It is easy to find data. Making sense of it is far tougher. The data has to be cleaned, optimized, and processed using a data pipeline.
Data is everywhere –
- captured by tracking cookies
- gleaned from website analytics
- scraped from social media
In its raw, unstructured proforma data is of little use.
That is where a data pipeline comes in. It ingests data from many sources and transports it to a repository.
Data pipeline helps you to:
- Assimilate data from various databases
- Clean and refine raw data
- Automate repetitive tasks and reduce errors
- Transport it to a data warehouse
Without further ado, let’s examine this more closely.
What are Data Pipelines?
A data pipeline is a set of tools and activities that move data. Just as a fiber optic cable delivers a streaming video, a data pipeline delivers data.
There are several stages to building a data pipeline. They are as follows:
- Data Source – IoT, mobile applications, web applications, CRM software, etc.
- Data Integration – ETL or ELT.
- Data Store – Data warehouse or lake.
- Data Insights – Data analytics, Predictive analytics, ML-assisted analytics.
- Data Delivery – Dashboards, reports, email.
What is the Importance of a Data Pipeline?
Businesses that rely on data-driven decision-making benefit from data pipelines. They collect, process, and analyze data from multiple sources, providing valuable insights to solve problems and make informed decisions.
Some advantages are:
- Enhancing the quality and consistency of data by eliminating errors, missing values, duplicates, and inconsistencies.
- Improving data accessibility and usability by consolidating data from multiple sources into a common destination and schema.
- Enabling real-time data analysis and action by processing and delivering data in near real-time or streaming fashion.
- Supporting data scalability by using cloud computing and open-source technologies to handle large amounts and different types of data.
- Facilitating data governance and security by enforcing data policies and regulations to ensure compliance and protection.
Types of Data Pipelines
There are many different ways of designing and implementing data pipelines based on
- Architecture
- Frequency
- Complexity
Some common types of pipeline design are:
Batch data pipelines
These pipelines execute manually or at regular intervals (such as daily, weekly, or monthly). They process large batches of data at once. Batch data models are suitable for scenarios where the data is not time-sensitive and can be analyzed periodically.
Real-time data pipelines
These designs transfer and process data in real-time and enable continuous analysis and decision-making. They are suitable for scenarios where data is time-sensitive.
Cloud-based data pipelines
These are pipelines where the tools and technologies are hosted online. Cloud application helps with the scalability, flexibility, and cost-effectiveness of cloud computing. Cloud-based designs are suitable when data sources and destinations are distributed across different locations.
Open-source data pipelines
Open-source models use open-source technologies as the primary tools for data ingestion, transformation, storage, and analysis. They are suitable for scenarios where customization and innovation are important.
On-premise data pipelines
These are pipelines where the tools and technologies are installed and maintained on local servers and networks within an organization’s premises. On-premise models are suitable for scenarios where data security is critical, and legacy systems need to be integrated.
These are some of the types that can be used for different purposes and scenarios. However, there is no one-size-fits-all solution for design and implementation. Depending on the goals, a combination of different types of pipelines can be used to achieve optimal results.
Read more – Different Types of Data Pipelines: Which One Better Suits Your Business?Â
Best Practices for Creating Efficient Pipelines
Data Pipelines have become increasingly important for industries to maintain and analyze their multi-source data. Here are some best practices (sequentially) that can help data engineers design, build, and maintain reliable pipelines:
- Define clear business requirements and objectives.
- Identify the data sources, destinations, transformations, and use cases that it needs to support.
- Align the design and architecture with the business goals and expectations.
- Choose the right tools and technologies for the data pipeline.
- Select the tools and technologies that match the volume, variety, and velocity of the data pipeline.
- Consider scalability, performance, security, cost, and compatibility factors when choosing the tools and technologies.
- Implement tools and frameworks that enable automation and orchestration of the data pipeline’s processes, such as data ingestion, transformation, storage, and analysis.
- Use tools and methods to monitor and test performance, quality, and reliability.
- Document and maintain the pipeline to facilitate collaboration, communication, reuse, and troubleshooting.
