There’s been increased attention lately on the quality of data processes, governance and automation to help companies improve decision-making. Data is everywhere, in all shapes and formats. In the absence of action, data is ineffective. It is essential to make use of it in order to generate profits for your company. This usually involves the process of extracting, transforming, and loading data in one manner or another, ensuring it’s fit to serve its purpose, providing information and informing the business’s decisions.
Today, we concentrate on one of the key processes that allows it data pipelines. This blog explains what is an data pipeline, and the challenges data engineers face to ensure it is strong. Simple data pipelines for exploratory purposes can be build to explore data. However, they become extremely complicated when you have to integrate multiple streams of data into digestible sets for analysts or systems that are able to effectively consume on a regular basis. This is the kind of thing that pipelines for production data must accomplish.
The process of creating production data pipelines isn’t that different from the creation of a high-end railway system to transport data. It’s an exercise in precision engineering that requires meticulous planning, sturdy construction, and ongoing maintenance. The aim is to ensure that information from a variety of sources is delivered quickly, accurately and ready to be used.
What is the Use Case?
The process begins with a lengthy consultation with all the stakeholders. Data engineers need to understand their business goals and the source of the data as well as its destination, and the data insights that analytics teams hope to discover. This step is crucial because it determines the selection of the technology, the layout for the data pipeline as well as the methods for data integration along with quality control and adherence to the standards for data governance.
It is vital to verify the knowledge of the usage in the case. I’ve witnessed many projects get a bit off track in the event that delivery teams create something totally distinct from what the company was hoping for.
Designing the Data Pipeline
Once the goals have been set and verified, After that, data engineers design the structure of the data flow. The design defines how the information will be extracted, how it will be positioned and the various transformations they will undergo. The process can be repeated and is accompanied by frequent review to ensure that the data is in line with the goals of the business and technological practicality. This stage can take a long time before becoming a reality due to the continuous exchange between business representatives and engineering teams.
Selection of Technologies
Choosing the appropriate tools and technologies is essential. Data engineers should consider the amount of data they have to handle, how often updates are made and the complexity of the transformations. I blend solutions can be used to handle streaming data in real time and for more complicated integration scenarios while batch processing could depend on MDS as well as Hadoop ecosystems. Data warehousing technologies such as Amazon Redshift, Google BigQuery, or Snowflake are the most popular options.
The most important thing is to develop an architectural framework and then select technologies that meet the needs most effectively and do not alter a pipeline’s design to something that is not designed to accomplish the task at the time. The most common mistake that businesses make is to limit themselves to a certain stack and then waste an enormous amount of money and time trying to find solutions. Always do a complete cost analysis for the life of every technology you buy or are already using.
Data Extraction (E)
The first step of the pipeline is to extract information from sources. It can be anything from simple databases to complicated distributed systems. Data engineers have to navigate a variety of protocols and formats, using ETL tools to load all the information in a stage zone (physical or stored in memory).
In general, data extraction involves information from databases, APIs and systems, ESBs, IoT sensors blocks, flat file formats etc. It may be unstructured or structured. Real-time or batch. If the data is digital format that is, it could theoretically be taken in.
No matter what extraction method used the pipeline should be able connect to the source and ingestion the data with accuracy and at a predetermined frequency.
Transformation and Enrichment (T)
The data is not always in a good shape and ready to be analysed. It requires cleaning, normalizing or enriching in order to be valuable for business purposes – verifying and changing it.
A popular method is by ingesting the raw information into a cloud warehouse or lake and then processing it. This is the Snowflake ecosystem is an excellent example of this, in which the data is taken in via an ELT process before being ingested into the warehouse, and then processed there using various tools.
Additionally, data can alternatively be “staged” virtually, in memory, to be used in situations that use real-time data or require processing. Real-time data needs CDC for instance, and so the process is performed during the time of transfer.
This transformation process is where much of the magic happens. Engineers write scripts, often in SQL or Python, and turn the raw data into something that can answer business questions. There is a plethora of tools that specialise in data transformations, DBT being a popular choice for doing in-warehouse transforms.
The Transformation layer handles the business logic, data cleansing as well as quality assurance and management, in addition to others. As previously mentioned the layer could occur in a warehouse or in memory. If it is a warehouse the pipeline is divided into T and EL parts and the T layer occurs in a separate manner in the pipeline.
In the ETL instance, transformations are performed in the process of transferring data. The manipulation of data takes place “in-flight” before the data is delivered to its final destination, which could be the warehouse mentioned above and/or directly at the consumer layer (e.g. systems, apps, dashboards, etc).
Persisting the Data (L)
Once it is transformed, the data has to be stored in the system store or warehouse to be able to be used again and archived. The data must be organized into schemas and tables which reflect the business context and allow efficient querying. The loading process can be performed in batches or streamed in real-time, dependent upon the type of data as well as the usage scenarios.
Quality Assurance and Testing
Through all stages of the ETL method, quality control is essential. Data engineers use automated tests to confirm that every step of the pipeline is functioning exactly as they would expect. This could include checking whether the data is correct and that transformations maintain integrity of data, and loading processes don’t introduce errors.
Deployment and Monitoring
But, engineers’ work isn’t completed. They have to monitor the pipelines and ensure that the data flows seamlessly and effectively and that any lapses or bottlenecks are addressed quickly (and do not bring the house down during the process! ).
There are many requirements placed on the data pipelines used for production to ensure that they’re robust and solid data streams (see this table). The functionality is either programmed by the programmers or through separate tools for different levels. The more crucial the data pipeline’s function is, the more comprehensive the checks will be.