Data pipelines are data processing elements that move one or more data sets from their source to their destination.
This article explains its meaning, architecture, and the many tools one can use.
What is a Data Pipeline?
A data pipeline is a process of moving one data set from its source to its destination through multiple connected data processing elements.
▸A data pipeline is a system that allows for the ingesting of raw data from different sources, and then transfers it to an analytics-ready file storage like a warehouse or data lake.
▸Data processing is required before it can be stored in a database.
▸Data transformations such as screening, masking and groupings are essential to ensure that data is standardized and integrated properly.
This is particularly important if the data is to be used in a relational database. This database has a predetermined structure. Therefore, updating the current information with new data requires alignment (i.e. linking data types and rows).
What are the Core Workings of a Data Pipeline?
▸Data pipelines, as their name suggests, are conduits for data science projects and business intelligence dashboards.
▸Although data can be obtained from many sources (e.g. APIs, structured queries language (SQL), NoSQL database, and files), it is not always immediately usable.
▸Data analysts and database administrators often have data preparation responsibilities. They organize data to meet the requirements of enterprise applications.
▸The type of data processing required by a workflow is often determined by both business needs and exploratory research.
▸Once the content has been properly filtered, merged, and summarized it can be stored and made accessible.
▸A well-organized data pipeline is the foundation of many data efforts such as exploratory data analysis and visualization.
👉These operations are part of the data pipeline:
Ingesting Data: Data can be gathered from various data sources, such as structured and unstructured data. These basic data sources are commonly referred to by the terms publishers, producers, and senders in streaming data.
Although organizations might choose to only extract data when they are ready to analyze it further, it is best to store raw data in a cloud-based data warehouse before.
If necessary, any data can be updated by the company if there have been changes to data processing.
Data transformation: Many tasks are performed during this stage to convert the data into the format required by the data repository.
To ensure data is always cleaned up and converted, these tasks employ automation and regulation, such as business reports.
For instance, a data flow may arrive in a nested JSON file, and the data processing phase will seek to extract the important fields for analysis from this JSON.
Storing data: The converted data is then stored in the data warehouse. This data may be accessible by other entities. These modified streaming data are commonly referred to by consumers, subscribers, or receivers.
A data pipeline is required for any functional or organizational activity that requires frequent automated aggregation of, cleaning, transformation, and dissemination of data to subsequent consumers. Data users are:
- Alerting and monitoring systems
- Dashboards for management and reporting
- Tools for Business Intelligence (BI)
- Data Science Teams
Many data pipelines can also transmit data between advanced data refinement units and conversion units. Neural network models and ML algorithms may be used to create more complex data enrichments and data conversions.
This includes classification, regression analysis, and grouping as well as the development of sophisticated indices or propensity ratings.
Are Data Pipelines Identical to ETL?
ETL refers to a specific type of data pipeline. ETL stands for Extract, Transform, and Load.
It's the process of transferring information from one source (e.g., an app) to another, often to a data warehouse.
👉"Extract" is the process of retrieving data from a source.
👉"Transform" is the transformation of the data to load it into the destination.
👉"Load” refers to the entering of the information within the destination.
In some cases, terminology such as data and ETL pipelines may be interchangeable in a discussion. ETL pipelines should be considered subset data pipelines.
Three distinguishing characteristics differentiate the two types of pipelines.
- ETL pipelines follow a specific order. They extract, convert and store data in a database. This schedule is not mandatory for all data pipelines.
- ETL pipelines are increasingly being used by cloud-native solutions. This pipeline allows data to be ingested first and then transformations can be performed after the material has been stored in the cloud database.
- ETL pipelines are often used to indicate batch processing. However, the extent of data pipes is greater, as previously stated. They may also include stream processing.
Finally, although it seems unlikely, data pipelines are generally not required to perform data transforms like-like ETL pipes. Data pipelines that don't use modifications to improve data analysis are rare.
Why do Enterprises Need Data Pipelines?
Data pipelines are designed to automate routine data collection, transformation, transfer, integration, and expansion.
