Data Pipelines Explained: Types, Uses, & Best Practices

Understanding Data Pipelines

Businesses set up data pipelines to collect, centralize, and transform data from diverse sources to perform a complete and accurate data analysis.

This article will cover data pipelines, their types, use cases, best practices, and a brief review of some of the best data pipeline tools for businesses.

What Is a Data Pipeline?

• A data pipeline collects data from multiple sources, processes it if required, and transfers it to a destination repository.
• It involves a series of steps, starting with identifying relevant data sources, aggregating the dispersed data, performing necessary data processing measures such as filtering, transformation, and validation, and finally, loading it into data warehouses.
• Data pipeline got its name because it acts as a "pipeline" for the flow of data from source to destination.
• A data pipeline automates the process of centralizing scattered and fragmented data.
• It improves data quality by converting raw data into an organized format from which actionable insights can be derived.

Why Are Data Pipelines Needed?

The nature of data is prone to error when added to different applications and platforms. Data pipelines help a data analytics team perform the following tasks:

Automates data consolidation Despite the availability of SaaS apps with open APIs, data remains isolated in disparate databases. Consolidate your data sources into a central destination.

Improves data quality Resolve defects in your data transformation process. Optimize data quality by processing, validating, and standardizing data, eliminating inaccuracies such as duplication and formatting issues.

Facilitates better analysis of big data Need help converting large amounts of data into a structured format? A data pipeline enables more rigorous big data analysis, even from unstructured data sources.

Achieves real-time data processing Make business decisions faster without waiting for a nightly data sync. Continuous real-time data streams and seamlessly migrate data into destination repositories instantly.

Who Manages Data Pipelines?

Data pipelines require a deeper understanding of data management. It's primarily why technical managers are the ones who interact with the pipeline themselves.

Typical job titles:

• Data engineers and architects
• Data analysts
• Data scientists
• CIO/CTOs 

The roles and responsibilities of these professionals in adjusting data pipelines vary for every organization.

Types of Data Pipeline Architectures

Data pipelines are built on three different architectures based on the nature of data:

Streaming Data

The continuous ingestion and processing of streaming data require tools to process real-time data from sources such as IoT devices, social media, and financial transactions.

This entails setting up an architecture that caters to real-time analytics and providing on-premises data science solutions. ELT pipelines can be used to optimize new data processing and minimize latency.

Batch Processing

For historical data, batch processing pipeline architecture is best for updating at set intervals.

This architecture is ideal for large workloads that require storing data analysis. Advanced data engineering tools such as SQL extract, transform, load, and store data accurately and efficiently. Basically, if you don't need to query a live data set, then batch data processing is a good option.

Hybrid Processing

Hybrid processing architecture processes real-time streaming data and stored data simultaneously. This approach is more complex since it requires segmenting stream processing and batch-processing jobs within the same pipeline.

A hybrid data ingestion engine optimally helps provide clear benefits to large organizations dealing with extensive amounts of real-time and historical data. The use of tools such as BigQuery and the extraction of new data are crucial components of hybrid processing.

Popular Data Pipeline Use Cases

Let's look at a few of the most common use cases of data pipelines in different organizations.

Consolidating Data from Multiple Sources

Data often comes from several SaaS app sources, complicating data analysis. Data pipelines are used to gather all the necessary data in one place for analysis. This process defines the data flow and connectors for pushing it into a cloud data warehouse.

Automating Data Transformation

Different data sources often produce data in different formats. Converting that data entails extract, transform, load operations to prep the data for loading. Data pipelines define the automated and scheduled steps for synchronizing data.

Improving Business Intelligence

Raw data is unable to provide insights into business operations. Data pipelines convert raw and unstructured data into a centralized, structured format. This transformed data is analyzed to extract reliable information researchers can use to optimize their processes and make data-driven decisions.

Stages of a Data Pipeline

A data pipeline consists of steps or stages as the data flows from different sources to the target repository.

Step 1: Data Ingestion

This is the first step, where the data pipeline extracts data from various source systems, such as CRM, ERP software, SaaS apps, APIs, web apps, and IoT devices.

Step 2: Data Storage

The extracted data is stored in a temporary data lake or data warehouse so that all the required data is gathered in a single location for further processing. The main goal is to centralize raw data from separate sources before it can be processed.

Step 3: Data Transformation

The collected data is now processed according to the target system's specifications, removing duplications and fixing errors for better data quality, organizing different data points into a relational database, etc. There is no one-size-fits-all approach to altering the data. This stage can be tailored based on the data's type and format.

Step 4: Data Integration

The last step is to combine the data for a complete analysis. Through data integration, the pipeline establishes connections between various data points. Data integrations help furnish an entire dataset to deliver insights for business intelligence (BI) and analytics.

Helpful Data Pipeline Tools

Setting up a data pipeline doesn't have to be complicated. Data pipelines can be fully automated through advanced ETL tools.

Here are some of the best ETL solutions for your next data project.

1. Portable

Portable lets you create data pipelines without any code with the help of 350+ ETL connectors. It offers specialized collaborative solutions for different areas, such as e-commerce, marketing, and analytics. With hundreds of ready-made connectors, you can set up automated ETL pipelines in minutes, even for big data sets.

Pricing: Portable has a free plan, a premium plan for $200/month, and a custom plan tailored to your business requirements.

2. Apache Kafka

Apache Kafka is an open-source platform that offers a complete solution for creating data pipelines and taking care of ingesting, aggregating, transforming, processing, and distributing data. It is also a top-tier tool for setting up flexible and scalable real-time data pipeline architecture. It even has a powerful stream-processing library called Kafka Streams.

