How do you choose between ETL and ELT for your data pipeline?

In today’s cloud-driven world, modern tools for ETL (Extract, Transform, Load) are essential. Google Cloud Platform (GCP) provides powerful tools that simplify each ETL step. Extract: Google Cloud Storage: For storing raw data files. BigQuery Data Transfer Service: Automates extraction into BigQuery. Cloud Pub/Sub: For real-time data streaming. Cloud Functions: Triggers data extraction from APIs. Transform: Cloud Dataflow: Managed service for batch and stream processing. Dataprep: Visual tool for data preparation. BigQuery SQL: SQL-based transformations. Load: BigQuery: Optimized destination for analytics. Cloud Dataflow: Loads transformed data into BigQuery. Orchestration: Cloud Composer: Manages ETL workflows based on Apache Airflow.

ETL is active data pipeline by make all required process to create unified and standardized data can be in one process. The time will take by ETL data pipeline will depend: - maximum of organized transformation processes. - Time to extract data from origin source. - time to load data in destiny source

Considera??es Importantes ao Utilizar ETL: - Seguran?a e Conformidade: A abordagem ETL é crucial quando é necessário aplicar medidas de seguran?a e conformidade. Isso permite a anonimiza??o e a pré-valida??o dos dados antes de serem armazenados no destino final. -Complexidade: Em cenários onde as transforma??es de dados s?o complexas, o ETL é benéfico, pois permite a aplica??o de filtros e outras transforma??es antes de transferir os dados para o sistema de destino, evitando sobrecarga. -Qualidade: O ETL proporciona um controle superior sobre a qualidade dos dados inseridos, garantindo que apenas dados precisos e úteis sejam armazenados no destino.

Choosing between ETL (Extract, Transform, Load) and ELT (Extract, Load, Transform) depends on factors like data volume, processing needs, and infrastructure. ETL is ideal for smaller datasets and environments with limited processing power, as it transforms data before loading it into a data warehouse. ELT is suited for large datasets and modern data lakes, leveraging powerful cloud-based storage and processing capabilities to transform data after loading, offering greater flexibility and scalability.

ELT (Extract, Load, Transform) is a process where: - Extract: Data is extracted from the source. - Load: The raw data is directly loaded into the target database (often cloud-based or modern data warehouses). - Transform: Data is transformed after loading, usually leveraging the processing power of the target system.

In my experience, it depends upon data needs(volume, transformations, analytics use cases etc). Choose the "traditional ETL" if you have smaller data sets and complex transformations. Choose "modern ELT" if you require real-time data and need data for future analytics. In my opinion, data holds a lot of significance and reduced storage costs make ETL a compelling choice for organizations looking to unlock the potential of data.

ELT is passive data pipeline by just dedicate to extract and load data, and then transform how you can; give you a two step data pipeline separated completly and independ. The time will take by ETL data pipeline will depend: - Time to extract data from origin source. - time to load data in destiny source - delay to use of transformation process. - maximum of organized transformation processes.

Think of cleaning your room in a different way. ELT is like throwing all your toys (data extraction) into a big bin (data warehouse) and then sorting and cleaning them (data transformation) later on. This approach prioritizes getting the data into the warehouse quickly and then transforming it as needed. * Pros of ELT: * ->> Faster Data Ingestion ->> Greater Flexibility ->> More Scalable * Cons of ELT: * ->> Potential for Dirty Data ->> Slower Query Performance ->> Requires More Storage

ELT... The evolution of ETL in the age of CHEAP cloud storage. Gone are the days that you had to upgrade physical storage to increase space. Extracting doesn't change much here figure out where your data is and develop any of a number of methods to pull it. But... Now just load it somewhere. In medallion architecture that somewhere will be something like a S3 bucket (AWS), a Azure Data Lake Storage (ADLS), or any other flavor of storage container, and you get to keep it in it's original format. JSON from a API call, store it, Parquet file from pulling data out of a system with python and dumping that even cooler, csv files for your legacy CRM dump it in. Then transform and query at will... Great for ML and troubleshooting DQ issues.

** Pros: - Controlled transformation: Transformations happen before loading, ensuring data consistency. - Data accuracy: Allows pre-processing and validation before storage. - Fits legacy systems: ETL works well with traditional, on-premise data warehouses that have limited compute power. ** Cons: - Time-consuming: Data is transformed before loading, adding to overall processing time. - High upfront costs: Requires dedicated infrastructure for data processing. - Scalability issues: ETL can struggle with scaling when working with large datasets or complex transformations.

