Extract, transform, load process; precisely, an ETL process helps collect data from the source system into your data warehouse. Usually, an ETL process envisages loading data in batches or as a real-time ingest process.
The data so fed into the data warehouse is used to provide up-to-date analytical data to end-users to update the warehouse accordingly. Several data warehouses make use of ETL to produce data-driven decisions.
Standard SQL methods are a cost-effective way to get insights into your big data analysis. You get the liberty of setting up any type of data model and run your preferred analytical query.
Read on to learn the tips for better ETL practices and ETL development in data warehousing.
Tips for Better ETL Practices and Data Warehousing
1> Source and Size
The source and size of the file are important factors while copying data. Data warehouses act as databases where all nodes divide to synchronize the work of data ingestion. Ensure to copy data from evenly sized files and multiple sources. A single large file is split into multiple uneven sizes that may or may not work.
2> ETL Runtimes
The entire ETL process requires runtime management of queries. The data warehouses manage the queues dedicated to different workloads.
It’s best to limit the concurrency of workload management within 15 or less. This will enable you to organize and monitor each queue as per the requirement and dedicate ETL processes accordingly.
3> Maintain A Perfect Sheet
If it is data, you need to maintain a table regularly. Regular data segregation, collection, and organization enables the faster transformation of the aggregating data.
It will also help you to protect and perform transformations to derive the best of your data warehousing and data mining efforts. Ensure the database is regularly updated, precisely vacuumed, and analyzed. The analyses of the vacuum system will ensure that you regularly automate the table.
4> Set-Based Operations
As an ETL developer, make sure you prioritize set-based operations over row-based executions in terms of procedural languages. This applies in the case of complex transformations employing data warehousing and business intelligence.
In other words, it is highly recommended that an SQL statement is given priority over the row-based cursor loop.
5> Unnecessary Indexes
Data warehouses often suffer from the challenge of over-indexing. Although on the contrary, indexes are rarely used.
Database administrators create indexes as an all-time approach to resolve every and any problem. In the case of ETL, unnecessary indexes won’t help. It may rather worsen the situation.
6> Nested Loops
An OLTP application prefers the efficient join method of small-scale results. If you are in the middle of selecting a few rows and columns from a big table and then perform joining the rows with another table, nested loops are the fastest way to retrieve the required rows.
In other hand, in the ETL case, usually, more than the required number of rows are joined with other tables, thereby disrupting the execution plan of an ETL statement. It’s best to avoid nested loops to join rows from table to table.
7> Bulk Data Loading
Data warehouses are designed to store data sets filled with queries. A database allows you to accumulate data from multiple sources before you even apply the operation of copying data from a single source to the other.
If you plan to ingest large sets of data containing multiple files, use the option of “manifest file” instead of copying. In addition to that, if you plan to hold data for transforming, temporary staging tables may be of great help.
Besides data ingestion, use the ALTER table APPEND option to swap data from target tables.
8> Monitor ETL health
The ETL health process requires regular monitoring to identify the onset of performance issues. If you plan to keep your data free from data clustering, it’s best to monitor your ETL process beforehand.
9> Reduce Data
Apart from ingesting data from target tables, an ETL developer also focuses on reducing the amount of data.
It is to ensure that there is less work on your table. Tuning in an OLTP application require reduced data so that selective queries can be answered with precision. In a way, the ETL job performance may increase.
Large data sets in the ETL process are recommended to run parallel. A data warehouse capable of running SQL statements in the parallel process must be set up as a default command and enabled automatically.
Those, as mentioned above, may not eliminate data risk from your ETL job performance. However, the chances of precision can be increased.
With the above pointers, export transform load from your legacy system, reduce the load time and improve SQL statements’ structure. Performance issues related to basic principles. Monitor the basics to gain control of ETL development.
Author: YittBox started with one individual trying out the “Gig Economy” freelancing. Providing freelancing IT services, focusing on small-medium size business, quickly exploded. There is an inherent void in the small-medium market with needing customized IT solutions, but not ready to spend the “Big IT” prices on consulting or “Off The Shelf” solutions. Within a year of beginning, a team of individuals had been hired and the freelancing service evolved and re-branded into YittBox and is quickly working to fill that void.