When is it better to use ETL instead of ELT for data integration?

Decision depends on business case (if it Raw, standardised or transfored data), target data repository (storage), latency. Both ETL and ELT has its purpose and should be used based on above decisions or requirements of the data. In traditional landscape of raw data were you need to maintain the grain same as the source system with ELT is recommended and ownership of data quality remains with the source data producers. Once the data is stored transform it based on the business case for downstream data layers. This approach definitely increases the storage and latency on availability of the data for consumtiom but has traceability for any regulatory needs. If your use case is otherwise then ETL is preferable.

ELT - Mostly in case of DataLake. Since we are not sure about the final reports to be extracted out of raw data, so we just Extract and Load the data and decide about Transforming it based on requirement. ETL - Mostly in case of directly populating a DataWareHouse or DataMart. Since we know from starting that what is needed as a outcome of this process, so we Extract only required data and Transform it as per requirement and then finally Load it in DataMart or DWH.

decision between ETL and ELT will determine your data storage, analysis, and processing. So, before choosing between the two methods, it’s important to consider all factors. This includes the type of business you are running and your data needs.

Choosing between ETL and ELT in data integration depends on specific business needs, data volume, and structure. ETL, involving a separate processing server, is suited for businesses with predefined data models requiring high data quality, usually dealing with smaller, structured datasets. In contrast, ELT, transforming data directly within the warehouse, is apt for handling vast amounts of varied data, offering flexibility and speed, especially beneficial when insights are sought from unstructured or voluminous data. Analyzing business requirements, data characteristics, and transformation complexities is essential to make an informed choice.

In the traditional ETL, data is extracted from various sources first. These sources could be DBs, spreadsheets, logs, or any other data storage systems. Once the data is extracted, it is transformed. Transformation involves cleaning, filtering, aggregating, & converting the data into a format suitable for analysis. After transformation, the cleaned & structured data is loaded into a data warehouse for further analysis and reporting. ETL is often used when organizations have large volumes of data that need to be cleansed, integrated, and transformed before being loaded into a data warehouse. It's an excellent choice when the data needs to be refined and enhanced before it can be effectively utilized for business intelligence.

ETL helps when you know use case and purpose of data before storing it, Whereas ELT gives you an opportunity to store the data first and then based on usage and purpose transform it.

Metaphorically: There's massive amounts of incoming ingredients and variety of dishes, planned and unplanned/ad hoc / experimental are to be prepared. if you clean (and discard something that you think are unusable, as of now) may make it unusable for some dishes. Better bring in everything, then process on need basis. some level of arranging, labeling, organizing will surely help prepare them for specific dishes.

ELT is the modern approach to loading potentially large volumes of data (Petabytes) into Cloud based Data Warehouses such as Google BigQuery or Snowflake.

The decision to use ETL will depend on how the data is put to use after it landed in to a database! Besides, it also needs to be seen how soon the finished, standardized data with all integrity jives well for reporting and analytics! Sometimes it is better to adopt ELT if we have to quickly make the data available and the transformation and standardization can be done quickly within the database using local tools and utilities.

In my experience, ELT solutions provide flexibility in development where RAD concepts have to be employed. I have used it in strictly OLAP focused environments, where OLTP and data integrity concerns were managed by service provides and our solution simply loaded and refreshed the source data on a schedule or as needed. ETL, by comparison, involves applying logic to the source data in transit where ELT is a pure copy of the target data and schema.

Pros: * ETL integrates data from various sources. * It improves data quality. * You can store historical data for analysis. * It scales well for handling large data. Cons: * ETL introduces processing delays. * Building and maintaining ETL can be complex and costly. * There's a risk of data loss during transformation. * ETL can struggle with rapidly changing data sources. * It needs ongoing maintenance.

Metaphorically: ingredient=data. dish=insight pros - Optimized storage, you keep what is needed, in compact & clean form Ingredients are in consistent consumable form cons - initial screening, cleaning, processing of ingredients take time. so sometimes it frustrates your chefs that although the ingredients are already in, they can't start using quickly until your preparation is completed.

If storage volume is limited in your Data Warehouse and you are trying to load and analyse large datasets eg clickstream, ETL has a major advantage in it can load pre-aggreggated (ie transformed) data. One big disadvantage with ETL in modern Cloud platforms such as BigQuery, is you no longer have the raw data - this often limits you from doing further analytics, makes troubleshooting difficult and from an auditability standpoint you don't have the data lineage that ELT allows.

ETL is often the preferred method when the data quality and data transformation are the top priority. 1. When there is a need to consolidate and clean data from multiple sources. The transformation step in ETL can handle complex data transformations, data quality checks, and ensure the consistency of data. 2. When the target system cannot handle transformation due to lack of computational power or the capability to perform complex transformations. In such cases, it's better to transform the data before loading. 3. While dealing with sensitive data ETL allows to mask or encrypt sensitive data before loading it into the target system, thus ensuring data privacy and meeting compliance requirements.

ETL (Extract, Transform, Load) is often preferred over ELT (Extract, Load, Transform) in instances where there is a large volume of data, complex transformation logic, a need for data quality and governance, limited compatibility between source and target systems, or when security and privacy are critical. ETL allows for optimized performance, separate transformation processing, data cleansing, and additional security measures before loading data into the target system, making it a better choice in these scenarios.

