Data Pipeline: Tools, Architecture, and Everything Else Explained
By
Anesu Kafesu
A data pipeline is essentially the channel through which data flows.
As you would imagine, the data flow between two places, the source, and the destination. And the channel it follows from source to destination is the data pipeline.
While flowing, data will be validated, transformed, and aggregated to be used at the destination. Data pipelines are incredibly useful in making business intelligence platforms and facilitating data-driven decision-making.
This article will dive deep to know exactly what data pipelines are.
What Is a Data Pipeline?
As mentioned before, a data pipeline is a channel through which data flows from a source system to a destination system. The source(s) is where data is generated or first recorded.
For example, this could be an online shop management system or a social media ad campaign management tool. The destination could be a dashboard showing the ad expenditure against sales recorded in the online shop.
Data pipelines can be constructed to collect data from the different systems, transform it as needed and place it in a repository where the dashboard will collect and display it.
Oftentimes, the format in which data is expected at the destination is not the format in which it is generated. For example, the online shop can provide all the shop orders in JSON format. However, the system requires total sales for the month.
The pipeline will therefore have to add up all the orders in a particular month to calculate total sales for the month. The pipeline, therefore, serves as an important middle step that will restructure and reorganize the data as needed.
Benefits of Data Pipelines
- Chief among the benefits of using data pipelines is that they enable you to collect and aggregate data from different systems and display the results in a single-centralized place. This makes information more accessible and decision-making easier.
- Constructed the right way, you will also be able to see real-time information and analytics for different metrics you track in a business.
- Automating data collection and summarisation is cheaper, faster, and less error-prone than manually transferring or entering data into systems.
- Data pipelines are also very scalable. As the amount of data increases, they are much more capable of handling the increased workload than manual methods.
Next, we will discuss the data pipeline architecture.
Data Pipeline Architectures
Broadly, there are two types of data pipeline architectures; one is ETL, while the other is ELT.
#1. ETL (Extract-Transform-Load)
ETL is a method of implementing data pipelines. ETL stands for Extract-Transform-Load. These are the steps followed as data is extracted from the source system. Then it is transformed into an ideal form for the destination use case. Lastly, it is loaded into the system.
An example would be trying to rank an online shop’s most popular products in a month. First, the order data is extracted from the online shop. Next, it is transformed by breaking it down into the individual items in the shop. Then the items are counted to find the most popular products. The resulting list is then loaded into the destination system.
#2. ELT (Extract-Load-Transform)
As you probably guessed, ELT is Extract-Load-Transform. In this method, the data is extracted from the source system. Next, it is loaded onto the destination server. After this, any transformations are applied after the data has been loaded. This means that raw data is kept and transformed as and when needed.
The advantage of this is the data can be combined in new ways over time to get a different perspective. Going back to the previous example, the same order data can be used to see which customers bought the most from the shop. This would not be possible if we had already transformed the data to rank products.
ETL Vs. ELT
| Aspect | ETL | ELT |
| Storage | Data is stored in its processed format on the data warehouse server | Data is stored in its raw format and transformed as and when needed |
| Use Case | It is suited for larger datasets and simple transformations | It is suited for small datasets and complex transformations |
| Data Format | Data is stored in a structured format; therefore, it can only be stored in data warehouses | Data can be structured, unstructured, and semi-structured, therefore can be stored in data warehouses and/or data lakes |
| Maturity | This has been the traditional way of implementing data pipelines but is more mature and well understood | It is the modern way of implementing data pipelines, but it is more complex and less understood by people |
| Compliance | It makes it easier to comply with regulations such as GDPR because the data is transformed before storage and may not be personally identifying | It makes it harder to comply with GDPR because data is kept in its original format. Therefore, it may still be linked to individuals |
| Data Availability | Need to specify which data is required upfront and how it will be transformed | As much data as is available can be loaded first, then transformed later |
| Time of Transformation | Transformation occurs in a staging system before loading | Transformation occurs in the data system itself |
| Time to Load | The time to load is longer because the data is transformed during the loading | The time to load is shorter because no transformations are happening |
| Time Taken During Transformations | Transformations happen upfront, which takes more time initially but once transformed, data analysis is faster | Transformations happen when needed and are recomputed every time. Therefore analysis takes time. |
Summary
Both ELT and ETL have their strengths and weaknesses, and none is necessarily better than the other. ETL allows you to structure your data before loading and makes analysis faster, while ELT gives you the flexibility of unstructured data. Ultimately, choosing which method is better depends on your business needs.
