Extract, Transform, Load workloads involve a series of data integration processes that extract data from various sources, transform it into a suitable format, and then load it into a target data repository for further analysis and reporting.
ETL workloads are common in data engineering and data warehousing, where data needs to be processed, cleaned, and organized for business intelligence and decision-making purposes.
Extraction is the first step in ETL. It involves retrieving data from various source systems, which can include databases, applications, flat files, APIs, and more. Source data can be structured (e.g., relational databases), semi-structured (e.g., JSON, XML), or unstructured (e.g., log files).
Transformation is the process of cleaning, structuring, and enriching the extracted data to make it suitable for analysis. This step often includes: Data cleansing, validation, enrichment, normalization, and deduplication.
Loading is the final step in ETL, where transformed data is loaded into the target data repository, such as a data warehouse or data lake. Data can be loaded in batches or in real-time, depending on the requirements of the organization. Once loaded, the data is available for analytics, reporting, and business intelligence.
Data transformation accounts for a significant portion of costs in ETL workloads. When analyzing our customers' workloads, transforms represent 3% of their total queries—but 68% of their costs.
Data transformation is often the most computationally intensive and resource-demanding part of ETL workflows. It involves applying various operations, such as filtering, aggregating, joining, and reshaping, to raw data to make it suitable for analysis and reporting.
The cost dominance of data transformation in ETL workloads is a common challenge, driven by the computational complexity and resource demands of transforming raw data into a usable format. To manage ETL costs effectively, organizations should focus on optimizing data transformation processes, leveraging parallelism and scalability, and continuously monitoring and fine-tuning their ETL workflows to strike a balance between data quality and cost efficiency.
To reduce ETL costs with Bodo, data engineers can leverage its extreme efficiency to achieve better performance, resource utilization, and cost savings. Learn more about Bodo's compute engine here.
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When used together, Bodo and Snowflake is an optimal solution that achieves the lowest cost and the highest performance.
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