ELT is often chosen when there’s a need for real-time or near-real-time data integration, as data is loaded first and then transformed within the target system. If you target data warehouse or analytics platform ELT has robust transformation capabilities and can handle raw data efficiently. ELT can be cost-effective because it eliminates the need for a separate transformation server or cluster, utilizing the processing power of the target system. If your data sources frequently change their schemas, ELT can be more adaptable since it loads data as-is and performs transformations later.

ELT allows for rapid creation of data ingestion pipelines into Cloud Data Warehouses. Google BigQuery for example has native support for no-code ingestion from a range of data sources, from spreadsheets to Google Analytics. Ingestion services such as Fivetran, enable no-code ingestion from hundreds of platforms. It can really reduce development time. Tranformation can be done with modern tools like dbt - and you end up with an awesome Data Warehouse tech stack.

In start-up types of environments, having the source data available in anticipation of unseen needs, that come through without warning, is of critical importance. The main con to this approach is storage costs. The pros in my use case outweighed the cons in that all our processing and staging happened on our own computing resources. This eliminates the network dependency that can halt in-flight transformations, typical in some ETL processes. Straight copies of data (with no transformation coding) from OLTP Source to OLAP Destinations move faster and are less exposed the processing errors. ELT allows us to focus full resources to tasks that are more simplistic and devote full force to their completion before applying business logic.

Metaphorically: ingredients=data. dish=insight pros- No loss of any incoming ingredients, gives flexibility to the chefs to be creatively use on-demand Better freshness cons - If some chefs are starting their job before bulk, basic processing of ingredients is done, there is potential risk of quality of the dishes, inconsistent tastes and lot of duplicate efforts by multiple chefs

ETL gives you a more fine grained control over the data that gets ingested, but it could often lead to limited flexibility for more complexed use cases. Where as ELT gives you the flexibility for more use cases and saving resources by processing at the data collection. Thus, ELT is great when you are storing the data for future analytics.

Metaphorically: ingredients=data. dish=insight Use "acquire, store and then/later process" approach when dishes are not well defined, ingredients acquired from trusted clean source, freshness is top priority. Use "acquire, process & store" approach when dishes are well defined, quality is much more important than variety and freshness, optimizing storage is important due to massive incoming volume of which you can only use a small portion, your chefs do not need granular level control

ETL is a better choice if data consistency and quality are crucial, and you intend to use a relational database as a DWH. It is computationally expensive, allows for clear and enforceable data definitions, and makes analysts' job more straightforward. On the other hand, changes to business definitions are slower, expensive and require a change in the process (possibly include data engineers from a different team). ETL is generally better for existing businesses with solid, clear, long-standing definitions. ELT is mostly about "get the data first, figure out how to use it afterwards". Transformations may be done by analysts, and business definitions change faster. Usually, as a business matures, the data streams migrate from ELT to ETL.

ETL focuses on transforming data before loading it into a storage system, ensuring that it's clean and consistent. ELT, on the other hand, loads raw data into a storage system first and then performs transformations within the storage environment. The choice between ETL and ELT depends on the specific requirements and characteristics of the data being processed and the organization's analytical needs.

Let's explore a situation where ETL might be a better choice! ??When data quality and consistency are crucial, ETL can be the go-to option. For instance, in healthcare, ?? data accuracy is vital for patient safety and compliance with regulations. ETL allows us to cleanse, validate, and standardize data before loading it into the target system, reducing the risk of errors. ??Another scenario is when we need to secure sensitive data with encryption or masking. ETL enables us to apply these changes before loading the data into the destination, ensuring data privacy and compliance. ?? #DataIntegration #ETL #ELT #DataQuality #DataSecurity #HealthcareData

ETL (Extract, Transform, Load) is often preferable when the transformation logic is complex, the source systems are diverse, or you need to ensure data quality before it enters the data warehouse. ELT (Extract, Load, Transform) works best when leveraging the power of modern cloud-based data warehouses for transformation. Choose based on your infrastructure and business specific needs!

ETLs or ELTs are taking a new dimensional shift. In today's data landscape it is no code / low code, zero ETL, continuous data pipelines are more evident than the traditional Data Engineering ETL direction. We build more domain oriented data products with point to point pipelines and lot of the cases even decentralized. Also, for security and privacy are attributing to consider a range of data pipelines ontology. For ML workloads, we are leveraging E(-T)L to preserve features and training the model as needed. In my view, I see more real-time, windowing EL patterns in the modern Data and AI platforms.

The timeless debate between ETL and ELT presents an exciting challenge, as both paradigms have stood the test of time. In my experience, the world of no-code/low-code solutions is open to explorers on either path. However, the determining factor lies in the technology landscape when developing data pipelines. I'm an advocate for pushing the boundaries of technology to the fullest. Imagine having the likes of Snowflake or BigQuery in your toolbox, it makes sense to leverage their architecture and capabilities to efficiently process large volumes of data at a lower cost But here's the twist: when you find yourself loading data into platforms such as GCS or S3, the ELT approach might not be your choice.

The involvement of an experienced business analyst is paramount in translating intricate business requirements, especially when deciding between ETL and ELT. Their proficiency in bridging the gap between technical intricacies and business objectives is instrumental in making informed decisions that align with organizational goals.