Pricing: As an open-source solution, Apache Kafka is free to download.

3 Apache Spark

Apache Spark is an open-source framework and a versatile solution for batch processing and stream processing pipelines. It is highly efficient at processing large volumes of data and is even used by big names like Apple, Microsoft, and Facebook. It can be combined together with Kafka for an even more robust and powerful pipeline for big data sets.

Pricing: Apache Spark is a free, open-source solution.

4. AWS Glue

AWS Glue is a serverless platform that lets you create ETL pipelines through visual and programming tools. It has various features designed especially for the setup of big data pipelines, such as:

• AWS Glue crawlers with custom classifiers for data extraction and schema discovery
• AWS Glue ETL jobs for data processing and transformation
• AWS Glue Data Quality for monitoring the quality of data
• Amazon S3 data lakes for storage

Pricing: The total cost for AWS Glue varies based on usage and specifications, but on average, the pricing starts at about $21 daily.

5. Apache Airflow

Airflow is an open-source platform from Apache. It is built on the Python framework and allows users to manage specific workflows about data pipelines. It can monitor data pipelines and handle various workflows through a convenient web interface. Airflow isn't capable of stream processing continuous real-time data but is efficient in batch processing historical and stored data.

Pricing: Airflow is also open-source and free to use.

6. DataHub

DataHub is a SaaS app where businesses and organizations can access open data and publish their data to share with others. It also lets you set up your own data portal, where you can manage all your data in one place with a visual drag-and-drop interface and gain valuable insights with the help of built-in data visualization tools. It is a resourceful application for data collection and management, essential aspects of a data pipeline.

Pricing: DataHub has free and premium plans. Pricing isn't disclosed; you must talk to sales to find out the cost.

7. Joblib

Joblib is a package that adds specific parallel computing functionalities to Python-based data pipelines. It is best suited for data pipelines designed for creating and testing machine learning models where data needs to be processed repetitively. It uses cached results, allows multiple processes to run simultaneously, and enables quick data dumping and loading, making the pipeline faster and more lightweight.

Pricing: Joblib is a special open-source package or library for Python and is free to use.

8. Snowflake

Snowflake is a cloud-based data warehouse service that utilizes Azure, Google Cloud, and AWS infrastructures. It lets businesses set up customizable and scalable data warehouses on the cloud, which cannot store, process, and share big data. It supports structured and unstructured data, eliminating the need for data transformation. It offers specialized solutions for different industries, such as healthcare, manufacturing, and retail, to name a few.

Pricing: Snowflake's pricing depends on your requirements and usage of resources. You only pay for what you use with Snowflake as a cloud-based service.

9. Looker

Looker is a data analytics and business intelligence (BI) solution by Google Cloud. It can connect to various databases, create data-driven workflows, and perform data processing. It can also be used for visualization on dashboards to deliver highly informative and actionable insights. It can also define relations between various datasets to structure and organize fragmented data.

Pricing: Looker has custom pricing for its paid plans. You have to request a quote to know the exact cost.

Best Practices for Reliable Data Pipelines

Data pipelines are essential in data analysis, data management, business intelligence, and decision-making. They also help with scientific applications such as training and developing machine learning models and AI systems.

But setting up pipelines in a modern data stack doesn't have to be complicated. 

Follow these best practices for optimal reliability and scalability. 

Catalog Raw Data Sources for Efficient Extraction

• Organize your raw data sources with metadata to quickly locate relevant data points and improve extraction efficiency.
• Data pipelines will only extract specific data needed, resulting in faster processing time and higher efficiency.

Establish Seamless Data Flow with Data Connectors

• Utilize data connectors such as APIs and ETL connectors to establish cross-connectivity between systems, databases, and SaaS applications.
• This results in a fully integrated data set that non-technical users can analyze efficiently and speeds up decision-making.

Leverage Cloud Data Warehouses To Scale

• Maintaining on-premises data warehouses requires time, effort, and budget, making cloud data warehouses an attractive option.
• Cloud data warehouses offer greater flexibility and cost efficiency by only paying for what you use. Plus, data scientists can access them worldwide.
• Snowflake, BigQuery, and Redshift are popular cloud-based data warehouses with limitless headroom for growth.

Inspect Pipelines Regularly for Maximum Data Quality

• Automated pipelines, such as manual data source modifications, may not always be free from data errors.
• Regularly testing pipelines can help uncover errors that may affect data analysis workloads downstream.

Set Up Error Detection for Streaming Data Pipelines

• Real-time streaming ETL pipeline architectures need live error detection systems that provide alerts and notifications.
• Use monitoring tools, integrate logging frameworks with notification channels, and leverage cloud-based monitoring services like Amazon CloudWatch for faster error resolution.

Automate Data Processing for More Efficient Pipelines

• Manual batch processing can be inefficient when dealing with large amounts of data or when processing must be performed based on certain conditions and requirements.
• Automation syncs smaller batches and performs automatic data transformation at less critical business hours.

Improve Business Outcomes With Reliable Data Pipelines

Moving data around without a purpose isn't practical.

But syncing support tickets with subscriptions — in real time — helps a company solve customer pains long before they result in a missed quarter. That's valuable.

The cloud-based ETL platform Portable lets you connect 350+ data sources to grow revenue, cut waste, and speed up decision-making. Use it for free with an unlimited data volume. And when you're ready for more, automate your data flows for just $200/mo.