Building an Extract, Transform, Load (ETL) pipeline is a critical first step in getting data into your data warehouse. It is important to understand the different parts of an ETL data pipeline and why they are needed to create one that works for your business.
Building ETL pipelines may seem an overwhelming task, but this guide will walk you through everything from getting started to building real-life business cases using ETL pipelines.
This blog post will discuss the three stages of a business's Extract-Transform-Load data pipeline. It will also provide approaches to building ETL pipelines for day-to-day enterprise workflow.
DATAFOREST will explain why you need an efficient and effective ETL pipeline process and how to achieve that.
Let's dive into it!
ETL stands for Extract, Transform, Load.
Data warehouses are often used to store business intelligence (BI) data. ETL pipelines are processes and tools that extract, transform, and load data into your data warehouse. ETL integrates BI data with other enterprise data sources, such as customer relationship management (CRM), supply chain management (SCM), and financial systems. ETL is often used with data warehouses and data lakes to store large volumes of unstructured data for future use.
These three steps of ETL pipelines may be repeated with different source and target systems. ETL processes can acquire data, such as a customer’s social media activity or sales data from various sales channels.
The goal of ETL pipelines is to make sure your data is up-to-date, clean and ready for analysis.
How Do ETL Pipelines Work?
Data pipelines are an important part of the ETL process. They help you move data from one system to another, and they can be used in various ways.
There are three main types of data pipelines: extract-transform-load (ETL), streaming, and service-oriented architectures (SOA).
Streaming pipelines are used to process large amounts of data in real-time. Service-oriented architectures are used for calling external services, like web services or other applications, through an API.
A data pipeline is a series of steps that transform raw data into something that can be used for analysis. A typical data pipeline includes:
- Data collection: Where does the data come from? How is it collected and stored?
- Data preparation: What transformations need to be done on the data before it can be analyzed? How will it be cleaned, normalized, and transformed into a suitable format for analysis?
- Storage: Where will your data be stored, and how will other systems access it?
- Analysis: How will you use your data to answer questions about your business or market?
Data warehouses are repositories for storing data, extracted from different sources. Data pipelines help you transform your data before loading it into your data warehouse. Stream processing lets you analyze your data as it streams through your system.
An ETL pipeline takes raw data from multiple sources and transforms it into a format ready for use. The main goal of the ETL pipeline is extracting relevant information from multiple systems. These systems may be business applications or any other source where valuable information is stored in an unstructured format like XML, CSV, JSON, text files etc.
The extracted data must be transformed before loading it into more sophisticated platforms like Hadoop Distributed File System (HDFS), Data Lake, RDBMS, or NoSQL databases, from where further analysis can be performed. This means that if we have one table within the Data Warehouse, with specific columns containing information about our customers, then by using ETL tools, we can extract this info from other tables stored elsewhere.
Hence, this data becomes a well-formed part of our master database containing all necessary records concerning our clients' relationships with us over time.
Users could then generate reports based on specific criteria such as age range/gender/income bracket, etc. So as soon as someone queries these columns, their results show up instantly rather than waiting until someone manually pulls them together first thing each morning after opening up their laptop at home.
DATAFOREST can help you build ETL pipelines, data lakes, and data warehouses for stream processing. For more than 10 years, we have been delivering complex data pipelines to thousands of customers worldwide!
Business Challenges and Benefits of ETL Pipelines
ETL pipelines are used to solve many business challenges in many industries and can help your organization to address the following issues. ETL Pipelines can:
- Data quality and consistency issues: help you to identify and solve many data quality issues, such as inconsistent data types, missing values, and outliers.
- Data volume growth: help you to process large volumes of data.
- Time-to-market: help speed up your data integration projects These are often the bottleneck in the software development lifecycle.
- Business agility: help you quickly change your data processing logic using a visual interface without any code changes.
- Security: help you to protect sensitive data by restricting access to your ETL tools.
- Version control: help you manage the versions of your data processing logic using version control systems.
- Reduced cost: help minimize data integration project costs. This can be achieved by automating data integration tasks, reducing the time it takes to complete projects, and simplifying your infrastructure.
