Data ingestion and ETL are often used interchangeably. But, they’re not the same thing. Here’s what they mean and how they work.
Today’s businesses have increased the amount of data they use in their daily operations, allowing them to meet growing customer needs and respond to issues more efficiently. However, managing these growing pools of business data can be difficult, especially if you don’t have optimized storage systems and tools.
ETL and data ingestion are both data management processes that can make data migration and other data optimization projects more efficient. Although ETL and data ingestion have some overlap in purpose and function, they are distinctive processes that can add value to an enterprise data strategy.
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What is data ingestion?
Data ingestion is an umbrella term for the processes and tools that move data from one place to another for further processing and analysis. It typically involves transporting some or all data from external sources to internal target locations.
Batch data ingestion and streaming data ingestion are two of the most common data ingestion approaches. Batch data ingestion involves gathering and moving information at scheduled intervals.
In contrast, information collection and movement during streaming data ingestion occur in or near real time. Streaming data ingestion is typically the better of the two choices when people want to use current data to shape their decision-making processes.
Data ingestion use cases
Real-time analytics: Through data ingestion, businesses, especially in e-commerce and finance, analyze data to make speedy and accurate decisions.
Customer behavior analysis: Online platforms ingest data to understand user behavior, such as pages visited, items clicked and time spent on a platform. This helps personalize user experiences and make product recommendations.
Operational monitoring: Businesses ingest logs and metrics from their applications and infrastructure, which enables them to monitor system health and ensure uptime and performance.
Supply chain management: Companies in manufacturing and retail take in data from many sources to monitor inventory levels, production rates, shipment statuses and more to optimize their supply chains.
Social media monitoring: Brands and businesses ingest data from social media platforms to monitor mentions, reviews and feedback to gauge public sentiment and respond to customer concerns.
Data ingestion examples
Fraud detection: Through real-time analytics, a credit card company can ingest and use transaction data to detect and block any suspicious activities, protecting customers from potential fraud.
Recommendation systems: Online streaming services like Netflix take in user data to analyze viewing patterns and preferences, which enables them to recommend shows and movies for each user.
Anomaly detection: A cloud service provider ingesting server logs can detect any anomalies or potential system failures, ensuring high availability and performance for its users.
Inventory management: A global e-commerce platform like Amazon ingests data from suppliers, warehouses and shipment carriers to make sure products are stocked and delivered efficiently.
Customer feedback: New restaurants can ingest reviews and ratings from platforms like Yelp and Tripadvisor to understand customer feedback and make improvements where necessary.
ETL (or extract, transform and load) is a more specific way to handle data. Not to be mistaken for ELT (extract, load, transform), ETL is simply a process where data is extracted from multiple sources, transformed into a standardized format and loaded into a destination system. Here’s a closer look at the three phases:
Extract: The extract stage involves taking data from its sources, requiring you to work with both structured and unstructured data.
Transform: Transforming data involves changing it into a high-quality, reliable format that aligns with a company’s reporting requirements and intended use cases, which may involve correcting inconsistencies, adding missing values, excluding or discarding duplicate data and completing other tasks to increase data quality.
Load: Loading data means moving it to its target location, such as a data warehouse repository that holds structured data or a data lake that accommodates both structured and unstructured data.
ETL is an end-to-end process that allows companies to prepare datasets for further usage.
Data warehousing: Companies consolidate data from disparate sources into a single, centralized data warehouse for reporting and analytics, which is particularly useful as businesses grow and find themselves using many software and database solutions.
Data migration: ETL enables businesses to migrate data, as they often need to move data from one system or platform to another without corruption or loss.
Data integration: A data integration use case involves combining data from different departments or from mergers and acquisitions to provide a unified view of a business.
Master data management: ETL extracts data from source systems, transforms it and then loads it into a master database, ensuring an organization has a single, consistent source of truth for crucial data entities like clients and suppliers.
Business intelligence: The transformation of raw data into actionable insights by aggregating, summarizing, and analyzing it to support decision-making.
ETL examples
Analysis of sales data: A business such as a retail chain may consolidate sales data from all of its stores across the country into a central data warehouse, which would enable it to analyze overall sales performance and trends.
System upgrades: A company upgrading its customer relationship management system can use ETL to transfer customer data from the old system to the new one to ensure data consistency and integrity.
