Let’s explore modern ETL architecture best practices used in building ETL architectures that work for your business goals.
Consolidation of Data from Different Sources
One of the most standard and common scenarios is when we have various data sources and we need to make data from all of them “friendly” for extraction and further processing. Timely data consolidation is one of the streaming ETL architecture best practices, which is carried out with the help of a specialized tool and is comprehensively implemented in the Visual Flow’s functionality.
Data Quality Check and Possibility of Regular Data Enrichment
This is one of the good ETL architecture best practices, which concerns not only the quality of data but also filtering, structuring, and implementation of data relationships between different assets. Typically, ETL pipelines fetch and load more information than necessary. And a large amount of big data can negatively affect the speed of processes.
This is why it is necessary to optimize extraction, perform transformations, and filter out unnecessary and repetitive data. In addition, an important task of each ETL framework is to provide the possibility of regularly supplementing data, that is, adding new assets to the system.
In Visual Flow, checking the quality of data (including when connecting a new source), their structuring, filtering, and interconnection are out-of-the-box opportunities while you may also regularly replenish data according to certain rules, for example, according to a schedule.
Optimization of Your ETL/ELT Workflows (Performance & Scalability)
This is one of the best practices and principles for ETL architecture which goes beyond the standard ETL solution and is a significant advantage and feature of the Visual Flow approach. In terms of ETL performance and scalability, Visual Flow definitely wins because it has Spark under the hood.
Here we are dealing with an infrastructure that is deployed on the fly when we need to run our application (as Spark and Kubernetes capacities lead to auto-scalable workflows) in contrast to standard ETL methods where the load on the server increases significantly as data grows.
A Large Number of Prebuild Transformations
Prebuild transformations are not only very important in the context of big data ETL architecture best practices, but also not very difficult to refine in terms of implementation. A large number of such transformations, including aggregation functions, can be scaled constantly in Visual Flow.
Metadata is essential to data mapping and it needs to be laid down at the architectural level. The processing, storage, and convenient access of metadata during the development of ETL/ELT (extract, load, transform) architecture processes is quite feasible, but it is voluminous. Without metadata, only the person who designed the process would understand it. The metadata processing practice is currently not available in Visual Flow but is coming soon.
Connection management is an architecturally important point in terms of real-time ETL architecture best practices that greatly affects the usability of the ETL development process. If an ETL framework lacks a connection manager, forcing developers to specify new parameters each time when creating a process, this will significantly affect the convenience of work.
A connection manager is an interface that stores configured custom connection parameters to a direct data storage. It helps developers improve their work performance and avoid mistakes while configuring the parameters each time. These parameters are kept in an encrypted format and can be reused for several ELT processes. This practice is not currently implemented in Visual Flow but is coming soon.
Easy Access to the History of ETL Logs
Runtime monitoring is a must when we talk about ETL architecture best practices in the ETL development process. In particular, metadata and metadata processes are sorted in order to understand how the entire ETL pipeline works. Easy access to the history of ETL logs in the familiar to an SQL developer standard is a very important part of building a comprehensive ETL pipeline. This history not only helps fix bugs but also collects data-enriching insights.
In Visual Flow, reports are sent by email in the form of Spark logs and a summarized file. This is not the fastest way to access history logs, but works on this feature for the paid version are in progress.
Performance and Status Monitoring
It is important to check the status and access the history of ETL logs in case something goes wrong. In Visual Flow, efficient data analytics performance and status monitoring features are implemented by default.
There are two aspects to this best practice of ETL architecture. The first is the technical one, which lies under the hood — when something fails before data processing is initiated. The second is when the processing kicks off and the failure occurs somewhere at the data load stage.
In this case, if the process crashes, nothing needs to be loaded. The option with partial data load is not suitable because you will still need to process the entire amount of data the next time you attempt to load it.
In Visual Flow, auto-restart is implemented to tackle such issues. Three recovery points are created to avoid failures that occur before the start of the job, those that occur after the start of the job, plus a stable performance recovery point that we need to roll back to when we do not need to rerun a process that doesn’t handle data correctly.
Error Alert Service to Change Code in Debug Mode
Error alerts during ETL are critical to help resolve errors in a timely manner. In Visual Flow, this ETL pipeline architecture best practice can be employed using Spark. Mechanically, this process can be described as follows: the job stops in a specific place, which is displayed in a separate window in the UI. Standard ETL tools do not support this and are unlikely to in the near future.
Having a clear display of how versions are controlled during the ETL is a must. The lack of one is a classic issue of all visual ETL tools. Version control is essential to tracking, organizing, and controlling all data changes taking place. Without version control, even a powerful and advanced ETL tool is insufficient. By the way, Visual Flow has GitHub integration, so you can be sure of our product’s relevance.