Data Lakes: Integrating Data From Multiple Sources with ETL Tools

Utilizing big data is one of the biggest assets your organization can use to stay ahead of the competition. Even though big data continues to grow, most organizations have yet to leverage its capabilities fully. Why? Because millions of data sources exist on the internet and physically. Ingesting and integrating this data can quickly become overwhelming.

With data lakes, you can integrate raw data from multiple sources into one central storage repository. But even the most capable data teams will, at times, have problems integrating large volumes of data. This post explores how data lake ETL tools can help your organization streamline its data integration process.

Our five key takeaways:

Using ETL (extract, transform, load) is the most effective and efficient way to integrate data from multiple sources into a data lake.
While integrating into data lakes has many benefits, it has certain challenges. Organizations should expect data quality issues, scalability problems, and disparate formats.
Using ETL tools in data lakes supports the ingestion, transformation, and loading of data into various storage layers for streamlined analytics and decision-making.
For your organization to get the most out of using ETL in your data lake, you must follow several best practices. These include carefully choosing data sources, practicing incremental data loading, monitoring and optimizing ETL performance, and documenting the ETL process.
Using ETL tools to integrate data in data lakes requires data ingestion, transformation, metadata management, integration, loading, access, and analysis.

In this article, we'll explore how data lake ETL tools can help your organization streamline its data integration process. We'll also look at how to avoid common pitfalls and maximize your organization's return on investment from data lake initiatives.

By understanding the nuances of using ETL tools, organizations can ensure efficient data integration and enable improved decisions.

What is Data Integration?

Data integration involves two main processes. The first process combines data from multiple sources, while the second presents it in a unified, consistent, and easily accessible format. These processes allow your organization to use the data for analysis and reporting.

Data integration is essential for any organization for numerous reasons:

Silos: If data in your organization is stored in separate systems or databases and dispersed across different departments, it can lead to fragmented information. Data integration tools help break down these silos by consolidating data from various sources into a single, unified platform.
Quality: Combining data from multiple sources can lead to inconsistencies, duplicate records, or missing values. Integration cleans, transforms, and validates the collected data before it's used for analysis.
Insights: Data integration enables you to consolidate data from diverse sources and update it regularly, providing up-to-date information for decision-makers.

Data Ingestion vs. Data Integration

While data integration and data ingestion both handle data from multiple sources, the two processes differ in their data management roles. As explained earlier, data integration focuses on combining and transforming data from various data sources into a consistent and unified format, enabling analysis and decision-making. This process involves extraction, transformation, and loading (ETL) to ensure data consistency and usability across different applications and systems.

Meanwhile, data ingestion refers to collecting, importing, and processing raw types of data from multiple data sources and transferring them into a storage system or repository for further data analysis. Data ingestion differs from data integration in that it typically handles raw data without applying any changes to its original format.

What is a Data Lake?

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A data lake is a centralized storage repository that stores large amounts of raw data in its original format. This data comes from different sources, such as databases, IoT devices, SaaS data, and log files. The data lake then stores this data in all manners, including structured, semi-structured, and unstructured data, which enables organizations to ingest data without constraints on schema or structure. Having no constraints lets decision-makers easily analyze data, apply machine learning, and gain valuable insights to make data-driven decisions.

Think of any lake you've ever been to: It contains water from various sources, such as rain, rivers, snow, and melted ice. A data lake is the same but in data form.

What is a Data Warehouse?

A data warehouse is a storage system for processed data that has been aggregated from different data sources to support data analysis and business intelligence (BI). Data warehouses also get data from several sources including log files, databases, application systems, etc. A data warehouse can contain several databases, with each organizing data into columns and rows for easier analysis.

Think of data warehouses as dams or water reservoirs: While the water from these reservoirs can come from different sources, it must be treated or processed before storing to remove impurities.

Data Lake vs. Data Warehouse

Unlike data lakes, data warehouses require pre-processed and transformed data before aggregating it into storage. When data is stored in a structured way, it provides several benefits and drawbacks. For instance, while the data can be easier to understand and use, it limits how organizations can manipulate it for data analytics. In contrast, when data lakes store raw data in its original and untampered format, it allows for greater versatility and flexibility in data analysis.

Other distinct data lake vs. data warehouse differences include:

Users: Due to its raw and unrefined format, data lakes are primarily used by data-savvy professionals, such as data engineers and analysts, for advanced data analytics. On the other hand, data warehouses are more suitable for users with little to no experience with data for reporting and analysis.
Schema: Data lakes have a schema-on-read approach, meaning the data is not pre-processed, and transformations are applied when the data is queried. Data warehouses use a schema-on-write approach, requiring pre-processing and transforming data before loading.
Cost: Data warehousing is more expensive due to specialized software, hardware, and personnel processing the data. Data lakes are more cost-efficient because you store data as-is.

