Improve ETL performance

How to Improve ETL Performance in 10 Easy Ways

Business intelligence operations have become mandatory to improve ETL performance in today’s cut-throat competitive world. Extracting, Transforming, and Loading data for computation, storage, and analysis is becoming more time-consuming, with the constant increase in data volume.

Resultantly, it gets more challenging to sift through data and make pathbreaking inferences as your business scales up. Most data analysts don’t realize how well a belligerent ETL pipeline can perform when it’s optimized from the start itself.  

A data pipeline’s configurations should cater to client requests, so that it reveals BI insights with little user effort. Once this process is mastered, rest assured you will resonate with the top 10 ways to improve ETL performance tricks listed below.

10 Tips to Improve ETL Performance

Here are the top 10 tips to help you improve ETL performance: 

1. Filter Datasets and Load Incrementally

As business databases grow in size, so do the number of unfiltered datasets. If you don’t reduce the load on your unfiltered datasets, you will end up lengthening your ETL pipeline. Hence, the prudent step is to filter your datasets from the start. This step lessens the inherent challenges as your database scales along with your business.

Avoid overloading your ETL pipeline with unnecessary operations, especially when it comes to data loading. Instead, you can batch data into small sets and load each set as per your business’s current priorities. This process is also applicable when you are loading data from multiple sources.

2. Parallel Data Processing

Image showing parallel processing methodology to improve ETL performance
Parallel processing options to improve ETL performance

Parallel processing is ideal for large data sets. With parallel processing capacity, your ETL pipeline counts towards the segregation of data and computation nodes. 

Loading data in large, uneven sizes can make it more difficult for your pipeline to ingest the data loads. The idea is to break up the data into equal-sized segments. Parallel processing complements horizontal scalability, thereby giving you plenty of options to work with.  

For SQL queries, you can even enforce an optimizer to process in parallel by using the following hint:

/*+ parallel */ or /*+ no_parallel */

Additionally, it is better to rely on a single copy command for batch data loading. Today, most BI tools let you process data tasks in parallel. All this is possible, even when data tasks use different data sets. Each of these tasks are brought together using a series of sort and aggregate functions.

3. Avoid Nested Loops

Nested loops give birth to complex queries. They are especially effective in the case of smaller datasets. Technically when you couple them with index scans, loops can help you retrieve data accurately even from the most congested databases. However, ETL tasks usually have to read through too many rows of data while joining them with other tables.

Hence, it is better to use ‘Hash Joins’ than Nested Loops. Make sure you do not consciously indulge in nested loop joins. These will usually end up causing optimizer errors, given the baseless where conditions and indexes.

4. Using Conditional Keywords

It isn’t possible to escape the usage of conditional statements. However, merely using where conditions are not an ideal practice. On the contrary, some poor practices include the use of expressions and methods inside subsequent where conditions.

Complex queries like functions written inside filter conditions throw off the optimizer’s cardinality estimation. In some instances, using expressions can suffice instead of multiple or statements that might confuse the optimizer.

For clarity, you can imagine a table with thousands of rows. If there are three PL/SQL function calls in the “where” condition, it would undermine the optimizer’s elemental selectivity. 

As a practice, implement method-driven functions, like upper, lower, substr, etc. in SQL, to aid in better query optimization.

5. Condense Multiple Steps Into Single Transactions

Every business has bespoke ETL pipeline configurations and requirements. The length and complexity of the pipeline ebbs and flows with that. However, the higher the customization requirements, the higher the time your data would need within the pipeline to respond to functions. Businesses need to try to compensate by scaling horizontally or vertically without resolving the issue at the core.

One of the best steps to improve ETL performance is to condense the steps required to execute and complete each transaction. Use ‘Begin-End’ statements to commit only after the execution of each transformational logic.

6. Data Caching

Improve ETL performance using data caching

Caching is a mechanism that speeds up any computational process by making it easier for processes to access frequently sampled data. You should note that caching uses a substantial amount of your hardware resources. Hence, caching should always be done on a priority basis so that only essential data is cached.

Data-rich ETL processes rely on file feeders for ingesting large datasets. Some processes rely on temporary staging tables to act as a ‘buffer’ to hold data. As expected, each of these temporary tables are dropped after use.

7. Workload Management for ETL Runtime Optimization

The key to ETL runtime optimization is workload management. Arrange for dedicated ‘queues’ that handle a limited batch of ETL processes. 

Limit the use of commit statements to ensure there are no ETL processes that have to wait in a queue. Claiming memory also helps you run multiple ETL processes in parallel. At this juncture, implementing parallel processing tricks can be of immense help.

Another means of tackling bottlenecks is by reducing table read/write steps that increase the number of ETL steps. Instead, database analysts can use flat files to load data from their sources. The lesser the number of tables to load, the faster will be your ETL runtime.

8. Always Address Bottlenecks

Bottlenecks in the network can further strain the ETL pipeline. A common workaround is to install your ETL toolchain on the server that houses the data. This ensures data staging and transformations do not have a longer path separating the data from the source to its destination.

Use a separate stream for mandatory updating, reading and deleting (URD) operations. Reducing unnecessary URD operations or multiple URD operations on the same network stream is an ideal option. These days, parallel processing of ETL data through network streams is relatively easy to execute by using ETL PaaS.

9. Table Maintenance

Train your database teams to conduct statistical collation from each table after every ETL job. These statistical datasets are essential for optimizing the next ETL steps and defining future instances of the same steps.

Another trick is to reduce data entry into your tables. Ensure only the most relevant datasets fill up the table rows. Your ETL pipeline will considerably improve as the processes consult smaller rows of data when inspecting any given table. Reading multiple smaller input tables is less time-consuming than going through one large input table for your ETL processes.

Sort tables and remove deleted blocks. As you delete unnecessary data, you eventually create unsorted regions within your tables. These impede the performance of database queries. Some ETL frameworks, such as Amazon’s Redshift, rely on ETL workload management using vacuum functions. Update database statistics after performing a vacuum.

10. Set Up a Systemic ETL Strategy

Setting up a systemic ETL strategy geared towards addressing performance pain-points can eventually help standardize the process in the following manner:

  • Stage the sequence of your ETL jobs in order and organize your files into homogenous datasets for faster data ingestion
  • Cleanse using JSON-based manifest files. This step will help you get rid of inconsistency and data redundancy/duplication issues.
  • Create periodically segregated data sets for loading into tables. Use single-transaction commit statements to reduce queue straining.
  • Create daily dataset dumps within data warehouses and data lakes to ensure resources are available for newer data.

Benefits of ETL Performance Optimization

There are several critical benefits to improve ETL performance:. 

Here are some benefits in a nutshell: 

  • Better ETL workflow orchestration
  • Easier to write complex queries and introduce innovative service features
  • Lesser data load times
  • Accelerated analytical process, faster implementation, and early results
  • Fewer business disruptions due to ETL pipeline bottlenecks and failures

Improving ETL Performance With Kloudio

These best practices cannot assuredly remove unforeseen pain points arising out of scenario-specific challenges. However, they can serve any ETL pipeline ops regardless of its customization. 

To facilitate better ETL orchestration and performance, you can integrate data transformation protocols within your data assets. This allows you to race ahead of your competitors before trends hit the market. 

Kloudio, as an organization, can help you with your ETL needs with their simple to use products and services. To know more about their products and services, you can create a free account and get associated with Kloudio 2.0. 

Sign up for Kloudio 2.0 for free

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