FAQs from Office Hour on Ecosystem SQL ELT

Introduction:

Adopting cloud-based tools and services has significantly transformed modern data engineering. These innovations enable more efficient data processing and real-time data management. This article consolidates some of the most frequently asked questions from a recent session on Informatica SQL ELT (Extract, Load and Transform) and Cloud Data Ingestion and Replication. The insights here highlight best practices, system capabilities and solutions to common challenges in today’s data engineering workflows.

1. Change Data Capture (CDC) in Data Engineering

Question: Is SQL ELT suitable for capturing database changes and transmitting these to third-party APIs??

Answer: While SQL ELT is a powerful tool, it may not be ideal for Change Data Capture (CDC) workflows. For such use cases, Informatica Cloud Data Ingestion and Replication service is a better fit. It effectively captures and processes real-time database changes, which can then be integrated with tools like Kafka for propagating these changes to external applications.?

2. Infrastructure and Performance Considerations for Cloud Data Ingestion and Replication???

Question: Does Cloud Data Ingestion and Replication require an advanced cluster to operate??

Answer: No, the service is designed to function efficiently without the need for advanced infrastructure. It uses a secure agent for seamless deployment, minimizing overhead. For more details, you may refer to Informatica’s official documentation - https://docs.informatica.com/integration-cloud/data-ingestion-and-replication/current-version/getting-started-with-data-ingestion-and-replication/preface.html?

Question: Are there performance benchmarks available for Cloud Data Ingestion and Replication??

Answer: Performance depends on various factors, including the data source, network bandwidth and target system capacity. While specific benchmarks vary by setup, general metrics can be shared upon request to suit individual requirements.?

3. Ecosystem SQL ELT Functional Capabilities:?

Question: How does SQL ELT differ from traditional tools like PowerCenter??

Answer: SQL ELT is specifically designed for modern cloud ecosystems. It leverages native cloud functions, ensuring maximum performance through complete pushdown optimization (PDO). Unlike Informatica PowerCenter, it features an intuitive, drag-and-drop interface that simplifies the creation of data pipelines and streamlines workflows.?

4. Real-time CDC Management and ELT Pipelines???

Question: How can Change Data Capture be managed in SQL ELT from source to target??

Answer: SQL ELT supports log-based CDC, which allows changes to be efficiently replicated to a designated landing zone. This approach minimizes the impact on source systems and enables smooth data processing.?

5. Supported Ecosystems and Compatibility???

Question: Does SQL ELT support CDC for platforms like Snowflake and Databricks??

Answer: Currently, SQL ELT does not support CDC for Snowflake and Databricks. For CDC scenarios on these platforms, Cloud Data Ingestion and Replication is the recommended tool.?

Question: What source and target systems are compatible with SQL ELT??

Answer: SQL ELT supports platforms such as Snowflake, Databricks, Google BigQuery, and Redshift. Certain systems, like SAP HANA, are not yet supported. However, Informatica provides alternative tools to address specific requirements, including partial pushdown capabilities.?

6. Cross-cloud Connectivity and Secure Agent Placement???

Question: How can cross-cloud data transfers be optimized with SQL ELT??

Answer: For workflows involving cross-cloud transfers, such as moving data from Amazon S3 to Snowflake, SQL ELT performs transformations directly within Snowflake. This reduces data movement, enhances performance and ensures efficiency.?

7. Usage Tracking and Cost Management

Question: How can teams monitor their Informatica usage costs across departments??

Answer: While a detailed usage tracking feature is currently on Informatica’s roadmap, the existing system provides metrics like IPU (Informatica Processing Unit) consumption at the folder or project level. This allows for high-level cost tracking to aid in budgeting and resource planning.?

8. Native Functionality Documentation for SQL ELT???

Question: Is there comprehensive documentation of the native functions available across platforms for SQL ELT??

Answer: Yes, detailed user guides specific to each platform (e.g., Snowflake, Databricks) are available. These guides list all supported functions and offer insights into optimizing SQL ELT capabilities.?

??? - [Snowflake Documentation] (https://docs.informatica.com/integration-cloud/data-integration-connectors/current-version/snowflake-data-cloud-connector/part-3--sql-elt-with-snowflake-data-cloud-connector/mappings-in-sql-elt-mode-for-snowflake-data-cloud.html)?

??? - [Databricks Documentation] (https://docs.informatica.com/integration-cloud/data-integration-connectors/current-version/databricks-connector/sql-elt-with-databricks-connector/mappings-in-sql-elt-mode-for-databricks.html)?

9. Ingestion vs. Integration Platforms???

Question: How is Cloud Data Ingestion and Replication different from Cloud Data Integration??

Answer: Cloud Data Ingestion and Replication focuses on ingesting data from applications, databases, files and streaming sources into the cloud. In contrast, Cloud Data Integration is designed for ETL and SQL ELT workflows, enabling the combination of multiple data sources and loading them into target systems.?

Conclusion???

This FAQ provides insights into the capabilities, constraints and best practices for using Informatica SQL ELT and Cloud Data Ingestion and Replication in cloud-based data workflows. These tools are pivotal for streamlining data pipelines and ensuring scalable solutions in multi-cloud or real-time scenarios. We hope this resource answers your questions and supports your efforts in modern data engineering.