Data has become a new source of valuable information, helping businesses to gain actionable insights and make data-driven decisions. So if your company constantly gathers vast amounts of data and still hasn’t built a system to analyze and transform data into valuable resources, this article will describe, what is ETL process, why you should consider ETL as a lever that unlocks the value of your data, as well as NIX’s best practices for building a sufficient ETL process in the data warehouse.
ETL is a process that extracts the data from any sources and works with various formats, then transforms the data and then loads it into the data warehouse.
The ETL process consists of three steps:
It’s essential to know how to manage data from various sources in the case and use different data analysis tools to leverage business intelligence. At this stage, structured and unstructured data is extracted from a wide range of sources like legacy systems, CRM’s, data warehouses, etc., and imported into the staging area.
Of course, you can manually do it, but it’s a complicated and expensive process and there is a chance that it can be misconfigured and data could be lost. A self-written solution is expensive, time-consuming, and difficult to maintain. That’s why ETL tools can come in handy since they can help automate the extraction process and create a more streamlined and secure workflow.
Data transformation occurs within a staging area and consists of several sub-processes of turning raw data into the required format. To make it happen data is sorted, verified, and cleansed from duplicates. When data is converted into the compatible format – it moves to the next stage.
It is generally deemed that transformation is the most crucial stage of the ETL process since it ensures data integrity, accessibility, and compatibility at the end-point.
The last stage implies loading data into the target data warehouse in two ways—full load or incremental load. Full load produces data sets that grow exponentially and can quickly become challenging to maintain. Incremental load is a less comprehensive but more manageable approach. It compares incoming data with what’s already on hand and only produces additional records if new and unique information is found. This architecture enables smaller, less costly data warehouses to maintain and manage business intelligence.
Better data = better decisions = better performance
An ETL process helps transfer data into a data warehouse and convert it to different formats and types to adhere to one consistent system. It offers deep historical context for business, enabling critical, data-driven decision-making and providing managers with quick access to the data.
The data warehouse integrates multiple data sources to reduce processing in the production system. Moreover, it helps to reduce overall response time for analysis and reporting while making it easier to prepare reports.
A well-structured and documented ETL system can help businesses obtain a holistic view of the data that drive better strategic decisions. ETL provides clean and filtered data to be used by different end-user tools and enables data aggregations helping businesses to generate higher revenue and optimize costs.
There are many different ETL tools and self-service pipelines that eliminate routine tasks such as manual ETL coding and data cleansing. Moreover, they enable better data analysis and drive strategic decision-making.
Here are the most popular tools that can come in handy for your specific tasks and business objectives:
Apache Spark is «a unified analytics engine for large-scale data processing» that runs on Hadoop, Apache Mesos, Kubernetes, or standalone or in the cloud. Spark empowers data scientists and developers to process data rapidly and provides a real-time analysis at high speed.
PySpark is a combination of Python and Apache Spark that can rapidly process massive amounts of data and provide real-time computation in-memory processing. Its superficial programming layer offers powerful caching and disk persistence capabilities.
Airflow platform is open-source ETL software that helps design and track workflows. It was designed to create a dynamic, elegant, and scalable solution and has a modern user interface with many visualization elements. You can view all current pipelines, track progress, and correct errors.
Airflow works with cloud services, including Google Cloud Platform, Azure, and AWS, and enables dynamic pipeline generation through Python coding.
This is a distributed platform that enables users to publish and subscribe to streams of records and process them as they occur. Kafka allows creating real-time streaming data pipelines and applications and is run as a cluster on one or more servers that can span more than one datacenter.
Kafka Streams is a client-side library for building applications and microservices where input and output data are stored in clusters. It combines the ease of writing and deploying standard Java and Scala client apps with Kafka’s server cluster technology benefits.
Snowflake supports both transformations during (ETL) or after loading (ELT) and enables fast speed and rapid performance. By separating computation and storage, you can scale the warehouse to keep high performance during peak loads and then scale down for fast data requests.
Talend is an open-source ETL solution that provides a graphical design environment, ETL and ELT support, and enables the export and execution of standalone jobs in runtime environments. Talend is reconcilable with databases both on-premises and in the cloud and offers hundreds of off-the-shelf integrations.
While the open-source version of Talend is quite sufficient, big enterprises prefer Talend’s paid data management platform. The paid version incorporates additional tools for design, productivity, maintenance, monitoring, and data management.