So, is it difficult to integrate data in a data lake?

Challenges Associated With Data Integration in a Data Lake

While data integration provides numerous benefits, there are several challenges you should expect when integrating data in a data lake.

Common problems your organization could encounter include:

Data Quality Issues: Inconsistencies will arise when you have data streaming in from different sources. This is especially true when you can't filter the type of data coming in. Your data lake may, therefore, end up with data quality issues such as having duplicate records, insufficient data, and data that's not usable.
Scalability Problems: As with any system, without proper scalability mechanisms, data lakes can quickly become overwhelmed when continuously fed large amounts of data, which then results in the system slowing down and causing performance issues.
Disparate Formats: With data lakes, you’ll have all types and formats of data from different sources. Converting all this data into a unified and usable format requires time, effort, specialized tools, and expertise.

What is ETL?

ETL (Extract, Transform, Load) is a data integration process.

The ETL process involves three distinct parts: extracting, transforming, and loading data into a target system, as follows:

Extract Data: Extraction involves pulling data from different sources, such as relational and non-relational databases, web applications, and APIs.
Transform Data: The extracted data is then changed into a consistent and usable format, which involves cleaning and validating the data, deduplicating records, and applying data transformations.
Load Data: The loading process involves moving the transformed data into a destination data storage repository.

ETL vs. ELT and Other Integration Methods

While ETL is among the popular methods for data integration, the following approaches may still be viable depending on your organization's needs and workloads:

ELT (Extract, Load, Transform): This approach is closely related to ETL but changes the order of the integration process. After the tools extract data, it loads the raw data into the target system and then applies transformations. This method is often used when dealing with large volumes of data, as it can leverage the processing capabilities of modern data platforms. It provides several benefits, such as better data governance, improved speeds in processing data, greater performance, and cost-effectiveness.
Data Virtualization: This technique creates a virtual layer that provides a unified view of data from different data sources without physically moving or storing the data in a central repository. This approach is useful for real-time data access and reduces the need for extensive data movement and storage. Data virtualization presents numerous advantages when implemented including improved analytics, ease of use for professionals who are not data-savvy, and increased cost-effectiveness.
Data Replication: This process involves replicating the same data across multiple systems. It's often used for offloading and archiving historical data and creating a data backup. With data replication comes various benefits, such as foolproof data recovery and security, faster data access, and improved reliability. This type of integration is best for organizations that prioritize disaster recovery and for those with multiple locations.
Change Data Capture (CDC): This method captures only the changes in new data over time rather than replicating the entire dataset. It's an efficient method for updating databases and eliminates replication workloads. CDC is best suited when an organization needs to keep multiple systems or databases synchronized in real-time. CDC has several advantages, such as improved data warehousing and business intelligence, reduced latency between the source and the target system, and improved auditability and scalability.

ETL Use Cases

Various industries and sectors in the economy use ETL. These use cases of ETL enable organizations to efficiently consolidate, transform, and store data for streamlined analysis and informed decision-making:

Retail and Ecommerce: Sales trends, customer behavior, inventory management, and cart abandonment rates are all important types of data for businesses in this sector. But all this data is only useful if it's consolidated, transformed, and loaded into a single storage system. ETL can integrate data from Ecommerce platforms, CRM systems, and point-of-sale terminals, giving retailers valuable insights for optimizing supply chain management, marketing campaigns, and pricing strategies.
Manufacturing: ETL processes collect and transform data from production lines, quality control, and supply chain management systems. By consolidating this data, manufacturers can monitor equipment performance, optimize production processes, and make data-driven decisions to reduce costs and improve efficiency.
Energy and Utilities: ETL is used in the energy and utilities sector to integrate data from smart meters, sensors, and other IoT devices with historical consumption data and weather information. This consolidated data enables advanced analytics for demand forecasting, grid optimization, and energy efficiency initiatives, contributing to more effective resource management.

How Do ETL Tools Work in a Data Lake?

In a case study dubbed “Architecting an Enterprise Data Lake, A Covid19 Case Study,” the experiment used publicly accessible datasets to integrate data into a data lake. ETL tools extracted data from various sources, such as the World Health Organization (WHO) and the Worldometer official site. These data sources provided information about the COVID cases situation worldwide and information regarding the current situation of countries that were integrated into a data lake. This data was then used to come up with analytics such as the number of confirmed cases, recovered, and deaths.