ETL - Extract, Transform and Load between source and target system. Pros: - Include data cleaning and validation enabling quality - Transformation enables clean data loaded in the warehouse - Offload processing from source systems - reducing load on transactional systems - Capable of storing historical data for analysis Cons: - Latency causing a delay in data availability - Designing the ETL process is complex for large data sets - Dependancy on source systems ( no staging )

Pros: - Cost effective, usually avoids storing raw data and uses only processed data Cons: - Depends on source data if you need to reprocess anything - Pipelines can get very complex

Transforming the data first... Cons: - Performance: Data sets are not getting smaller or simpler. Transforming the data outside of a tool or system that is optimized to do it can be time consuming. Slowing down the delivery to the end user. - Which leads me to Scalability and Flexibility - Transforming the data up-front sometimes leads to loss of data b/c you decide it is unneeded or an aggregation happened and you lose lose granularity. And you just don't have the ability to scale at that point. Pros: - Data is clean and validated up front (which can also be a con when troubleshooting-this will happen) - Your data is in a consistent format, usually a tabular / table format. - Transform is completed and data is ready to be analyzed.

** Pros: - Speed: Faster because raw data is loaded without waiting for transformations. - Scalability: Works well with modern cloud data warehouses (e.g., Snowflake, BigQuery) which handle large volumes of data efficiently. - Flexibility: Transformations can be performed as needed, after data is available. - Lower infrastructure costs: Reduces the need for expensive on-premise servers. ** Cons: - Requires powerful target systems: ELT relies on the target data warehouse having strong compute and storage capabilities. - Potentially unstructured data: Loading raw data without transformation can lead to messy data storage if not managed well. - Complex transformations: May become challenging if raw data contains significant anomalies.

ELT - Extract, Load and Transform between source and target system. Pros: - Low latency increasing availability of data in the warehouse - Scalability to leverage processing power and parallel processing - Real-time data processing - Easy design and maintenance Cons: - Data quality concern limiting transformation - Resource requirements on the warehouse for complex transformation and cost - Historical data changes may require additional considerations when using an ELT approach.

Pros: - Raw data is available for reprocessing or for new analysis Cons - Costs can scale quickly, both for storage and for processing - Requires strong documentation

ELT Cons: Processing costs can be pricy. It is important to make your transforms as efficient as possible to control cost. Increased storage requirements and complexity. Storing various file types and accessing those can both take up space and create additional complexity you don't have with ETL. Pros: Scalability and Flexibility - You can easily "crank up" your platform to meet the storage and processing needs (with processing often being a premium) Speed: ELT gets the data in fast meaning more real time reporting and analytics. ML availability to historical data. Pulling and storing all the raw data means that your DS can user ML to train data at any granularity without DEs (like me) having already taken that from them!

ETL: - Best for legacy systems with limited storage/computation capabilities. - Ideal when data needs to be standardized before entering the target system. - Useful for companies with complex data transformations that must happen early in the pipeline. ELT: - Best for cloud-based, scalable systems that can handle large datasets and complex transformations post-load. - Ideal when speed and flexibility are prioritized, and transforming data in real-time or on-demand is essential. - Suitable for organizations leveraging big data or unstructured data that need raw data available for later use.

ETL performs data transformations outside of the target data warehouse, often in a separate processing environment. ELT performs transformations within the target data warehouse, utilizing its processing capabilities.

Selecting between ETL and ELT hinges on data volume, velocity, and complexity of transformations. ELT may be preferable for large data volumes and simple transformations, leveraging the target system's processing power. ETL suits complex transformations and real-time processing, as transformations occur before loading. Consider the capabilities of the target system and adherence to data governance and compliance. Evaluate tooling, infrastructure, and organizational expertise for seamless implementation. Ultimately, the decision rests on understanding data requirements, processing needs, and organizational constraints. Experimentation and iteration may be needed to determine the optimal approach for the specific use case.

I do not want to seem short winded but I believe it comes down to amount of data in transit, data reporting needs (end user capability) and the repetitive nature of updates. If I find the process needs to be revisited by my engineering team often we will update the pipeline to ELT. When the workflow is super structured or even inherited from legacy processes I will leave it as so. When planning for the future or working with Data Scientists I will use ELT which gives more autonomy on the process for them.

As often when we talk about data, it depends. First thing to consider is the tools and infrastructure available. ETL uses less storage, so it was developed in the time of on premise warehouses. ELT started to be possible once cloud storage became very accessible (but we should be careful anyways, because costs can scale). After that, we need to consider the architecture. What are the data sources (structured vs unstructured, how many sources, streaming vs batch), and how data will be delivered (a well designed data warehouse, a more flexible data lake, data mesh, etc).