Why Cloud Data Lakes Need Cloud-Native Data Integration

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Click to learn more about author Sanjay Vyas.

More organizations are moving their data lakes to the cloud with the promise of improved performance, optimized analytics, and reduced costs. The path to seamless data integration, however, is not always a straight one. Before your organization moves its data lake to the cloud, you need to fully understand the challenges involved in moving and managing massive volumes of structured and unstructured data between on-premises and cloud environments.

Today’s IT environments are complex. From SaaS applications and cloud data warehouses to on-premises legacy apps and data lakes to IoT devices at the edge, data sources are as numerous as they are varied. Adding to this complexity is the adoption of a multi-cloud approach in which organizations use more than one cloud vendor such as AWS, Azure, or GCP.

As you move your data lake to the cloud, you must carefully consider the technologies you’re using to connect data sources to processing platforms and analytic environments. The last thing you want is a custom data integration solution, legacy application, or data migration standing in the way of your data exploration. Organizations migrating to cloud data lakes should ask the following four questions about data integration.

Four Questions to Evaluate Data Integration in the Cloud

  1. Can my current solution connect on-premises systems with the cloud?

The biggest challenge with establishing a cloud data lake is connectivity. Organizations need to connect SaaS applications, legacy systems, existing data lakes, warehouses and more to the cloud. In addition, the cloud itself has many different components that require connectivity, including storage, compute, database, and data warehouse. And any time you bring on a new data source, you need a new connection.

The truth is legacy frameworks and existing data integration tools aren’t designed to connect on-premises systems with the cloud. Building individual connectors manually is time consuming, costly, and error prone.

  1. Will my data be secure in the journey from on-premises to the cloud?

Security is always a major concern for any enterprise moving data to the cloud. Legacy applications present their own security challenges. They weren’t designed to move data beyond the firewall and don’t have the security features required for cloud. Data from legacy systems needs to be encrypted on-premises at the source using Advanced Encryption Standard (AES)algorithms and decrypted only when it reaches its target destination. Data integration solutions need to leverage cloud vendors’ secure key management components to meet the security requirements of cloud data ingestion.

  1. Will my current solution leverage scalability of the cloud?

The cost savings promised by cloud data lakes can quickly evaporate if your data integration solution requires custom coding or additional servers to expand data sets or access new data sources. Additional servers are expensive and developing custom pipelines can be costly, assuming you even have the right talent on staff to build them. A truly scalable solution is one that has a modular architecture, handling numerous applications and data types out of the box, while offering fast pipeline development through an intuitive user interface.

  1. Does my current solution support proper process controls and notifications in a cloud environment?

When moving between the cloud and on-premises systems and orchestrating processes within cloud systems, data integrity must be protected with proper process controls and notifications. Enterprise features such as high availability and process load balancing are vital to ensure data is always available. To provide these features, data integration solutions must integrate easily with cloud vendors’ APIs.

Cloud-Native Data Integration Leverages the Compute Power of Cloud Data Lakes

Cloud data lakes and data warehouses have the power to transform and process data, eliminating the need to send data outside the platform to a separate transformation environment.

Cloud-native data integration solutions are designed to take advantage of this capability, leveraging the cloud platform, functionality, and language. A cloud data integration solution must be able to meet the following needs to keep your data flowing at optimal speed:

  • Converting Data for Storage: Each cloud vendor has its own storage requirements. Data must be converted to the optimized format (e.g., Apache AVRO or Parquet) before it can be uploaded.
  • Orchestrating Communication: The data integration solution needs to be able to tell various systems – within the cloud platform’s network, from storage to database/data warehouse components – what data to move, where to move it, and what to do with it.
  • Leveraging Cloud Compute for Data Transformations: Data processing and transformation should be performed in the cloud platform. Whether the target destination is a cloud data lake or a processing platform such as Hadoop or Spark, transforming the data in the cloud is more efficient than moving it to an external ETL solution before loading it into the target system.
  • Managing Security Access Controls: Especially in a multi-cloud environment, your data integration solution must be able to adopt each platform’s security features and understand which users can access, read, and change data.

It’s 2019 and moving data is still hard. By understanding the challenges posed by data integration when moving to a cloud data lake, you’re better prepared to overcome them. Choosing a cloud-native data integration tool is perhaps the best way to ensure success and realize the promise of your cloud data lake.

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