Transforming the Architecture of Data Preparation and Storage
The complexity of data preparation and storage often stems not from the individual tools but from how they interact or — more accurately — fail to interact. Shakudo simplifies this by offering a unified platform that seamlessly integrates with a variety of data preparation engines, data storages, and frameworks. For example, if your data team is already comfortable using Apache Spark for data processing, Shakudo allows you to easily integrate with Delta Lake for robust data lake storage. This enables your team to leverage the best aspects of both platforms without needing to reinvent your existing data pipeline architecture.
Reduce Reliance on DevOps Teams With Infrastructure Abstraction
Many organizations find that their data infrastructure is a major obstacle to their data team's productivity, especially as DevOps roles have expanded to include complex ETL tasks — extracting, transforming, and loading data into various storage solutions, from traditional databases to modern data lakes and warehouses. These workflows require coordination across multiple environments, each with its own unique demands for speed and scalability. Shakudo simplifies these challenges by serving as a single platform capable of managing these tasks, freeing up teams to focus on analytics and insights.
A Flexible Approach to Data Management
Managing a variety of specialized tools for tasks like data preparation, storage, and orchestration can complicate operations and elevate the risk of errors. Shakudo streamlines this by integrating with a range of open source and commercial tools. This approach reduces operational complexity and frees teams from the limitations often imposed by vendor lock-in.
Our Data Preparation and Storage Stack Components
Shakudo is compatible with over 20 specialized data preparation and storage tools. This includes data processing and warehousing tools like Doris, Clickhouse, SingleStore, and Snowflake, as well as cloud storage layer. This adaptability is crucial for data teams needing flexibility to quickly adapt to changing requirements and to leverage existing investments in data tools and infrastructure.