduce the time and resources that are usually poured into the process.

No Matter Where the Data Lives, It Still Needs to Be Wrangled

The rise in cloud data integration and analytics has brought change to the data and analytics landscape, but there are a few things that stay the same. One such example? Data preparation, or the messy work of structuring, cleaning, and enriching data before it can be used for analysis. No matter whether you’re dealing with cloud data analytics, data integration or otherwise, the data must be prepared. And if the trend among cloud data analytics is growth, the trend among data preparation is a shift of who can do this work—and how fast. One of the challenges with data integration and data preparation is the time it takes to perform properly.

Instead of solely relying on IT organizations to prepare data, many of today’s organizations are adopting data preparation platforms to democratize this work. A data preparation platform offers an easy-to-use interface combined with seriously powerful data preparation capabilities that would otherwise require the skills of a data scientist. Not only is it much more accessible than traditional ETL solutions, but a data preparation platform has the potential to accelerate the work of preparing data for cloud data analytics by up to 90 percent. Data preparation platforms can include data integration as part of the process and make that key step efficient and effective. It’s no surprise that data preparation platforms are at the top of the list for organizations investing in cloud data analytics and/or a general modernization of their analytics stack.

Designer Cloud: The Data Preparation Platform for Data Integration

Designer Cloud powered by Trifacta is routinely recognized as the leader in data preparation and has been specifically architected for the cloud and data integration. With Designer Cloud’s seamless, cloud-agnostic support for AWS, Azure, Google Cloud Platform and Snowflake, users can prepare data in their cloud of choice or a multi-cloud environment. Among its cloud accolades, Designer Cloud is an AWS certified ML Competency and  Data & Analytics Competency partner and is the embedded technology of Google Cloud Dataprep by Trifacta, the data preparation service on Google Cloud Platform. But perhaps our greatest accomplishment in the cloud data analytics space is the results that Designer Cloud customers have seen with data preparation and data integration. To learn more about how you can use Designer Cloud to improve data integration and cloud data analytics, schedule a demo today.