Fivetran is an automated ETL integration and analytics platform that allows you to combine data from multiple sources into a single service.
In ETL, data flow from the data source to staging. ETL can help with data privacy and compliance, cleansing sensitive data before loading into the data destination, while ELT is more straightforward and for companies with minor data needs. Data modification occurs right after extraction within a staging area, followed by data load, and then into the data warehouse.
ELT transfers data to the transformation stage without the need for data staging. Firstly, the data is extracted, then loaded into the target system, and only later some of the data is transformed for future analysis.
The choice between these two depends not only on the volume and structure of the data but also on many other factors such as business objectives, the frequency of data updates, the complexity of the necessary data transformations, and other aspects specific to a particular extension.
Based on our case, we want to share our best practices on how to build a scalable ETL system that will bring value to the business.
The client is in the healthcare insurance industry and was interested in analyzing cost patterns and trends for further optimization and receiving actionable insights. The NIX team needed to build a big data processing ecosystem to handle the orchestration of the ETL processes focused on getting initial data from different data sources.
Understanding and analyzing the data source and its structure is critical to building a well-organized ETL pipeline. This includes knowing the data types, schema, and other details of your data that mainly depend on the data source. More challenging might be to handle large sets of structured and unstructured data from disparate sources. No less important is to keep in mind that data volume can increase along with sources. That’s why you need to ensure scalability at the stage of system planning.
The better you understand the data source structure, volume, and frequency of updating/extension at this stage, the better you can choose the most optimal architecture and approach for data processing (ETL, ELT, or hybrid solution).
In our example, the data comes from three different sources—data warehouse, SFTP, and Amazon S3 storage—and has a different structure and formats.
The foremost value of an ETL process is data. Data input from different sources, varying quality, and consistency need to be transformed into a consistent system for future analysis and discovery of actionable insights.
In our case, data of the same type come from different sources and have a diverse structure. To build a consistent system, we needed to conduct the data through several essential steps—cleaning, validating, and augmenting the data to ensure accuracy, completeness, and uniformity. The team used Spark applications and different ML/AI models that prepared data for identifying patterns, trends and discovering solutions.
Logging is a crucial part of any data processing that allows tracking both ETL pipelines and individual modules. Considering that we are talking about big data, it is necessary to track events at the individual record level that require a scalable system.
In our case, the team used the following approaches:
Considering ETL pipeline is a complex process consisting of many different steps, it’s important to save intermediate data, or “checkpoints.” This allows you to restart not the whole pipeline but only a part of it after the problem is solved and the necessary checkpoint is selected. Furthermore, it helps debug pitfalls in business logic and logs, allowing analyzing how data has changed and, if necessary, quickly and efficiently identify the problem.
In our case, the team used the pipeline modularity—most modules save their outputs to pass data to the next step in the chain. In addition, Spark applications provide a mode that allows storing internal spark application checkpoints for lower-level analysis.
Since the primary purpose of ETL is to process data and guarantee its quality, it’s essential to perform audits even if the ETL process was completed accurately and without flaws.
In our case, the team carried out an audit through the data lineage—a group of specific events in ELK, which allows you to track which ETL stages the data passed, applied parameters, and statistical data on the input-output.
The audit consisted of two steps:
Since ETL processes can be pretty spacious and complex, it is good practice to break down their components into building blocks. For general tasks, it is good practice to create customizable elements (i.e., via parameters) that can be reused in different pipelines. An additional advantage is that such components are easier to test and debug.
In our case, the team implemented modularity on two levels:
Securing the data is a crucial task. It’s essential to ensure security when accessed by the end-user/application and during the ETL process.
Within our case, security for the ETL has several levels, which include:
It’s good practice to set up an alert system for the ETL pipelines to notify the defined authorities regarding any errors that require immediate intervention.
For our case, we used two alert types:
When working with big data, it’s essential to monitor performance to ensure it hasn’t dropped and has improvement potential. The test data is not always sufficient for this purpose, as with distributed computing such as Spark, you need to understand the data structure to identify gaps and bottlenecks.
In our case, we implemented:
It’s pretty challenging to choose the right approach and tools among all possible options, but the key for that is expertise, a deep understanding of the data, and the business needs of a particular case.
Dealing with data requires comprehensive expertise and analysis of many aspects to picking up the right tool stack: ETL or ELT or a combination of both. Our data engineers can help choose the right approach and set up and maintain the data infrastructures that support corporate systems and apps, uncovering actionable insights and solutions regarding your business needs.
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