Data pipelines are essential tools in today’s big data landscape, enabling organizations to extract value from their raw data. By understanding their dynamics, organizations can optimize their data processes to gain better insights and make more informed decisions. In this post, learn how to make the most of data pipelines for your business.
Introduction to Data Pipelines
Today, data pipelines are essential for processing data from a variety of sources, allowing organizations to gain valuable insights and make informed decisions.
So what is a data pipeline? A data pipeline is a series of processing steps that prepare business data for analysis. Organizations have large volumes of data from a variety of sources, such as applications, Internet of Things (IoT) devices, and digital channels. However, raw data has no value until it is moved, sorted, filtered, reformatted, and analyzed to gain business insights. A data pipeline incorporates various technologies to validate, aggregate, and find patterns in data for business decision making. Well-organized data pipelines enable various big data projects, such as data visualization, exploratory data analysis, and machine learning tasks.
It typically consists of three steps:
- Data source: Where the data comes from, such as databases, CRM systems, or IoT sensors.
- Data processing or transformation: Includes operations such as classification, translation, deduplication, validation, and analysis.
- Data destination: Where the transformed data is stored for users to access.
Benefits of data pipelines
Data pipelines allow you to integrate data from different sources and transform it for further analysis, especially in a business use case. They eliminate data silos and make data analysis more reliable and accurate. Here are some of the key benefits of a data pipeline:
- Improved data quality: Data pipelines cleanse and refine raw data, making it more useful to end users. They standardize formats for fields such as dates and phone numbers while checking for input errors. They also eliminate redundancy and ensure consistent data quality across the enterprise.
- Efficient data processing: Data engineers must perform many repetitive tasks when transforming and loading data. Data pipelines allow them to automate data transformation tasks so they can focus on gaining better business insights. Data pipelines also help data engineers quickly process raw data that loses value over time.
- Comprehensive data integration: A data pipeline uses data transformation capabilities to integrate data sets from different sources. It can compare values for the same data from different sources and resolve inconsistencies. For example, imagine the same customer makes a purchase on your e-commerce platform and on your digital service. However, they misspelled their name in the digital service. The pipeline can correct this inconsistency before the data is sent for analysis.
Data pipeline architecture and operation
Just as a water pipe moves water from a reservoir to your faucet, a data pipeline moves data from the point of collection to storage. A data pipeline pulls data from a source, makes changes to it, and then saves it to a specific destination.
Let’s explore the three basic steps that make up the data pipeline architecture:
- Data Ingestion: Data is collected from various sources, including software-as-a-service (SaaS) platforms, Internet of Things (IoT) devices, and mobile devices, and from various data structures, both structured and unstructured. In the case of streaming data, these raw data sources are often referred to as producers, publishers, or senders. While organizations can choose to pull data when they are ready to process it, it is a best practice to first place raw data in a cloud data storage provider. This allows the company to update historical data as it makes adjustments to data processing jobs. During this data ingestion process, various validations and checks can be performed to ensure data consistency and accuracy.
- Data transformation: During this step, a series of jobs are run to process the data into the format required by the target data repository. These jobs incorporate automation and governance for repetitive workflows, such as business reporting, to ensure that the data is cleaned and transformed in a consistent manner. For example, a data stream may arrive in nested JSON format, and the data transformation stage aims to unroll this JSON to extract the key fields for analysis.
- Data storage: The transformed data is then stored in a data repository where it can be made available to various stakeholders. In streaming data, these transformed data are typically referred to as consumers, subscribers, or recipients.
Types of Data Pipelines
There are typically two main types of data pipelines: stream processing pipelines and batch processing pipelines.
- Stream processing pipelines: A stream is a continuous, progressive sequence of small packets of data. It typically represents a series of events that occur over a period of time. For example, a data stream might represent sensor data containing readings from the past hour. A single action, such as a financial transaction, can also be called an event. Stream pipelines process a series of events for real-time analysis. Streaming data requires low latency and high fault tolerance. Your data pipeline should be able to process data even if some data packets are lost or arrive in a different order than expected.
- Batch processing pipelines: Batch data pipelines process and store data in large volumes or batches. They are suitable for high-volume and infrequent tasks, such as monthly accounting. The data pipeline contains a series of sequential commands. Each command is executed on the entire batch of data. The data pipeline uses the output of one command as the input for the next command. When all data transformations are complete, the pipeline loads the entire batch into a cloud data warehouse or other similar data store.
On the other hand, it is worth clarifying the difference between a data pipeline and an ETL pipeline. An extract, transform, and load (ETL) pipeline is a special type of data pipeline. ETL tools extract or copy raw data from multiple sources and store it in a staging area. They transform the data in the staging area and load it into data lakes or data warehouses. Not all data pipelines follow the ETL sequence. Some can extract data from one source and load it into another without transformation. Other data pipelines follow an extract, load, and transform (ELT) sequence, where they extract unstructured data and load it directly into a data lake. They make changes after the information is moved to data warehouses.
Primary Uses of Data Pipelines
As the volume of data and the scale of enterprise projects continue to grow, data management is becoming an increasing priority. While data pipelines serve a variety of functions, the following are for business applications:
- Exploratory data analysis: Exploratory data analysis (EDA) is used by data scientists to analyze and explore data sets and summarize their key characteristics, often using data visualization methods. It helps determine the best way to manipulate data sources to get the answers you need, making it easier for data scientists to discover patterns, spot anomalies, test a hypothesis, or verify assumptions.
- Data visualizations: Data visualizations, such as charts, graphs, infographics, and even animations, can be created to represent data through common graphics. These visual representations of information communicate complex data relationships and data-driven insights in a way that is easy to understand.
- Machine learning: A branch of artificial intelligence (AI) and computer science, machine learning focuses on using data and algorithms to mimic the way humans learn, gradually improving their accuracy. Statistical methods are used to train algorithms to make classifications or predictions, uncovering key insights within data mining projects.
- Data observability: To verify the accuracy and security of the data being used, data observability uses a variety of tools to monitor, track, and alert on both expected events and anomalies.
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