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The Use of ETL Pipelines
ETL Pipelines are an influential tool which data analytics professionals use to extract, transform and load data between data sources and data warehouses. ETL pipelines automate this process and enable your team to focus on more important things, like analyzing your data.
Once set up correctly, the pipeline will automatically pull your data from multiple sources (such as databases) and transform it to the correct format before loading it into an SQL database like Postgres or Redshift (two of our favorites).
The purpose of building ETL pipelines can vary greatly depending on the needs of your business or organization. For example, ETL pipelines may be used to acquire data, clean data, enrich data, aggregate data, or all of these, or more!
Traditional ETL has been around for over two decades and is mature. It has enabled enterprises to capture data from any source, transform it into a standard format, and load it into the target database. Traditional ETL tools are expensive, complex, and challenging, especially for the business analysts who need to use them daily.
The cost of running these systems also adds up quickly because they require expensive hardware and software licenses (like Oracle), CPU/RAM resources that need constant upgrading, and high-end storage arrays due to increased data volumes – all of which add up very quickly over time!
Cloud-based ETL solves these problems by using SaaS (Software as Service) technology that enables customers to access their data without incurring any upfront costs associated with infrastructure setup or maintenance costs associated with traditional solutions where you have an upfront capital investment in hardware & software licenses, etc.
Building ETL pipelines for your business is challenging. ETL pipelines are composed of many different components that work together to make the process of loading data into a data warehouse as seamless as possible. It can be helpful to think of ETL pipelines as a bit like plumbing! A series of tubes, with data flowing through them, where each line represents an individual component in your pipeline. Each member performs one specific task and passes the result forward to others to progress the job.
Extract
Data extraction is the first step in building ETL pipelines. In this stage, we extract data from different sources and load it into the repository.
This can be done in two ways:
1) Using SQL queries to extract data from database tables, files on disk, or web services via HTTP requests;
2) Using Python scripts to access Web APIs, parse XML documents, and so on.
The advantage of using Python over SQL is that Python has a wide range of libraries for accessing different sources like Facebook, Twitter, Instagram, etc., which means there are fewer lines of code required compared to writing similar operations using SQL syntax only. The disadvantage is that Python code is harder to read and maintain than SQL queries.
Full-Extraction
A full extract is when all data from a database is extracted into a single file. This includes both the data you want and any additional information that may be useful, like indexes or foreign keys. The advantage of this approach over other methods is that it reduces the work required to create new tables for analysis; it also makes it easier for other users to understand how your data was collected and what’s included in each table.
Incremental Extraction
An incremental extract is a more involved process that involves extracting data from the database and then analyzing each table individually. This method can be helpful if you want to pull only certain data types or filter out specific fields.
Source-Driven Extraction
A source-driven extraction is similar to an incremental extraction in that it starts with the database and filters out unwanted data. However, this method differs because it involves pulling data from one source (like a relational database) and analyzing each table separately before combining them into a single file.
Transform
Data transformation is the process of changing raw data format, structure, and content into a new design stored in a database or used as input for other operations.
Basic Cleaning
Basic cleaning is the removal of all the junk data you don’t need. This includes blank spaces, tabs, and other characters not part of the actual data. It can also involve replacing or removing special characters such as dashes or commas.
Join Tables
Joining tables combines data from two or more tables into one new or augmented table. This is often done when you want to see the relationships between different tables and how they interact. For example, if you have a table that shows a list of customers and another table that contains their orders, you can join them together so that they both show up on the same page.
Filtering
Filtering is the process of selecting only certain records from a table, allowing you to view only the relevant data. For example, if you have a table containing information about all your customers, but you want to see only those who live in California. You can filter the data within the pipeline so that only these customers are selected for further processing.
Grouping
Grouping is similar to filtering; it only shows certain records from a table. However, the set allows you to view them together instead of separately.
Aggregation
Aggregation allows you to summarize data. For example, the collection will let you do this if you have a table containing sales for each day of the week and want to calculate the total amount sold during a specific period.
Load
The load phase of ETL is the process of extracting, staging, cleaning, and transforming data from a source system into a data warehouse. It often includes the creation of check and reference data and source validation
Flat Files
Flat files are the simplest and most common form of data. Text-based, they can be read by any computer program, so they’re ideal for sharing data between systems or sending files to business partners. When you load flat files into a database, they may need some cleanup before they can be used as reference data (such as removing headers or footers).
