• A GUI-based specification and development environment that can be used to define, build, and test data pipelines, with version control capabilities for maintaining a library of pipelines.

• A data pipeline monitoring application that helps users monitor, manage, and troubleshoot pipelines.

• Data pipeline development, maintenance, and management processes treat pipelines as specialized software assets.

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.

• Data scientists and other members of data science teams

• Business intelligence (BI) analysts and developers

• Business analysts.

• Senior management and other business executives.

• Marketing and sales teams.

• Operational workers.

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.

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.

• Batch: Batch processing in a data pipeline is most useful when an organization wants to move large volumes of data at a regularly scheduled interval and immediate delivery to end users or business applications isn't required. For example, a batch architecture might be useful for integrating marketing data into a data lake for analysis by data scientists.

• Real-time or streaming data processing: Real-time or streaming data processing is useful when data is being collected from a streaming source, such as financial markets or internet of things (IoT) devices. A data pipeline built for real-time processing captures data from the source systems and quickly transforms it as needed before sending the data to downstream users or applications.

• Lambda architecture: This type of architecture combines batch and real-time processing in a single data pipeline. While more complicated to design and implement, it can be particularly useful in big data environments that include different kinds of analytics applications.

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.

• Extract, transform and load (ETL) is the batch process of copying data from one or more source systems into a target system. ETL software can be used to integrate multiple data sets and perform rudimentary data transformations, such as filtering, aggregations, sampling and calculations of averages.

• Other types of data integration tools are also often used to extract, consolidate and transform data. That includes extract, load and transform (ELT) tools, which reverse the second and third steps of the ETL process, and change data capture software that supports real-time integration. All of these integration tools can be used together with related data management, data governance and data quality tools.

• Data streaming platforms support real-time data ingestion and processing operations, often involving large amounts of data.

• SQL is a domain-specific programming language that's often used in data pipelines. It's primarily designed for managing data stored in relational databases or stream processing applications involving relational data.

• Scripting languages are also used to automate the execution of tasks in data pipelines.

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.

• huge volumes of data,

• coming from multiple (100+) sources

• in a great variety of formats (structured and unstructured and semi-structured), and

• at high speed.

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.