Experience Business Growth with FLIP: The DataOps Tool
We have explained what data pipelines are and their impact. But between theory and practice, there is a huge gap.
The process of implementing and maintaining them is a complex and time-consuming task. Fortunately, there are tools available that can help streamline the process.
FLIP, a data automation platform from Kanerika, enables you to easily connect, transform, and move data between various sources.
Here are some reasons why you should consider using FLIP:
- Has a simple and intuitive interface
- Analytics tools that help extract insights
- Requires zero coding skills
- Allows you to create customized dashboards
To sum it up, you can access customized reports and summaries when you want and where you want.
Data operations implemented using FLIP will enable your business to stay competitive and make data-informed decisions.
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FAQs
What is meant by a data pipeline?
A data pipeline is like an automated assembly line for your data. It takes raw, messy data from various sources, cleans, transforms, and organizes it, then delivers the refined data to its destination (like a database or analysis tool). Think of it as a series of connected steps ensuring your data is ready for use. Essentially, it's the backbone of efficient data management.
Is data pipeline an ETL?
No, a data pipeline is broader than just ETL (Extract, Transform, Load). While ETL is a *core component* of many pipelines, pipelines encompass the entire data flow – from ingestion and processing to storage and potentially onward actions like analysis or model training. Think of ETL as a specific tool *within* the larger data pipeline machinery.
What are the main 3 stages in a data pipeline?
Data pipelines typically involve three core stages: Ingestion, where raw data is gathered and cleaned; Transformation, where data is processed, enriched, and structured; and Loading, where the prepared data is sent to its final destination (like a database or data warehouse). Think of it as collecting, refining, and delivering the goods. These stages ensure data is usable for analysis and other purposes.
Is AWS a data pipeline?
No, AWS isn't a data pipeline itself, but rather a *platform* offering numerous services *to build* data pipelines. Think of it like a toolbox: you use AWS services (e.g., S3, Kinesis, Glue) as components to construct your specific data pipeline architecture. The pipeline itself is what you design and implement using those tools.
Is a data pipeline an API?
No, a data pipeline and an API are distinct but often related. A data pipeline is a system for moving and transforming data, while an API is an interface for accessing and interacting with data or services. A pipeline *might* use APIs to get or send data, but the pipeline itself isn't an API; it's the entire process. Think of a pipeline as the factory and an API as one of its delivery points.
Is Kafka a data pipeline?
No, Kafka isn't a data pipeline itself, but rather a crucial *component* within many data pipelines. Think of it as a high-throughput, distributed message broker – the highway system for your data. A complete pipeline needs additional tools for ingestion, processing, and storage, with Kafka facilitating the reliable and scalable movement of data between them.
What does a data pipeline include?
A data pipeline is like an assembly line for your data. It gathers raw data from various sources, cleans and transforms it (think scrubbing and polishing), and then delivers it to its final destination – like a database or analytics tool. Essentially, it's the entire process of getting data ready for use. Key components include ingestion, transformation, and loading (ETL).
What is a data pipeline and how do you build it?
A data pipeline is like an automated assembly line for your data, transforming raw information into usable insights. Building one involves defining the data source, choosing the right tools (e.g., ETL software), designing the transformation steps, and setting up monitoring for smooth operation. Think of it as orchestrating the entire journey of your data, from origin to analysis.
What is data pipeline in DevOps?
In DevOps, a data pipeline automates the flow of data from its source to its final destination (like a data warehouse or machine learning model). Think of it as an assembly line for data, transforming and moving it through various stages – cleaning, processing, and enriching – reliably and efficiently. It ensures consistent data availability for analysis and application use, crucial for continuous delivery and integration. Essentially, it's the backbone for data-driven decision-making in a DevOps environment.
What is data pipeline in ML?
In machine learning, a data pipeline is like an automated assembly line for your data. It takes raw, messy data and transforms it into a clean, structured format ready for model training. This involves steps like cleaning, transforming, and validating data, often using specialized tools and software. Think of it as the crucial pre-processing step that ensures your models get high-quality fuel.