The following key stages of data integration illustrate how ETL tools work in data lakes:

1. Data Ingestion

Data Lakes collects structured, semi-structured, and unstructured data formats from various sources such as websites, social media channels, databases, files, and APIs.

2. Data Transformation

Once ingested into the data lake, ETL tools transform the data by cleaning, standardizing, and reshaping it according to your business needs; filtering, aggregating, converting, and enriching the data in the process.

3. Metadata Management

Metadata management is essential in data lakes because it helps users understand how data is stored and organized. It can involve different processes, such as applying labels to classify data, cataloging data sets, and capturing lineage information.

4. Data Integration

Data lake ETL helps to merge data from different systems, resolve inconsistencies, and create a comprehensive view of the data landscape, enabling more accurate and consistent analysis.

5. Data Loading

ETL tools load the processed data into the appropriate storage layer within the data lake. Depending on the organization's data storage strategy and analytical requirements, this can include loading data into columnar storage, SQL databases, or distributed file systems.

6. Data Access and Analysis

Finally, data lake ETL tools facilitate data access and analysis by providing connectors and APIs that enable seamless integration with various analytics tools, business intelligence platforms, and machine learning frameworks. This allows organizations to easily extract insights from the data stored in the data lake, empowering them to make more informed decisions and drive business value.

Best Practices for Using ETL Tools in a Data Lake

Optimizing data management and analytics using ETL requires a strategic approach to avoid inefficient processes and outcomes. Here are some guidelines your organization can adopt to ensure that using ETL tools in data lakes improves data quality and enhances decision-making capabilities.

Choose Data Sources Carefully

Not all data sources will add value to your organization’s data stores. Therefore, you must assess the relevance of the different sources against your business model and needs. To do this, here are a few things you should do:

Ensure that the data sources are continuously updated.
If sourcing online, ensure the data sources have a strong online presence.
Consider working with a well-established data vendor.

Practice Incremental Data Loading

Processing large amounts of data at once is extremely time-consuming and requires a lot of resources. When using ETL tools, you should consider using the incremental data loading method, which involves processing only changed or new data.

Achieving incremental data loading requires a systematic approach and the use of appropriate techniques, such as change data capture (CDC), timestamps, or versioning. With CDC, you can capture and track changes in the source data by monitoring the data as it is being modified. When you use timestamps, you can associate a timestamp with each record in the source data to indicate when it was last modified. Versioning involves assigning each record in the source data a version number such that when a change occurs, the version number is incremented.

Monitor Then Optimize ETL Performance

ETL workflows, if not carefully optimized, may develop inefficiencies. Therefore, it’s best practice to check how these tools work and regularly adjust them, if necessary. Here are some things you can do:

Adjust resource allocation by allocating more CPU, memory, or disk resources to critical steps or transformations.
Use monitoring tools or features provided by your ETL platform to track the performance of your ETL workflows.
Leverage parallel processing techniques to divide the ETL workload into smaller tasks and execute them concurrently.
Partition the data into smaller subsets or partitions based on specific criteria such as date ranges, geographic regions, or customer segments.

Document Your ETL Process

Maintaining comprehensive documentation of ETL processes and workflows is vital for business continuity and growth. Sharing knowledge across departments ensures consistency across teams and solves the issue of data silos within your organization.

Integrate Data from Multiple Sources into Data Lakes with Integrate.io

While using ETL tools to integrate data from multiple sources into a data lake is efficient, you should expect challenges like data quality issues, scalability problems, and disparate formats. That’s why it is important to have a systematic approach when implementing ETL and using only the best tools.

If your organization is looking to streamline data integration and leverage the power of ETL tools in a data lake, we can help. Integrate.io offers a comprehensive and efficient solution for your organization to integrate data from multiple sources into data lakes. Our cloud-based ETL capabilities, user-friendly visualized data pipelines, a drag-and-drop interface, and no-code data integration enable you to build rich data pipelines that cater to your specific needs. By leveraging Integrate.io's powerful features, such as automatic transformation, scalability, monitoring, and alerts, your organization can unlock the full potential of big data.

Try Integrate.io and see for yourself how we can help you unlock your data's potential. Sign up for a 14-day free ETL Trial to get a feel of how the platform can help with your data integration needs. Or alternatively, schedule a platform demo with one of our experts to learn how to make the most of your trial.

big data integration

Data Lake ETL:
Integrating Data From Multiple Sources

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