Hassan is a data scientist and has obtained his Master of Science in Data Science from Heriot-Watt University.
Introduction to data pipelines in Big Data
A data pipeline is a process that helps you to store and process data. This is an essential element in any organization that deals with big data. This might seem unimportant, but think about it for a second: When you have large amounts of data, it can be challenging to manage.
This is because you need more resources and time to analyze and make sense of your data. This article will help you understand what data pipelines are and how they work.
What Is a Data Pipeline?
A data pipeline is a system that manages the flow of data from one system to another to process it. It can also move data between different databases or instances of the same database.
A data pipeline is an essential component of any big data solution, allowing you to load data into your system and then process it in various ways after it has been loaded. For example, if you use Hadoop for your big data solution, you will need a data pipeline to transfer the data between different system components.
Use of Data Pipelines in Data Science and Analytics
What Is the Need for Data Pipelines?
Data pipelines are a data-driven process that enable the movement of data from source to destination. It is a way to optimize the data flow between various applications and databases. The need for data pipeline arises when you have more than one application or database that needs access to the same data set but cannot be connected directly due to technical reasons.
A good use case would be an online store where you need order information in real-time for processing but also want an archive copy of each order for accounting purposes. The data pipeline can connect your application and database so that data is seamlessly transferred between them.
It also allows you to filter and process the data before sending it along its journey. Essentially, a data pipeline is a set of tools for automating data movement between various applications and databases.
The Three Types of Data Pipelines
Types of Data Pipelines
The data pipeline is a system that connects data sources with data sinks. It can be used to process and store data. There are three main types of data pipelines: real-time, batch, and cloud.
Real-time data pipelines are used to build and run applications that need to respond quickly to events, such as fraud detection or customer service monitoring. Real-time pipelines are designed for low latency and low cost. They can process and analyze large amounts of data very quickly. However, they do not allow users to store or manipulate data in any way after it has been processed or analyzed by the pipeline itself.
2. Batch Data
Batch data pipelines are typically used in business intelligence systems. They allow users to store large amounts of data before analyzing it at one time instead of processing each piece individually as they come in over time like real-time pipelines do (which would be too slow). This allows them to analyze more significant amounts of information at once without having so many resources available as they would if they were using real-time processing methods instead (which would require more computing power).
Cloud data pipelines are the most recent pipeline to be developed. They allow users to store their data in a database accessed through an application programming interface (API) instead of having to keep it on their servers. This will enable them to use cloud computing resources without needing any of their equipment. The most significant benefit of cloud data pipelines is that they’re much easier to set up than traditional pipelines.
The Three Components of Data Pipeline Architecture
Data Pipeline Architecture
Data pipelines are designed to be modular, which means you can add or remove individual components as needed. This allows you to scale as your business grows and change your processes over time to adapt to new requirements.
The components of a data pipeline may include the following:
Data collection systems: These systems gather raw data from various sources, including social media posts, sensors, and other streaming data sources.
Storage systems: Data storage systems provide long-term raw and processed data storage. Some storage solutions allow you to query the stored information using SQL languages to run queries against the database without waiting until processing is complete.
Data preparation tools: These tools cleanse and organize your raw data into formats that make it easier to analyze later in the process (e.g., by removing duplicate entries or converting values from one type into another).
The three main data types in data pipelines
Types of Data
Data pipelines allow companies to pull together their disparate data and use it. As you might expect, many data types can be used in a pipeline. Here are some examples:
Structured Data: This is your typical spreadsheet or database. It’s generally easier to use with data pipelines because it’s already organized for you. As a result, you don’t need much work to get it into a valuable form for analysis.
Unstructured Data: Unstructured data refers to images, audio files, or video files. While these may not be as easy to work with as structured data, they provide some interesting insights into their right—especially when paired with other types of data sets.
Semi-structured Data: Semi-structured data is somewhere between structured and unstructured. It’s like a spreadsheet; it has rows and columns, but it’s not as tightly organized as a database.
Data Pipeline and ETL Pipeline: What’s the difference?
Data Pipeline vs. ETL Pipeline
Data pipelines are used to design and implement a framework for moving data from one place to another. ETL (extraction, transformation, and loading) pipelines are a subset of data pipelines that focus on extracting data from different sources, transforming it into a format suitable for analysis, and loading it into a database for querying.
Organizations use ETL pipelines to extract data from various sources (like databases or websites) and load it into an analysis database where analysts can query it. They’re also used to perform transformations on the data so that it’s easier to analyze.
The goal of ETL is to ensure that all of your systems are communicating seamlessly so that your analysts can save time cleaning up messy data before using it.
Data Pipeline Uses in Data Science and Analytics
Data pipelines can be used in a variety of ways. Here are a couple of examples:
Exploratory data analysis: Data pipelines can be used to explore large datasets, which is often the first step in the scientific process. First, data points are analyzed and organized into groups. Then, those groups are further analyzed and compared to others until you have enough information to conclude.
Machine learning: Data pipelines can also be used for machine learning, which requires inputting data into models that learn from it over time. This is how computers learn to recognize images or language, for example. Data scientists use these models to predict future events based on past events (for example, predicting weather patterns based on current weather conditions).
The data pipeline is a concept that makes it possible to process large amounts of information in real time. It is an essential component of the overall big data ecosystem and can be used in many ways.
Data pipelines are not just a tool used by companies; they have also become a feature of the open-source community. Many different types of data pipelines are available today, including Apache Spark, Apache Flink, and Apache Apex. I hope this article has helped you better understand data pipelines and why they are essential.
This content is accurate and true to the best of the author’s knowledge and is not meant to substitute for formal and individualized advice from a qualified professional.
© 2022 Hassan