It’s hard to believe that enterprises are still struggling to make use of the vast amount of data within their organization but for most, accessing and analyzing data remains an elusive goal. The cloud and cloud data warehouses can help them centralize the information they need from across their organization to perform analytics, forecasting, predictive modeling, machine learning, and other advanced use cases that will get them the insights they need. The cloud is a scalable, high-performance platform that can help organizations achieve faster time to insights. But simply moving data into the cloud alone won’t actually make it actionable.
It’s important to understand that where your data resides does not address how it is used. For example, 90% of data professionals say it is challenging to make data available in a format usable for analytics. When you choose a cloud data warehouse to store your data, you’ll need to migrate those data sources first. After that, you will need to figure out how to use that data for analytics and reporting to derive valuable insight from it.
To build a data analytics platform in the cloud, you need to first design and set up the data infrastructure and complementary cloud-based solutions to make it happen.
Here are a few general do’s and don’ts when building out a cloud data analytics platform.
The line between a cloud data warehouse and a data lake is beginning to blur as these two technologies appeal to data professionals looking to store data in a central location. A data lake is not a direct replacement for a cloud data warehouse. They are supplemental technologies that serve different use cases with some similar capabilities. Most organizations that have a data lake will also have a data warehouse.
Some cloud service providers combine data lake and data warehouse technologies into one platform to support better analytics. Whether you want to call it a data warehouse or a data lake matters less: The data is centralized in a form that is useful, and that’s the key. Breaking down data silos helps you access the data you collect and keep that data synchronized and up to date.
Simply loading data is not enough to get the insights you need for analytics and reporting. Data transformation, the joining together and embellishment of data from different sources, produces analytics-ready data by taking it from a raw, normalized state to data that is denormalized. Traditional ETL (extract-transform-load) processes and manual coding, along with failure to plan and test data before running an ETL job, can introduce errors such as duplicates, missing data, and other issues.
