We achieved this by implementing a multi-tiered storage solution, automating data lifecycle management, and optimizing data access patterns. Our approach involved a combination of on-premises and cloud storage, with a focus on cost-effective solutions such as Amazon S3 and Google Cloud Storage. We also leveraged open-source tools like Apache Spark and Apache Hive to manage and analyze our data.
Our data platform must be able to support these workflows, and our data and BI analysts must have the tools they need to perform their jobs effectively. In this article, we will explore the key components of a data platform that can support these workflows, and the tools that our data and BI analysts need to perform their jobs effectively.
Key Components of a Data Platform
However, as our data volume grew, we encountered limitations with Debezium Slots, such as the inability to handle large-scale CDC events and the lack of support for certain databases like MySQL. To overcome these challenges, we transitioned to using Debezium Connectors, which offered a more robust solution for CDC events. Debezium Connectors provided better scalability and supported a wider range of databases, including MySQL. This transition allowed us to handle larger volumes of data and improved our overall data ingestion process. Another significant improvement was the integration of Hevo with Snowflake, our data warehouse. By leveraging Snowflake’s capabilities, we were able to store and manage our data more efficiently. Snowflake’s unique architecture, which separates compute and storage, allowed us to scale our data storage and processing capabilities independently. This separation enabled us to optimize our data storage and processing costs, as we could allocate resources based on our specific needs. Furthermore, Snowflake’s support for various data formats, such as Parquet and Avro, made it easier for us to ingest and process our data. We were able to leverage Snowflake’s native support for these formats, which eliminated the need for additional data transformation tools. This streamlined our data ingestion process and reduced the complexity of our data pipeline.
We also wanted to ensure that our data was always available, even during peak times or unexpected outages. After careful consideration, we decided to use Amazon Redshift as our data warehouse solution.
Why Amazon Redshift? Scalability: Amazon Redshift is a fully managed, petabyte-scale data warehouse service that can handle massive amounts of data. It can scale up or down based on your needs, ensuring that you always have the right amount of resources. Performance: Redshift uses columnar storage and massively parallel processing (MPP) to deliver high performance. This means that it can process large amounts of data quickly and efficiently.
We also introduced a new data pipeline for our legacy data, which is now being ingested into our data lake. We are now able to run our ETL jobs in parallel, which has significantly reduced our data processing times. We have also implemented a new data governance framework, which includes data quality checks and data lineage tracking. We are now able to track our data from ingestion to transformation, and we can easily identify any issues that arise. We have also implemented a new data catalog, which allows us to easily discover and access our data. We are now able to easily find and access the data we need, and we can easily share it with our team members. We have also implemented a new data visualization tool, which allows us to easily create and share visualizations of our data. We are now able to easily create and share visualizations of our data, and we can easily share them with our team members. We have also implemented a new data security framework, which includes data encryption and access controls.
Employed Confluent Cloud’s Schema Registry for schema management. Implemented Confluent Cloud’s Schema Registry for schema evolution. Used Confluent Cloud’s Schema Registry for schema validation. Integrated Confluent Cloud’s Schema Registry for schema enforcement. Leveraged Confluent Cloud’s Schema Registry for schema versioning.
The combination of dbt and DuckDB offers a powerful toolkit for data engineering, with dbt handling the orchestration and transformation, and DuckDB providing a lightweight, efficient querying engine.
Orchestrating Data Transformations with dbt
dbt (data build tool) is an open-source tool that helps data engineers and analysts build and manage data transformation pipelines. It uses a domain-specific language (DSL) based on SQL to define and execute data transformations. dbt allows users to create modular, testable, and maintainable data transformation pipelines, making it easier to manage and scale data projects. dbt allows users to define data transformations using SQL
Fast, Interactive Querying with DuckDB
DuckDB is an in-process, embeddable analytical database engine that is designed for fast, interactive querying.
The datasets are then stored in S3, and the Glue catalog is updated with the new datasets. The Glue catalog is a central repository that contains metadata about the datasets stored in S3. It allows users to easily discover and access the data they need. The Glue catalog is automatically updated as new datasets are created, ensuring that users always have access to the most up-to-date information. The Glue catalog also provides a unified view of all the data stored in S3, making it easier for users to find and access the data they need.
We can query data from both Hive and S3 simultaneously, making it a powerful tool for our data analysis needs. Trino’s performance is impressive, with the ability to handle over 1,000 concurrent queries. This ensures that our data processing tasks are executed swiftly and efficiently. Trino’s compatibility with various data sources, including Hive, S3, and others, makes it a versatile choice for our data analytics requirements. Its ability to handle large volumes of data and provide fast query execution makes it an ideal solution for our data processing needs.
I would also like to thank my colleagues at Hevo and Airflow for their support and guidance. I would like to thank my family for their unwavering support and encouragement throughout this journey.
Hevo vs Airflow: A Comparative Analysis
In the realm of data integration and management, two prominent tools have emerged as frontrunners: Hevo and Airflow. Both tools offer unique features and capabilities, catering to different needs and preferences.