Types of Data Pipelines
Another way of classifying data pipelines is based on whether the pipeline implements batch or real-time processing.
#1. Batch Processing
In batch processing, data is collected regularly and processed in one go. This method is ideal when the data is needed periodically. An example of a data pipeline utilizing batch processing is a payroll system where timesheets are extracted from the clocking-in system.
The hours are then calculated and billed according to one worked. The wages to be paid can then be loaded into a different system. This system would only run once a week or a month. Therefore the data will be collected periodically and processed in one go.
#2. Realtime Processing
The alternative to batch processing is real-time processing. In this system, data is processed as soon as it is generated. An example of a real-time processing data pipeline is a website registering visitors and sending the data to an analytics system immediately.
By looking at the analytics dashboard, one will know the number of website visits in real time. Real-time streams can be implemented using technologies like Apache Kafka. Here is a guide on how to get started with Apache Kafka.
Other tools that can be used include RabbitMQ.
Use Cases
Building an Analytics Dashboard
Data pipelines are incredibly useful for aggregating data from different sources to show a business’s performance overview. They can be integrated with analytic tools on a website, social media, and ads to monitor a business’s marketing efforts.
Building a Database for Machine Learning
They can also be used when building a dataset that will be sued for machine learning and other predictions. This is because data pipelines can handle lots of data being generated and recording it just as fast.
Accounting
Data can be collected from different applications and sent to the accounting system. For example, sales can be collected from Shopify and recorded in Quickbooks.
Challenges
- Building a data pipeline often requires some technical expertise. While some tools make it easier, there is still some knowledge required.
- Data pipeline services can get costly. While the economic benefit may make the cost worthwhile, the price is still an important factor to consider.
- Not all systems are supported. Data pipeline systems support and integrate with some of the most popular systems as either sources or destinations. However, some systems are not supported; therefore, some parts of a business’s tech stack may not be integrated.
- Security is another factor to consider when data moves through third parties. The risk of a data breach is increased when there are more moving parts in the system.
Now, let’s explore the best data pipeline tools.
Data Pipeline Tools
#1. Keboola
Keboola is a data pipeline-building tool. It enables you to build integrations to collect data from different sources, set up workflows to transform it and upload it to the catalogue. The platform is very extensible, with options to use Python, R, Julia, or SQL to perform more advanced analyses.
#2. AWS Data Pipeline
AWS Data Pipeline is an Amazon Web Service that enables you to transfer and move data between Amazon Web Compute and Storage Resources such as EC2 instances and S3 storage. This service is only available within AWS.
#3. Meltano
Meltano is an open-source, command-line tool for building ELT data pipelines. It supports extracting data from different data sources such as Zapier, Google Analytics, Shopify, etc. It is widely used by product teams of some of the biggest and most popular tech companies.
#4. Stitch Data
Like Meltano, Stitch Data is a tool used by big companies. However unlike, Meltano, Stitch is an ETL tool meaning, you extract first, then transform and load the data into the data warehouse.
#5. Hevo Data
Hevo Data is a platform that makes it easy to build a pipeline that moves data from sources to destinations. And integrates with lots of data sources and supports destinations such as MYSQL, Postgres, BigQuery, and many other databases.
Final Words
Data pipelines are a very powerful tool. They help you make your business decisions more data-driven by empowering you to extract and combine data in more meaningful ways to gain insights into this complicated, ambiguous world.
Next, you can check out digital transformation courses & certifications.
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Anesu KafesuAuthorFull stack web developer and technical writer. Currently learning AI.