SQL RDBMS:
Relational database management systems (RDBMSs) are the most common type of database. They’re designed to store and manage data in a way that makes it easy for users to find what they need. RDBMSs use tables and relations to organize, store, query, and manipulate data.
NoSQL
NoSQL databases are a type of database that isn’t designed to store data in a way that complies with the relational model. Instead, they use different structures and indexing methods to manage large amounts of data and make it easy for users to find what they need.
NoSQL databases streamline the process of building ETL pipelines that help organizations store data in a data lake and quickly have access to the right data, no matter how it’s structured.
Cloud
Cloud-based databases are hosted on servers separate from the computer where you run your application. The advantage of cloud-based services is that you don’t need to worry about maintaining the server or backing up your data—the cloud provider does it for you
ETL processes move data between incompatible applications or platforms for use in analytics, reporting, and business intelligence tools.
Create Reference Data
Reference data is information you use to compare and validate your extracted data.
You will want to create a reference data set for each database table in your production environment, using the same columns and relationships as the source tables. This ensures that you can compare any given record in your production database with its corresponding description in your reference database
Connectors to Extract Data From Sources and Standardize Data
When building an ETL pipeline, connectors are used to connect to a source and extract data from it.
Depending on your business needs and data architecture, you might need to use connectors that can extract data from one source (e.g., Salesforce) or different sources (e.g., POS systems).
Once you're done extracting the required information, it will go through some transformation steps where we'll clean up any missing values, merge duplicates and ensure all fields are consistent. This ensures that once our data is ready for analysis, the pipeline operation has ensured consistent results, removing the need to worry about inconsistent formats during the analysis phase.
Validate Data
The validation stage is critical for building ETL pipelines. This is where you verify that your data is clean and can be used in the next phase of your ETL process. It’s an essential part of the entire process because it may be difficult or impossible to fix errors in downstream processes.
Transform Data
The transformation stage is where you transform your data from one format, schema, type, or database to another, and so on. You can also convert data from one language or system into another. If your business needs it—and chances are good that it will—you'll need a way of migrating source data into a more easily managed state.
Stage Data
Stage data is data that is ready for loading into the data warehouse. This can be either controlled by your ETL solution or outsourced to an ETL vendor who will manage this process.
Load to Data Warehouse
The data warehouse has tables with columns representing each attribute in your source systems. This structure allows for fast queries because it eliminates the need for joins across multiple tables (which is required when querying regular databases).
Scheduling
Scheduling is an integral part of the process of building ETL pipelines. It's used to determine when the tasks in the pipeline are executed and how long they take to run. This helps keep your data fresh, up-to-date and accurate across your organization.
Scheduled ETL pipelines are designed for large data sets—millions of rows or more—that need to be updated frequently throughout the day or week.
Pipeline scheduling can also be implemented on smaller data sets that don't require frequent updates but still need protection from stale information.
The traditional approach to building ETL pipelines is to use tools like Informatica or Talend. However, tools like Apache Kafka have recently become popular because they allow developers to build real-time streaming applications quickly.
With the rise of cloud computing, many companies are considering leveraging big data without spending large sums on IT infrastructure. As a result, there has been an increase in the popularity of cloud-built ETL pipeline tools that offer businesses an alternative to traditional enterprise software.
ETL pipelines are the backbone of your data warehouse, so it's essential to find the right tools for the job. The right tool can help save time and money while making your ETL process more efficient.
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DATAFOREST provides AI-powered cloud data pipeline-building services, and we love our customers. With all the complexity of different ETL pipelines, we've put together a simple success checklist to help ETL users get the most out of our service and stay on top of their projects. Use this list as a reference while building ETL pipelines, and then find out how DATAFOREST can help you improve them!
Why DATAFOREST?
DATAFOREST helps you build data pipelines: ETL, BI, and stream processing. We have over 15 years of expertise in data engineering and become a reliable data integration partner for clients over the world.
DATAFOREST – Is an AI-powered service company providing data management solutions to various industries. We can help businesses of any size extract meaningful insights from data collected in any format, store them in any data store, and transform them into valuable assets for their operations.
