What are some ETL data transformation techniques to improve your data warehouse?

Most of the analytical challenges come when we don't have foundation data in the right shape and form to be analysed. Data cleansing on a large dataset could bring in huge amounts of processing overhead so it can't be done indiscriminately on any and every attribute, there has to be a fine balance of choosing the right attribute to be cleansed for a better performance. The thumb rule for data cleansing should be to avoid duplication at the target using constraints. However, this would work only in relational targets but as of today most of the data is moving to the data lake which is predominantly file based so having an upsert strategy would yield the best outcome for duplicate management using its ACID compliance capabilities.

The first crucial factor is to understand the data asset. Why we need that and how we use it? That gives a perspective of what we need to do in terms of cleansing or validating or what subset of features to be bring it or how we partition it. Most of the time that throught process is never done. Organizations assume that all the data is useful and bring it features that may or may not be used in its life time. And also most will not consider the data life cycle in an earnest way. That will help in when to archive or delete the data. This thinking not only optimize the various ETL process but also help in optimizing the costs across computing and storage.

ETL is now ELT , the key principle is to have the data modeling deferred to the point when you need the data. New Business ecosystems have bought massive volumes of data while the need to have all of it at same time is minimal. Hence this approach saves time, computation and hence provides better speed to value for business with minimal architecture.

To enhance your data warehouse, consider ETL data transformation techniques like data deduplication, data normalization, aggregations, data cleansing, and data enrichment. These methods help ensure data quality, reduce redundancy, improve query performance, and enhance overall data warehouse efficiency. Additionally, implementing version control, error handling, and data lineage tracking can further enhance ETL processes and maintain data integrity.

It is very important that you standardize the data before loading it into a data warehouse. I faced a lot of challenges when extracting data from multiple data sources such as CSV files, Oracle, DB2, SQL Server. For example: there were instances where same customer name was spelled differently in different databases and also date formats were not consistent across different sources. To effectively create a golden copy of data (single source of truth). It is necessary we standardize the data and load it into a common staging area and then do the further data cleansing steps as per the business requirements. It also enable us to perform reconciliation of data once it's loaded as all the source data is stored in a common staging area.

Following techniques helps us a lot: 1. Use nologging for staging tables, which have frequency truncate / insert. 2. Use parallel ETL jobs instead of sequences. It will save a lot of time, divide the sequence job to parallel multiple jobs by using filter (etc where clause). 3. Once job completed on staging level, use exchange partition instead of insert command for rows insertion into fact table. 4. Tables stats must be updated for fetching data and completion of jobs on time. Stale stats on tables may result in performance degradation. 5. Avoid select * from (on FACT) tables, because it will fetch all columns and scans whole table as well, if filter are not used.

I previously said data cleansing was fun, and so is data integration. Imagine this: you must combine survey data from CSV files, text files, databases, and online forms stored in JSON format. Suppose you were to use spark, data bricks, or aws lambda based on your tech stack. After reading it from different sources, you could combine all this data and finally get a table document or JSON in your desired format. The whole process from reading, cleansing, transforming, and integrating is the most fun about data. But wait, I am still going; the aspects of data you focus on give you unbelievable insight to understand a population or your customers.

There is a better way to get out of the DI chaos and this is by exploring the virtual data warehouse method. DI chaos is why majority of DW projects fail or become obsolete. Programming will never be able to cope up with the business changes that occur and should be therefore avoided. DI must also tie up with the enterprise Data Governance Strategy or chances for success is lower.

My first thought here is to always understand the data first! I've seen too many data integrations made hastily, only to later untangle them to make sense. It's crucial to understand the NEED, such as the business requirements, data quality, and performance constraints, before diving into the wants of the data integration process. Having a way to track data lineage is imperative, which is why I appreciate tools like DBT. It provides features like version control and the ability to write modular SQL queries, making it easier to manage and understand your data pipeline. If you're dealing with data from multiple time zones, it's beneficial to standardize the time data. Consider using a universal standard like UTC.

Always consider your query patterns. For instance, if most queries filter by date, range partitioning on the date column can be a game-changer. However, don't over-partition; it can lead to tiny partitions (known as partition skew), which can degrade performance. Also, remember that partitioning strategies might evolve as data grows and access patterns change. Regularly review and adjust as necessary. And while tools can assist, there's no substitute for understanding your data's nature and how it's consumed.

Implement partitioning based on logical divisions, like date ranges or key ranges, to optimize query performance. Align partitions with query patterns for targeted data retrieval. Regularly maintain and prune partitions (to eliminate unneeded partitions) for efficient storage management. Consider using a combination of partitioning and indexing for optimal results.

In fact, it's one of the features that has the greatest impact on database maintenance and also on query and data processing performance. It is practically impossible to manage a large database without using this feature.

It is important to note that this idea and approach is viable if the project intends to keep historical copies of data for government organizations who deals with the likes of pension benefits, demographics, civil records, taxation, insurance, health etc. Cloud resources is clear and proven solution that must be explored if physical data partitioning is still warranted.

