Data Pipelines

- Overview

Data is the oil of our time - the new electricity. It is collected, moved, refined.

Today, it seems every business is looking for ways to integrate data from multiple sources to gain business insights that will lead to a competitive advantage.

A data pipeline is a method of obtaining raw data from various sources and then porting it to a data store (such as a data lake or data warehouse) for analysis. Before the data flows into the data repository, it usually undergoes some data processing.

The data pipeline encompasses how data travels from point A to point B; from collection to refining; from storage to analysis. It covers the entire data moving process, from where the data is collected, such as on an edge device, where and how it is moved, such as through data streams or batch-processing, and where the data is moved to, such as a data lake or application.

Data pipelines should seamlessly transport data to its destination and allow business processes to function smoothly. If the pipeline is blocked, quarterly reports may be missed, key performance indicators (KPIs) cannot be understood, user behavior cannot be processed, advertising revenue may be lost, and more. Good pipelines can be the lifeblood of an organization.

Before you attempt to build or deploy a data pipeline, you must understand your business goals, specify data sources and targets, and have the right tools.

- Components of Data Pipelines

A data pipeline is a process that moves data from one location to another, while simultaneously optimizing and transforming the data. Data pipelines are sometimes called data connectors. 

A data pipeline is essentially the steps involved in aggregating, organizing, and moving data. Modern data pipelines automate many of the manual steps involved in transforming and optimizing continuous data loads. Typically, this involves loading raw data into a staging table for temporary storage and then changing it before final insertion into the target reporting table.

A data pipeline consists of three components: 

• Source: The source is the source of the data. Common sources include relational database management systems like MySQL, CRMs like Salesforce and HubSpot, ERPs like SAP and Oracle, social media management tools, and even IoT device sensors.
• Data Transformation Steps: Generally speaking, data is extracted from the source, manipulated and changed according to business needs, and then stored at the destination. Common processing steps include transformation, enhancement, filtering, grouping, and aggregation.
• Destination: The destination is where the data arrives at the end of processing, typically a data lake or data warehouse for analysis.

- Data Pipelines Are Important

Data pipelines are critical for modern data-driven organizations. Your organization may need to process large amounts of data. To analyze all your data, you need a single view of the entire data set. When this data resides in multiple systems and services, it needs to be combined in a way that is meaningful for in-depth analysis. 

The data flow itself can be unreliable. There are many points along the way from one system to another where corruption or bottlenecks can occur. As the breadth and scope of the role data plays continues to expand, the scale and impact of the problem will only grow. 

This is why data pipelines are critical. They eliminate most manual steps in the process and enable a smooth, automated flow of data from one stage to another. They're critical for instant analysis, helping you make data-driven decisions faster because they: 

• Enable data flow: Data pipelines enable the smooth flow of data across different systems and apps, from various sources in different formats.
• Ensure data accuracy: Data pipelines ensure that data is processed accurately, which is important for making reliable decisions.
• Eliminate manual steps: Data pipelines automate the process of moving data, eliminating most manual steps.
• Centralize data: Data pipelines centralize data, which is important for providing a unified view of data for analytics and insights.
• Transform data: Data pipelines transform data into actionable insights that can demonstrate business value.

Data pipelines are essential for avoiding the cumbersome, error-prone, and unscalable process of managing and analyzing data without them. 

Some examples of when a delayed data pipeline can be costly include:

• Recommendation engines: Delayed data pipelines can lead to outdated recommendations, which can result in missed sales and a poor user experience.
• Fraud detection systems: Delayed or down data pipelines can mean the difference between catching fraudulent activity and financial loss.

- Benefits of Data Pipelines

Data pipelines allow you to integrate data from different sources and transform it for analysis. They eliminate data silos and make your data analysis more reliable and accurate. 

Here are some of the key benefits of data pipelines.

• Improve data quality: Data pipelines clean and refine raw data, making it more useful to end users. They standardize the format of fields such as dates and phone numbers, and check for input errors. They also eliminate redundancy and ensure consistent data quality across the organization.
• Efficient data processing: Data engineers must perform many repetitive tasks when converting and loading data. Data pipelines enable them to automate data transformation tasks and focus on finding the best business insights. Data pipelines also help data engineers more quickly process raw data that loses value over time.
• Comprehensive data integration: Data pipelines abstract data transformation capabilities to integrate data sets from disparate sources. It can cross-check values ​​from multiple sources for the same material and fix inconsistencies. For example, let’s say the same customer purchases from your e-commerce platform and your digital service. However, they misspelled their names on the digital service. The pipeline can fix this inconsistency before sending the data for analysis.

[Machine Learning Pipeline - Medium]

- Data Pipelines in AI, ML, DL, and Neural Networks

A machine learning (ML) pipeline is a way to automate the workflow of producing machine learning models. It coordinates the data flow input and output of the machine learning model. Pipelines include:

Deep learning (DL) is a subset of machine learning, essentially a three- or multi-layer neural network. These neural networks attempt to simulate the behavior of the human brain, allowing it to "learn" from large amounts of data.

- Data Pipeline Process

To understand how data pipelines work, consider any pipe that receives data from a source and delivers it to a destination. What happens to the data along the way depends on the business use case and the destination itself. A data pipeline can be a simple process of extracting and loading data, or it can be designed to process data in a more advanced way, such as a training dataset for machine learning.

• Source: Data sources may include relational databases and data from SaaS applications. Most pipelines obtain raw data from multiple sources through push mechanisms, API calls, replication engines that periodically pull data, or webhooks. Additionally, data can be synced instantly or at scheduled intervals.

• Destination: The destination can be a data store, such as an on-premises or cloud-based data warehouse, data lake, or data mart, or it can be a BI or analytics application.

• Transformation: Transformation refers to the operation of changing data and may include data standardization, sorting, deduplication, verification and validation. The ultimate goal is to make it possible to analyze data.

• Processing: There are two data ingestion models: batch (source data is collected periodically and sent to target systems) and stream processing (data is acquired, manipulated, and loaded immediately after creation)

• Workflow: Workflow involves the sequencing and dependency management of processes. Workflow dependencies can be technical or business-oriented.
• Monitoring: Data pipelines must have monitoring components to ensure data integrity. Examples of potential failure scenarios include network congestion or a source or destination going offline. The pipeline must include a mechanism to alert administrators about such situations.

- Workflow Dependencies

Workflow involves the sequencing and dependency management of processes. Workflow is a general term for a carefully planned, repeatable pattern of activity. It may be described as a set of operations, the work of an individual or group, or the work of an organization of employees.

A workflow dependency is when one job must wait for another job to start or finish before it can start. This means waiting.

Workflows are made up of the same three basic components: triggers, jobs or activities, and results.

A data pipeline is a series of processes that move data from a source database to a target database. Data pipelines are particularly useful when working with large amounts of data or when working with data that needs to be constantly updated.

• ​Technical Dependency: An example of technical dependency is that after material is obtained from the source, it is held in a central queue where it is further verified and then finally dumped to the destination.
• Business Dependency: An example of a business dependency might be that information must be cross-validated from one source against another to maintain accuracy before being merged.