DATAFOREST solutions, like sales and marketing automation, data science, data insights and forecasting, and system integrations, can streamline the collection, storage, and analyses of the data generated by connected devices. The software also provides end-to-end data management for unique requirements, for example operating systems logs; website browsing logs; business transactions; payment information; financial transactions; etc.
Our software helps users to build ETL pipelines that automatically process raw data collected from various sources into a format suitable for storage and research and preprocess raw datasets before storing or analyzing them.
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ETL pipelines are critical to any data-driven business. They are the process that allows you to extract data from one source, transform it into a format that can be stored and processed by your systems, then load that data into a database or other storage system.
ETL pipelines consist of different steps and tasks—you may need to store data in other formats, depending on which component of your pipeline you're considering.
For example, suppose you're building an ETL pipeline for real-time data. In that case, it might involve extracting data from a streaming source like Kafka, and then transforming and storing it in Redshift or BigQuery. If you're building ETL pipelines for batch processing, the same input could go straight into BigQuery or be stored in Google Cloud Storage (GCS) as CSV files.
And remember security! It is key that only authorized users have access to your ETL pipelines and their output, which means setting up user permissions correctly.
If all this sounds confusing, don't worry—we've got you covered! Our DATAFOREST team has years of experience helping companies build ETL pipelines using Python and other highly innovative technologies.
What is an ETL data pipeline?
An ETL pipeline is a system for moving data from one place to another. It can transfer data from its source through a series of transformations, cleansing, and storage processes to a final destination.
ETL pipelines are often used to build data warehouses and extract data from different sources to populate them with new information. However, they can also cleanse or update existing data stores.
With the help of such a pipeline, you can:
- Extract data from various sources, such as databases or web services;
- Transform it into a form that's more convenient for further processing;
- Load it into your Data Lake or Data Warehouse.
Why do we need ETL pipelines?
ETL pipelines are one of the most critical components used in data engineering. They provide a framework for moving data from one place to another, transforming it along the way. ETL pipelines take input data from one application or system and convert it into another format, making it easier to share data.
ETL stands for Extract, Transform and Load. The main reason why companies build ETL pipelines is that they are more efficient than performing these steps manually. A lot of time and effort goes into gathering various types of data from different sources, cleaning and preparing them for storage in one place, and then finally moving them into a database or other storage system. Using ETL tools and pipelines, enterprises can automate most of these tasks.
Benefits of ETL processes
ETL helps to ensure consistent, clean, quality data that business users can access without any issues. They also reduce the amount of manual effort needed to manage data.
Drawbacks of ETL processes
Constructing an ETL is time-consuming and often requires specialist technical expertise. They can also be expensive, depending on how much data needs to be processed and the tools used.
ETL Pipeline Examples
Every company has its data, often in separate siloed systems. But if this data can be made accessible and well organized, you can use it for decision-making, monitoring and reporting.
ETL pipelines are commonly used to migrate data from one system or platform to another, such as moving data from an on-premise database to the cloud or relocating it from a legacy application to a modern SaaS solution.
The ETL pipeline is a critical part of any data analytics project. It’s the process by which raw data is transformed into something that can be used for analysis and reporting. Some companies build ETL pipelines for their data to analyze every aspect of the business, like customer behavior real-time analytics, including user profiling and personalized shopping recommendations based on past purchases and browsing history.
For example, after a company collects sales data from its point-of-sale system and other sources, it needs to transform this information into something that analysts can use. This often involves adding additional fields such as time stamps, product category codes, price point ranges, etc.
Characteristics of ETL pipelines
ETL pipelines typically use a data warehouse as the starting point, although they can also begin in other places, such as platform databases or log files. The ETL process is designed to act as a “middleman” between two different types of data stores: source systems and data warehouses. ETL pipelines aim to transform the raw data from these sources into something that can be consumed by any BI tool.
Differences between ETL and ELT
ETL is a subset of ELT but is a different and more specialized process.
ETL is a way of extracting data from a source system and loading it into a data warehouse.
ELT is the process of taking that data (Already within the data warehouse) and transforming it so that it’s ready for analysis. ETL tools can also perform some of these transformations, but they don’t have all the capabilities needed for full-scale ELT.