An established data pipeline can speed up and automate the collection of, cleaning, converting, and enriching information for subsequent systems and applications.
▸The company's daily operations will be more complicated as the information volume, variety, and frequency continues to rise.
▸Data pipelines that can grow linearly in the cloud and hybrid environments are essential. As big data continues to grow, data management is becoming more difficult.
▸Data pipelines can be used for many purposes but they are primarily commercially useful.
Exploratory Data Analysis: Data scientists use exploratory data analytics (EDA), to analyze and describe data sets, and often incorporate data visualization techniques.
Data analysts can use it to determine the best way to alter data sources to obtain desired results. This makes it easier for them to identify patterns, detect anomalies, test hypotheses and verify their assumptions.
Data visualizations: are graphical representations that show data in graphs, charts, or infographics. These visualizations make it easier to communicate complex data links and draw data-driven conclusions.
Machine Learning: It falls under Artificial Intelligence (AI). It is an area that focuses on the use of data and algorithms to mimic how humans learn, while continually improving its accuracy.
Data mining projects teach algorithms how to create classifications and predictions using statistical techniques. This reveals critical insights.
Imagine that you own an e-commerce website and plan to use Tableau BI tech to analyze purchase history. If one uses a data warehouse, one must create a data pipeline that transfers all transaction information to the source repository.
You can then use Tableau to create a data pipeline from the database system. This will allow you to create cubed or aggregated parts to make the information easier to understand.
If you use a data lake, you might also have a pipeline that runs across your transaction data source and your data lake. Tableau and other BI tools can then immediately search the cloud storage for the relevant material.
Considerations When Building a Data-Pipeline
Data pipelines in the real world are similar to plumbing systems. Both serve basic needs (to transfer information or water). Both can fail and need maintenance.
Data engineering teams are responsible for building and maintaining data pipelines in many organizations.
To reduce the human oversight required, this should be as automated as possible. But, automation is not the only option.
Speed and performance: Data pipelines can lead to slow query responses depending on how data is replicated and moved around an organization.
Pipelines can become slow when there are many concurrent requests or large data amounts, especially if they rely on multiple data replicas or use a data virtualization approach.
Complexity due to scale: A company could have hundreds of data pipes. It may be difficult to identify which pipeline is in use at the moment, how old it is, and what dashboards or insights are dependent on it.
A data landscape that includes multiple data pipelines can make it more difficult to comply with regulations and cloud migration.
Rising costs: Additional routes may result in rising expenses. Data engineers and developers might need to create new pipelines due to technological changes, migrations to the cloud, or increased analysis and analysis requirements.
Over time, managing multiple data pipelines can increase operating costs.
Data Pipeline Architecture
There are many possible ways to design data pipelines.
▸The first is batch-based data pipes. A point-of-sale application may generate a large number of data points that must be sent to a database and an analytics database.
▸The second type of design is streaming data pipelines. A streaming data pipeline would process the data as it is generated from the point of sale system.
▸The stream processing engine could provide outputs to data storage, marketing apps, customer relationship management system, and other apps as well as point-of-sale systems.
▸You can also use Lambda architecture which combines streaming and batch pipelines. Because it allows developers to simultaneously address real-time streaming use cases and historical batch analysis, Lambda architecture can be used in big data contexts.
▸This design promotes data storage as a raw format. You can operate new data pipelines continuously to fix code issues in existing pipelines or create new destinations that permit new types of queries.
▸The event-driven data pipeline architecture is the final step. When a pre-defined event occurs on the source system, event-driven processing can be advantageous.
▸This includes anti-lock systems and airbags. The data pipeline collects and transmits data from the event to the next procedure.
👉These components are common to all these architectures:
1. Origin or Source
The point of data input in the data pipeline is called Origin.
An organization's monitoring and analysis data ecosystem could include sources of data (transaction processor software, connected devices and APIs, and any other accessible dataset), and storage systems (storage servers, data lakes, or data lakehouse).
2. The Destination of Choice
The destination is the final location to which data is sent. Depending on the purpose of the data, one might supply data for data visualization or analytical tools.
