Data Pipeline

Orchestrating Success with Data Pipelines - Unraveling Components, Purpose, Key Technologies, and Cutting-Edge Tools

What is a data pipeline?

A data pipeline is a set of tools and activities for moving data from one system with its method of data storage and processing to another system in which it can be stored and managed differently. Moreover, pipelines allow for automatically getting information from many disparate sources, then transforming and consolidating it into one high-performing data storage.

As more and more companies seek to integrate data and analytics into their business operations, the role and importance of data pipelines are also growing. Organizations can have thousands of data pipelines that perform data movements from source systems to target systems and applications. With so many pipelines, it's important to simplify them as much as possible to reduce management complexity.

To effectively support data pipelines, organizations require the following components:

What is the purpose of a data pipeline?

The data pipeline is a key element in the overall data management process. Its purpose is to automate and scale repetitive data flows and associated data collection, transformation, and integration tasks. A properly constructed data pipeline can accelerate the processing that's required as data is gathered, cleansed, filtered, enriched, and moved to downstream systems and applications.

Well-designed pipelines also enable organizations to take advantage of big data assets that often include large amounts of structured, unstructured, and semi-structured data. In many cases, some of that is real-time data generated and updated on an ongoing basis. As the volume, variety, and velocity of data continue to grow in big data systems, the need for data pipelines that can linearly scale—whether in on-premises, cloud, or hybrid cloud environments—is becoming increasingly critical to analytics initiatives and business operations.

Data pipeline components

To understand how the data pipeline works in general, let's see what a pipeline usually consists of. Senior research analyst of Eckerson Group David Wells considers eight types of data pipeline components. Let's discuss them in brief

Destination.The final point to which data is transferred is called a destination. Destination depends on the use case: Data can be sourced to power data visualization and analytical tools or moved to storage like a data lake or a data warehouse. We'll get back to the types of storage a bit later.

Dataflow.That's the movement of data from origin to destination, including the changes it undergoes along the way as well as the data stores it goes through.

Storage.Storage refers to systems where data is preserved at different stages as it moves through the pipeline. Data storage choices depend on various factors, for example, the volume of data, frequency and volume of queries to a storage system, uses of data, etc. (think of the online bookstore example).

Processing.Processing includes activities and steps for ingesting data from sources, storing it, transforming it, and delivering it to a destination. While data processing is related to the data flow, it focuses on how to implement this movement. For instance, one can ingest data by extracting it from source systems, copying it from one database to another (database replication), or streaming data. We mention just three options, but there are more of them.

Workflow.The workflow defines a sequence of processes (tasks) and their dependence on each other in a data pipeline. Knowing several concepts—jobs, upstream, and downstream—would help you here. A job is a unit of work that performs a specified task—what is being done to data in this case. Upstream means a source from which data enters a pipeline, while downstream means a destination it goes to. Data, like water, flows down the data pipeline. Also, upstream jobs are the ones that must be successfully executed before the next ones—downstream—can begin.

MonitoringThe goal of monitoring is to check how the data pipeline and its stages are working: Whether it maintains efficiency with growing data loads, whether data remains accurate and consistent as it goes through processing stages, or whether no information is lost along the way.

In-house or in the cloud?

Many businesses create their own data pipelines. Spotify, for example, built a pipeline to analyze data and learn about customer preferences. Its pipeline enables Spotify to detect which region has the most users and to map consumer profiles with music recommendations.

However, there are obstacles to building an in-house pipeline. Different data sources offer different APIs and use various technologies. Developers must develop new code for each data source, and this code may need to be rewritten if a vendor changes its API or if the organization adopts an alternative data warehouse destination.

Another challenge that data engineers must address is speed and scalability. Low latency might be critical for supplying data that drives decisions in time-sensitive analysis or business intelligence applications. Furthermore, the solution should be elastic as data volume and velocity increase. The substantial expenses involved, as well as the ongoing maintenance efforts necessary, might be significant deterrents to creating a data pipeline in-house.

Data pipeline vs ETL

There's frequently confusion about pipelines and ETL. So the first order of business is to clear that up. Simply put, ETL is just a type of data pipeline, that includes three major steps

Extractgetting/ingesting data from original, disparate source systems.

Transformmoving data into temporary storage known as a staging area. Transforming data to ensure it meets agreed formats for further uses, such as analysis.

Loadloading reformatted data to the final storage destination.

This is a common, but not the only, approach to moving data. For example, not every pipeline has a transformation stage. You simply don't need it if the source and target systems support the same data format. We'll discuss ETL and other types of data pipelines later on.

Who needs a data pipeline?

A data pipeline is needed for any analytics application or business process that requires regular aggregation, cleansing, transformation, and distribution of data to downstream data consumers. Typical data pipeline users include the following:

To make it easier for business users to access relevant data, pipelines can also be used to feed it into BI dashboards and reports, as well as operational monitoring and alerting systems.

How does a data pipeline work?

The data pipeline development process starts by defining what, where, and how data is generated or collected. That includes capturing source system characteristics, such as data formats, data structures, data schemas, and data definitions—information that's needed to plan and build a pipeline. Once it's in place, the data pipeline typically involves the following steps:

  1. Data ingestion: Raw data from one or more systems is ingested into the data pipeline. Depending on the data set, data ingestion can be done in batch or real-time mode.

