As data volumes continue to grow, and applications produce more streaming data in real time, enterprises are moving to cloud data warehouses to store all this information. Before embarking on the cloud route, it’s important to stop and consider how to properly manage a cloud data warehouse integration to ensure your organization is always relying on the most accurate and valuable data for its insights.

The variety of data in a cloud data warehouse

It’s no secret that keeping up with the incoming data is a challenge for enterprises. There are numerous big data sources that are behind this: Internet of Things (IoT) sensors, social media, existing databases, and the Web all have a hand in creating the current tsunami of data.

A cloud data warehouse (CDW) can be a conduit for a wide variety of data: big data, cloud, on-prem data, different data sources, semi-structured, and even unstructured data. Storing all this data in a cloud data warehouse is a just one stage in the data-driven journey.  With any cloud data warehouse integration, enterprises need to think about the bigger picture: managing the data lifecycle, which includes ensuring data quality, providing a data governance framework, among other considerations.

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Expectations for data integration and management

Cloud data warehouses need to do complicated work — crossing hybrid environments as well as accommodating a larger organizational shift to the cloud. Business use cases for cloud data warehouses are evenly spread among marketing, finance, sales, customer service, operations, etc., so the most successful cloud data warehouse integrations will make data quickly and easily accessible to everyone who needs it.

In October 2018, TDWI and Talend asked over 200 architects, IT and Analytics managers, directors and VPs, and a mix of data professionals about their cloud data warehouse integration plans. As it turns out, those enterprises surveyed have very high expectations of the CDW data:

cloud data warehouse

These findings indicate that enterprises have big plans for their cloud data warehouses. In order to effectively introduce the processes in the chart above, comprehensive data integration solutions must be in place to complement the cloud data warehouse.

Organizations that are moving their data warehousing initiatives to the cloud and using data integration tools are seeing benefits.  A CDW must support increases in speed and scale and be capable of handling both your current and future needs. That’s why your data integration solution must have a wide range of capabilities to make this possible. Your cloud data warehouse integration solution should support:

  • Standardizing, cleansing, and data quality processing before and after data is in a CDW
  • Being flexible enough to allow business rule transformations on data
  • Advanced transformations like data quality, data masking, and machine learning, as well as wider capabilities including metadata management and data cataloging
  • Fast and powerful processing with in-memory and big data capabilities such as Apache Spark

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The demands placed on CDW data

Enterprises have high hopes for the data in a cloud data warehouse – but the cloud data warehouse doesn’t perform the needed actions to extract value from the data. That’s where data integration solutions come into play:

Performing analytics on CDW data

To get value out of the data in a cloud data warehouse, companies require a number of additional processing and methodologies to perform data analytics. These are usually not included in a traditional CDW but are particularly important to companies embarking on the CDW journey.

Managing CDW data in real-time

Automated data, often entering systems in real time, requires additional processing to handle. Over half of organizations currently using cloud data warehouses have some form of automated tooling in place for various data management processes.

Technical use cases for CDW data

A cloud data warehouse must support a variety of technical use cases. These include: accessing data in the CDW for analytics, ingesting data from the cloud and on-premises to a CDW, transforming and processing data, and more. These needs go beyond a simple data warehouse solution. Different users throughout the enterprise use the CDW in different ways, demanding power and flexibility.

It’s no longer enough to stick your data into a CDW where it will wait for an ETL or ELT process. Processing should happen both before (standardization and data cleansing) and after (data integration and business rule transformation) data is loaded into a CDW. In short, organizations expect to be able to manage the full data lifecycle from ingestion to distribution.

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Why Talend for cloud data warehouse integration

What a cloud data warehouse must do has evolved with changes in cloud computing, automation, machine learning, and other important trends. Integration plays a critical role when moving to a cloud data warehouse, but it can easily become the longest mile, the main bottleneck to get to the insights.

data integration

Talend Data Fabric is the only platform in the market that can optimize your cloud data warehouse use cases and support the needs of all modern data integrations. Talend can optimize your use and scale to process your data before and after your data is in a CDW, for both complex and advanced processing and transformations.

Talend Cloud Pipeline Designer 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.