Or one could move to storage like data lakes or data warehouses. We'll soon return to storage types.
3. Dataflow or Movement
This refers to the transport of data from its source to its final destination. It also includes any conversions or data storage encountered along the way.
4. Data Storage
The storage process is the way data is kept at different points in the pipeline.
Many parameters can be used to determine the options for data storage, including the quantity and frequency of data requests, the purpose of the data, and the number of queries.
5. Data Processing
The process of processing involves the acquisition and storage of information from different sources, as well as changing it and sending it to a recipient.
Data processing is linked to dataflows but it also involves the implementation of this movement. Data processing can be done in a variety of ways.
One could extract data from the source system, transfer it to another database (database replication), and stream it. There are many other options.
6. Workflows and Tasks
The workflow of a data pipeline specifies the order and interdependence of operations (tasks). This situation might benefit from understanding many concepts such as tasks downstream and upstream.
A job is a unit of labor that does a specific task, such as data processing. The point at which material is delivered to a pipeline from upstream, while downstream refers to the destination.
Data travels through the data pipeline, just like water. Upstream tasks must be completed before downstream operations can begin.
7. Continuous Monitoring
Monitoring is used to assess the performance of the data pipeline and its stages. It is intended to determine if it is efficient despite increasing data loads if it remains consistent and accurate as it moves through processing stages, and if any data is lost.
8. Tolerance For Faults
Modern data pipelines use a distributed architecture. This provides instant failover and alerts consumers about component failures, application malfunctions, and other service issues.
If a node is lost, another cluster node takes its place quickly and without any effort.
👉When designing your data pipeline architecture, consider the following:
- Data processing that is continuous and extensible
- Flexibility and adaptability of the cloud storage systemsystem
- Access to democratic data and self-service management
- High availability and rapid recovery after disasters
Data pipeline frameworks are systems that organize, collect, and route data to gain insight. Many data points may not be relevant in raw data.
Data pipeline architecture organizes data events to make it easier for analysis, reporting, and usage.
A mix of software technologies and protocols automate data management, visualization, conversion, and transmission, depending on business goals.
Data Pipeline Tools
The task of a developer might be to create, evaluate, and manage the code required for the data pipeline. These frameworks and toolkits may be used by developers:
Workflow management tools: These tools allow you to create a data pipeline. Software structure that is open-source automatically resolves dependencies. This allows developers to manage and analyze data pipelines.
Event and messaging platforms: Existing apps may be able to provide faster, better-quality data by using Apache Kafka or similar tools. They use their protocols to collect data from business applications and facilitate communication between systems.
Scheduling tools: Process scheduling is an essential component of any data pipeline. Many tools allow users to create detailed timetables for data input, conversion, and transfer.
👉The following are some of the most useful and popular data pipeline tools:
Keboola allows the automation and construction of all data pipelines. Businesses can spend more time on revenue-generating activities with automated ETL, ELT, and reverse ETL pipelines.
This allows them to save time and money in data engineering. Keboola is entirely self-serviceable and offers no-code tools.
2. Apache Spark
Apache Spark is a powerful tool for creating a real-time pipeline. It is a data processing engine that was designed for large-scale operations.
A data pipeline processes large data sets and distributes them to multiple sources.
Integrate.io, a flexible ETL platform, facilitates data integration, processing, and analysis preparations for enterprises.
Data pipeline tools provide organizations with immediate access to multiple sources of data as well as a large data collection for analysis.
NetApp is a visual data-pipeline solution that requires very little to no programming to activate your data.
It can interact with almost any source and destination using no-code connectors. NetApp also provides a GUI to data modeling, and transform your data.
This tool allows cloud-native data pipe administration. Dragster allows for easy interaction with the most popular technologies like Spark, Pandas, and Spark.
Dragster can handle common problems such as localized development, testing, dynamic workflows, and job execution.
A data pipeline automates data mapping, transformation, and migration between systems. They can be scaled to any data type and adapt easily.
ReportLinker's research predicts that by 2028, the global market for data pipeline tools will reach $19 billion.
You can find the right tools for you by understanding the role and meaning of data pipelines.