This depends on the project and the data structure. For the ingestion of raw data, choose a partitioning structure that is simple, scalable, and unaffected by changes in the data source schema. This should align with your data processing goals, such as partitioning by date. For ETL processes, plan the partitioning so it's optimized for querying as soon as the data lands in the warehouse; this should be planned for in the design phase. Building flexible and reliable data partitions is crucial. Some databases like Redshift and BigQuery offer options to manage partitioning at a more granular level and also have built-in automatic partitioning optimizations.

In many situations, data compression is essential. But there are also situations where not compressing data increases the performance of some operations. For each case, I always prefer to make the final decision after testing, and without compromising the environment's capacity.

Data compression is another piece of the data pipeline that is extremely important. Previously, we had to use physical hard drives to store data, which was space and cost. With the advent of cloud solutions, we can keep data in cold storage for a fraction of the price as long as compliance dictates or is needed based on different terms. To save money on cloud solutions, use Glacier or Google archive storage for infrequent, long-term data. Compress files with zip, rar, or gzip to be able to maximize this method of storage

In the olden ways this may have been a practice to solve resource challenges. Not anymore. The concept of the Virtual Data Warehouse is changing how we look at "to-be" data minus the pains of building a physical data warehouse.

Data compression does not affect the ETL process, it is just a way to compactly store data. I would remove this article from consideration. There are many other interesting issues in the ETL processes. For example, the Staging area.

In a scenario of building a Data Warehouse/Data Mart the creation of index helps a lot searching data, but decreases performance on load, a good strategy for this is: 1. Delete index before insert 2. Insert data 3. Recreate Index 4. Be happy.

Data indexing can significantly improve read speeds but may also extend the time it takes to load new data into the database, as each entry must be indexed. For this reason, use a minimal number of indexes when dealing with large incoming data sets. Indexes should be applied only where they are truly necessary. Indexes should be tailored to your specific needs; whether they are single-column, composite, or full-text indexes, each serves a unique purpose. Indexes aren't a 'set it and forget it' element; they require regular maintenance to ensure optimal performance.

Regularly monitor and fine-tune indexes for evolving data patterns. Best approach is to delete indexes before inserting and recreating indexes once data is inserted. Utilize selective indexing to enhance query performance by focusing on high-impact columns. Also, optimize index usage and avoid over-indexing to minimize storage overhead.

Data operations must be efficient in manipulation, search and visualization. Comprehensive architectures include Data cleansing, Partitioning and Sharding, Compression, Integration, Indexing etc. Workload (data and operations) should be distributed with lineage control, consistency and availability. For distributed operations, It's important to have efficient strategies for back up, restoration and archiving. Data replication and reshaping is important for visualization and data analysis. Centralization to Decentralization is also an important topic. AI and Data Fabrics would be in high demand. Which can address complex and systematic solutions. Contents Generation and Digital Twins would also be next topics.

All previously described items are all necessary steps in the creation of a DWH. However, in my experience, one common obstacle is the reduction of the data from the details (sale contracts, patient visits) to a higher level perspective (customers, patients). This usually involves a few aggregations and sometimes the calculations of some KPIs.

We were in an era of ETL in the past and ELT is taking the storm in recent times. ELT is Extract, Load and Transform where the Transformation happens after loading into the datbase or datastore. ELT was so popular since large scale distributed processing was done by Spark etc in the past (and it is still relevant). But due to the advent of powerful ddatabase architectures like Snowflake, BigQuery, Redshift etc, Extreme parallel processing of huge amount of data is possible to do within the database. The benefit of having the data transformation inside Database is the simplicitity of skillset required since transformation rules are often written in native SQL. Frameworks like DBT makes it even more easier by providing complete data lineage

As we embrace technological advances in other industries such as EVs, AI, Cloud, etc. DW practitioners and architects must re-think and re-assess our value to our stakeholders and ask "Are we still relevant?". This article had it source in the 80s or even earlier and still preaching it like much of it still applies. If you truly love this practice then go outside the box to learn new concepts and techniques to better serve be relevant to your stakeholders. I recommend the idea of a Virtual Data Warehouse that is now supported by solutions you find in major cloud ecosystems. As you would discover that new paradigms based on old techniques are changing the olden ways. There is still so much to discover and learn...

Absolutely, those techniques listed can definitely make your data warehouse better. They'll give you more reliable and valuable data for your analytics and reporting. And here's another tip: don't forget about handling errors. Having strong error handling in place to deal with unexpected hiccups during data transformation is a real game-changer.

Parallel processing (e.g. Apache Spark) makes the full transformation workload speedier and more efficient. If you need to perform multiple operations on the same dataset Apach Spark's in-memory caching is very powerful. Consider also if the full workload is always needed or if an incremental load would be sufficient to process only the new or modified data. Lastly, the choice of data formats and compression types can significantly impact the efficiency of processing the data.