Data integration after a merger: After a merger, an enterprise can utilize ETL to integrate employee data from separate human resources systems into a unified HR platform.
Product management: ETL processes can help a multinational business ensure product data from its various regional databases is consistent and unified in its global product management system.
Customer behavior: An e-commerce platform using ETL to transform raw data into structured data can analyze this data to understand user behavior and ultimately optimize user experience.
Data ingestion has real-time data processing capabilities, especially in streaming ingestion, which help businesses get immediate insights and make timely decisions.
Data ingestion is flexible; it can handle a wide variety of data types and sources and adapt to different use cases.
Modern data ingestion tools and platforms are scalable enough to handle large volumes of data.
Improved data availability and lower latency since data ingestion ensures data from various sources is readily available for further processing and analysis.
Drawbacks
Direct ingestion may result in errors or inconsistencies if incorrectly managed, leading to potential data quality issues.
Managing data ingestion from many sources can become complex and end up requiring specialized tools and expertise.
Real-time data ingestion in particular can be resource-intensive, which may lead to increased costs.
If not properly secured, ingesting data from external sources can introduce security vulnerabilities.
ETL benefits and drawbacks
Benefits
The target system often has high-quality data since the transformation phase cleans, standardizes and enriches data.
ETL processes make sure data from multiple sources is consistent and unified to deliver a single source of truth.
Data is optimized for business intelligence and analytics once it is loaded into a data warehouse after ETL.
ETL processes can store historical data, which means businesses can perform trend analysis to inform their long-term strategic decisions.
Drawbacks
ETL processes, especially batch ETL, introduce latency since data is not available for real-time analysis.
Designing and maintaining ETL workflows may require specialized tools and skills, as they can be complex.
ETL, especially the transform phase, can be computationally intensive, requiring robust infrastructure.
Traditional ETL can be rigid and might not adapt quickly to changes in source systems or business requirements.
How are data ingestion and ETL similar?
Despite their different goals, data ingestion and ETL share many similarities. In fact, some people consider ETL a type of data ingestion, although it includes more steps than just collecting and moving information.
Additionally, data ingestion and ETL can support tighter cloud security, adding additional layers of accuracy and protection to datasets as they move to and transform in the cloud. These processes also improve an organization’s overall data knowledge and literacy, as they take the time to meticulously move and change their data to the right format. As a result of either data ingestion or ETL projects, these teams will more than likely identify new data security opportunities they need to take advantage of.
Finally, assistive software is available for both ETL and data ingestion processes. Although some solutions are strictly designed for one or the other, the overlap in what these processes do means many data ingestion products perform some or all of the steps of ETL.
How are data ingestion and ETL different?
Data teams generally use ETL when they want to move data into a data warehouse or lake. If they choose the data ingestion route, there are more potential destinations for data. For example, data ingestion makes it possible to move data directly into tools and applications in a company’s tech stack.
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In addition, data ingestion involves collecting raw data, which may still be plagued with numerous quality issues. ETL, on the other hand, always includes a stage in which information is cleaned and changed into the right format.
ETL can be comparatively slower than data ingestion, which usually occurs in near-real time. A data warehouse might receive new data once a day or on an even slower schedule. That reality makes it difficult and sometimes impossible to access information immediately.
Can data ingestion and ETL be used together?
Many companies use data ingestion and ETL strategies simultaneously. How and when they do that largely depends on how much information they must handle and whether they have existing infrastructure to help with the project. For example, if a company does not have a data warehouse or lake, it is probably not the best time for them to focus on developing an ETL strategy.
One of the primary benefits of data ingestion is that it does not require a company to go through an operational transformation before it starts the process. The main thing companies must focus on is pulling data from reliable sources.
However, when pursuing ETL as a data management strategy, organizations may need to expand their current infrastructure, hire more team members and purchase additional tools. In comparison, data ingestion is a relatively low-skill task.
Getting started with data ingestion and ETL
Enterprises must evaluate their data priorities first before deciding when and how to use data ingestion and/or ETL. Data professionals should question how data ingestion and ETL support short- and long-term goals for using data in an organization.
The main thing to remember is that neither data ingestion nor ETL is the universally best choice for every data project. That’s why it’s common for companies to use them in tandem.