  2. Data integration: If multiple data sets are being pulled into the pipeline for use in analytics or operational applications, they need to be combined through data integration processes.

  3. Data cleansing: For most applications, data quality management measures are applied to the raw data in the pipeline to ensure that it's clean, accurate, and consistent.

  4. Data filtering: Data sets are commonly filtered to remove data that isn't needed for the particular applications the pipeline was built to support.

  5. Data transformation: The data is modified as needed for the planned applications. Examples of data transformation methods include aggregation, generalization, reduction, and smoothing.

  6. Data enrichment: In some cases, data sets are augmented and enriched as part of the pipeline through the addition of more data elements required for applications.

  7. Data validation: The finalized data is checked to confirm that it is valid and fully meets the application requirements.

  8. Data loading: For BI and analytics applications, the data is loaded into a data store so it can be accessed by users. Typically, that's a data warehouse, a data lake, or a data lakehouse, which combines elements of the other two platforms.

Many data pipelines also apply machine learning and neural network algorithms to create more advanced data transformations and enrichments. This includes segmentation, regression analysis, clustering, and the creation of advanced indices and propensity scores.

In addition, logic and algorithms can be built into a data pipeline to add intelligence.

As machine learning and, especially, automated machine learning (AutoML) processes become more prevalent, data pipelines will likely become increasingly intelligent. With these processes, intelligent data pipelines could continuously learn and adapt based on the characteristics of source systems, required data transformations and enrichments, and evolving business and application requirements.

What are the different types of data pipeline architectures?

These are the primary operating modes for a data pipeline architecture:

Event-driven processing can also be useful in a data pipeline when a predetermined event occurs on the source system that triggers an urgent action, such as a fraud detection alert at a credit card company. When the predetermined event occurs, the data pipeline extracts the required data and transfers it to designated users or another system.

Key technologies used in data pipelines

Data pipelines commonly require the following technologies:

Open source tools are becoming more prevalent in data pipelines. They're most useful when an organization needs a low-cost alternative to a commercial product. Open source software can also be beneficial when an organization has the specialized expertise to develop or extend the tool for its processing purposes.

Big Data pipeline for Big Data analytics

Big Data pipelines perform the same tasks as their smaller counterparts. What differentiates them is the ability to support Big Data analytics which means handling

ELT loading infinite amounts of raw data and streaming analytics, extracting insights on the fly, seem to be perfect for a Big Data pipeline. Yet, thanks to modern tools, batch processing and ETL can also cope with massive amounts of information. Typically, to analyze Big Data, organizations run both batch and real-time pipelines, leveraging a combination of ETL and ELT along with several stores for different formats.

Data pipeline tools

These are tools and infrastructure behind data flow, storage, processing, workflow, and monitoring. The choice of options depend on many factors, such as organization size and industry, data volumes, use cases for data, budget, security requirements, etc. Some groups of instruments for data pipelines are as follows.

ETL tools include data preparation and data integration tools such as IBM DataStage Informatica Power Center, Oracle Data Integrator, Talend Open Studio, and many more.

Data warehouses (DWs) are central repositories to store data transformed (processed) for a particular purpose. Today, all major DWs — such as Amazon Redshift, Azure Synapse, Google BigQuery, Snowflake, and Teradata — support both ETL and ELT processes and allow for stream data loading.

Data lakes store raw data in native formats until it's needed for analytics. Companies typically use data lakes to build ELT-based Big Data pipelines for machine learning projects. All large providers of cloud services — AWS, Microsoft Azure, Google Cloud, IBM — offer data lakes for massive data volumes.

Batch workflow schedulers (Luigi or Azkaban) enable users to programmatically specify workflows as tasks with dependencies between them, as well as automate and monitor these workflows.

Real-time data streaming tools process information continuously generated by sources like machinery sensors, IoT and IoMT (Internet of Medical Things) devices, transaction systems, etc. Popular instruments in this category are Apache Kafka, Apache Storm, Google Data Flow, Amazon Kinesis, Azure Stream Analytics, IBM Streaming Analytics, and SQLstream.

Big Data tools comprise all the above-mentioned data streaming solutions and other technologies supporting end-to-end Big Data flow. The Hadoop ecosystem is the number-one source of instruments to work with BD. You have

Amazon, Google, IBM, and Microsoft Azure also facilitate building Big Data pipelines on their cloud facilities.

Implementation options for data pipelines

You can implement your data pipeline using cloud services by providers or build it on-premises.

On-premises data pipeline. To have an on-premises data pipeline, you buy and deploy hardware and software for your private data center. You also have to maintain the data center yourself, take care of data backup and recovery, do a health check of your data pipeline, and increase storage and computing capabilities. This approach is time- and resource-intensive but will give you full control over your data, which is a plus.

Cloud data pipeline. Cloud data infrastructure means you don't have physical hardware. Instead, you access a provider's storage space and computing power as a service over the internet and pay for the resources used. This brings us to a discussion of the pros of a cloud-based data pipeline.

Disadvantages of cloud include the danger of a vendor lock: It will be costly to switch providers if one of the many pipeline solutions you use (i.e., a data lake) doesn't meet your needs or if you find a cheaper option. Also, you must pay a vendor to configure settings for cloud services unless you have a data engineer on your team.

If you struggle to evaluate which option is right for you in both the short and long run, consider talking to data engineering consultants.

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