As the surge of data continues, enterprises are looking for the most efficient way to handle this data. As a result, many organizations are making the move to cloud data warehouses (CDWs). On the surface, transitioning to a CDW seems like a no-brainer: virtually unlimited capacity and scalability coupled with a faster and more economical way to get into and use the data sounds very attractive.
Before you jump in to a CDW, it’s important to take a step back and consider ways to ensure your data maintains its integrity and effectiveness. This is the only way to maximize the value of moving to a cloud data warehouse, because your employees will expect fast access to quality data to analyze and apply information to their daily job functions.
The Definitive Guide to Cloud Data Warehouses and Cloud Data Lakes now.
Why move to a cloud data warehouse?
Because the amount of available enterprise data is so large, organizations find it is becoming more time-consuming and expensive to rely on an on-premise data warehouse for data storage. The upfront costs of hardware and ongoing maintenance costs of maintaining a conventional data warehouse can add up. It’s a lot of money for a legacy solution that can’t scale to meet data needs.
Alternatively, with a cloud data warehouse, there are no physical servers to buy or set up. Capacity isn’t an issue, so data can flow seamlessly at peak and slow times, which brings us to a major benefit of cloud: the low cost of data storage. In addition, cloud data warehouses offer virtually unlimited capacity and scalability, and a faster way to get into the data so it can be leveraged for data analysis.
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Expectations of cloud data warehouses
All too often, enterprises think once the data has made the move to a cloud data warehouse, the benefits will follow. It is important to realize that the move to a cloud data warehouse is not the end, but rather a stage in the data-driven journey. To realize the full benefits of a CDW – beyond lowering costs – data architects need to be aware of how every business unit is using data and focus on data integration.
What a cloud data warehouse must do has evolved with changes in cloud computing, automation, machine learning, and other important trends. This journey involves managing a data lifecycle, ensuring data quality, providing a data governance framework, metadata management, data transformation and more – before and after data is loaded into a CDW.
Those requirements cannot be met solely by CDW technologies; the response suggests a need for data integration solutions to complement the infrastructure. CDWs must be enabled to accommodate a range of use cases, from business to technical, and support increases in speed and scale, while handling both current and future needs.
When it’s time to move to a cloud data warehouse
Big data has enabled the world of unstructured data sources to be tapped for any sort of intelligence. The cloud data warehouse is a new and fundamentally different technology offering and to get the most out of it will require a new and fundamentally different kind of thinking. Unfortunately, the best practices we use to design and build on-premises data warehouses will not translate en masse to a CDW.
Why Your Next Data Warehouse Should Be in the Cloud now.
4 key operations to perform on data moving into a CDW
When it’s time to move your data to a cloud data warehouse, you will need a cloud data integration solution to accompany your data that makes sure the data is primed and ready for analysis. Ideally, the solution should perform these operations:
- Data preparation: This is the process of cleaning and transforming raw data before it is ingested into the cloud data warehouse. The data preparation process usually includes standardizing data formats, enriching source data, and/or removing outliers.
- Data cataloging: This will act as the single source of trust for data. A data catalog should not only provide context to key stakeholders to find and understand data, it will also automate metadata management and make it collaborative.
- Data quality: The insights extracted from data are only as good as the quality of the data itself. An ideal data quality solution will profile, clean, and mask data in any format or size to deliver quality data to a cloud data warehouse in real-time.
- Data stewardship: In the case of erroneous data, this will orchestrate the job of fixing, merging and certifying the data.
A comprehensive way to move data
Talend enables organizations to harness the value of their data as it moves to a cloud data warehouse. Talend Data Fabric is an integrated platform for collecting raw data, then governing, transforming, and delivering it as insight-ready data. Enterprises can rely on the platform to make sure the data going into a CDW will be accurate, complete, up-to-date, and comparable to make the right decisions.
Talend Data Fabric is the only data integration solution with built-in pervasive data quality and governance. Talend Cloud Pipeline Designer, part of the Talend Data Fabric suite, enables IT and ad-hoc integrators to use a simple interface to transform and move their data—whether it is on-premises or cloud, batch or streaming— into cloud data lakes and cloud data warehouses within a single seamless environment. With many connectors to popular cloud providers and data warehouses, including Amazon Redshift, Cloudera, Databricks, Microsoft Azure SQL, Snowflake, and HP Vertica, Talend can provide the integration solutions you need to move data into a cloud data warehouse.