ETL, which stands for “extract, transform, load,” are the three processes that move data from various sources to a unified repository—typically a data warehouse. It enables data analysis to provide actionable business information, effectively preparing data for analysis and business intelligence processes.
As data engineers are experts at making data ready for consumption by working with multiple systems and tools, data engineering encompasses ETL. Data engineering involves ingesting, transforming, delivering, and sharing data for analysis. These fundamental tasks are completed via data pipelines that automate the process in a repeatable way. A data pipeline is a set of data-processing elements that move data from source to destination, and often from one format (raw) to another (analytics-ready).
What is the Purpose of ETL?
ETL allows businesses to consolidate data from multiple databases and other sources into a single repository with data that has been properly formatted and qualified in preparation for analysis. This unified data repository allows for simplified access for analysis and additional processing. It also provides a single source of truth, ensuring that all enterprise data is consistent and up-to-date.
There are three unique processes in extract, transform, load. These are:
Extraction, in which raw data is pulled from a source or multiple sources. Data could come from transactional applications, such as customer relationship management (CRM) data from Salesforce or enterprise resource planning (ERP) data from SAP, or Internet of Things (IoT) sensors that gather readings from a production line or factory floor operation, for example. To create a data warehouse, extraction typically involves combining data from these various sources into a single data set and then validating the data with invalid data flagged or removed. Extracted data may be several formats, such as relational databases, XML, JSON, and others.
Transformation, in which data is updated to match the needs of an organization and the requirements of its data storage solution. Transformation can involve standardizing (converting all data types to the same format), cleansing (resolving inconsistencies and inaccuracies), mapping (combining data elements from two or more data models), augmenting (pulling in data from other sources), and others. During this process, rules and functions are applied, and data cleansed to prevent including bad or non-matching data to the destination repository. Rules that could be applied include loading only specific columns, deduplicating, and merging, among others.
Loading, in which data is delivered and secured for sharing, making business-ready data available to other users and departments, both within the organization and externally. This process may include overwriting the destination’s existing data.
ELT (extract load transform) is a variation in which data is extracted and loaded and then transformed. This sequence allows businesses to preload raw data to a place where it can be modified. ELT is more typical for consolidating data in a data warehouse, as cloud-based data warehouse solutions are capable of scalable processing.
Extract, transform, load is especially conducive to advanced analytics. For example, data scientists commonly load data into a data lake and then combine it with another data source or use it to train predictive models. Maintaining the data in a raw (or less processed) state allows data scientists to keep their options open. This approach is quicker as it leverages the power of modern data processing engines and cuts down on unnecessary data movement.
ETL tools automate the extraction, transforming, and loading processes, consolidating data from multiple data sources or databases. These tools may have data profiling, data cleansing, and metadata-writing capabilities. A tool should be secure, easy to use and maintain, and compatible with all components of an organization’s existing data solutions.
Snowflake and ETL
There are several potential points of failure during any ETL process. Snowflake eliminates the need for lengthy, risky, and often labor-intensive ETL processes by making data easily accessible for internal and external partners via secure data sharing and data collaboration.
That said, Snowflake supports both transformations during (extract, transform, load) or after loading (extract, load, transform). Snowflake works with a wide range of data integration tools, including Informatica, Talend, Tableau, Matillion, and others.
In data engineering, new tools and self-service pipelines eliminate traditional tasks such as manual ETL coding and data cleaning companies. Snowpark is a developer framework for Snowflake that brings data processing and pipelines written in Python, Java, and Scala to Snowflake's elastic processing engine. Snowpark allows data engineers, data scientists, and data developers to execute pipelines feeding ML models and applications faster and more securely in a single platform using their language of choice.
With easy ETL or ELT options via Snowflake, data engineers can instead spend more time working on critical data strategy and pipeline optimization projects. In addition, with the Snowflake Cloud Platform as your data lake and data warehouse, extract, transform, load can be effectively eliminated, as no pre-transformations or pre-schemas are needed.
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