Organizations these days have more data at their fingertips than ever before. According to a study by IDG, the average company now manages 163 terabytes (163,000 gigabytes) of information—and this figure is only going up year after year.

But it’s not only the amount of information that organizations have to worry about; it’s also the increasing variety of data sources. Even the average small and medium-sized business uses more than 20 SaaS applications, each of which can generate massive quantities of data.

This creates a paradox for businesses struggling to deal with data complexity. With a deluge of information (and more arriving every day), how can you get data from multiple sources efficiently and unlock the hidden insights that it contains?

Below, we’ll discuss how to get information from multiple sources in a business IT environment, giving you a holistic, single pane of glass view into your data assets. In so doing, you’ll be much better prepared to meet the challenges of modern business intelligence and analytics workloads.

Table of Contents

  1. What is Big Data?
  2. What is ETL?
  3. How To Extract Data from Multiple Sources
  4. How to Combine and Merge Data from Multiple Sources
  5. Challenges of Using Data from Multiple Sources
  6. Problems with Merging Data
  7. Get Big Data from Multiple Sources with Xplenty

What is Big Data?

Big data is exactly what it sounds like: the use of extremely large and/or extremely complex datasets that stretch the capabilities of standard BI and analytics tools. While there’s no formal definition for what exactly makes a dataset “big,” the U.S. National Institute of Standards and Technology defines big data as:

“extensive datasets—primarily in the characteristics of volume, variety, velocity, and/or variability—that require a scalable architecture for efficient storage, manipulation, and analysis.”

As this definition suggests, there are several qualities that make big data distinct from traditional data analytics methods:

  • Volume: The data may be intimidating due to its sheer size.
  • Variety: The data may come in many different forms or file formats, making it harder to integrate.
  • Velocity: The data may arrive very rapidly in real-time, requiring you to constantly process it.
  • Variability: The data’s meaning may frequently change, or the data may have serious flaws and errors.

Dealing with big data is one of the greatest challenges for modern BI and analytics workflows. The good news is that when implemented correctly, ETL can help you collect and process big data—no matter its size or location—to get better insights, monitor historical trends, and make smarter data-driven decisions.

What is ETL?

ETL (extract, transform, load) is the dominant paradigm for efficiently getting data from multiple sources into a single location, where it can be used for self-service queries and data analytics. As the name suggests, ETL consists of three sub-processes:

  • Extract: Data is first extracted from its source location(s). These sources may be—but are not limited to—files, websites, software applications, and relational and non-relational databases.
  • Transform: The extracted data is then transformed in order to make it suitable for your purposes. Depending on the ETL workflow, the transformation stage may include:
    • Adding or removing data rows, columns, and/or fields.
    • Deleting duplicate, out-of-date, and/or extraneous data.
    • Joining multiple data sources together.
    • Converting data in one format to another (e.g. date/time formats or imperial/metric units).
  • Load: Finally, the transformed data is loaded into the target location. This is usually a data warehouse, a specialized system intended for real-time BI, analytics, and reporting.

How to Extract Data from Multiple Sources

Extracting data from multiple sources is an involved process that requires contemplation and planning. In this section, we'll discuss the steps required to extract data from multiple sources.

Step 1: Decide Which Sources to Use

The first step is to identify which data you want to extract. This may be a more difficult question than it seems, depending on your goals and use cases. Also, note that your choice of data sources may evolve over time as your needs change.

Step 2: Choose the Extraction Method

Depending on your needs, you may want to extract all of the data from a given data source every time you perform ETL. Alternatively, you can perform incremental updates on a regular basis by only extracting the changes to a data source, or even run ETL as soon as you detect an update to one of the data sources.

Step 3: Estimate the Size of the Extraction

Once you’ve decided on the data sources to use, and the frequency with which you’ll perform the extraction, come up with an estimate of how much data will be extracted during each run of the ETL process. Extremely large big data workloads will likely have to run less often to avoid overwhelming your IT resources and may also require a different technical approach.

Step 4: Connect to the Data Sources

Each data source may have its own API (application programming interface) or connector to help with the extraction process. If you can’t easily connect to a given data source, you may have to build a custom integration.

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How to Combine and Merge Data from Multiple Sources

Once you’ve extracted data from multiple sources, you need to combine and merge it before loading it into the target destination. In this section, we'll go over some of the most important steps of combining and merging data from multiple sources. Though they are numbered, they do not necessarily need to be completed in this precise order.

Step 1: Data Cleansing

Data cleansing involves deleting information that is old, inaccurate, duplicate, or out-of-date, as well as detecting and correcting errors and typos.

Step 2: Data Reconciliation

Data reconciliation is the identification, integration, and standardization of different data records that refer to the same entity.

Step 3: Data Summarization

Data summarization creates new data records by performing operations on existing records. For example, this step might involve adding up sales figures in different regions to come up with the company’s total sales last quarter.

Step 4: Data Filtering

Data filtering ignores irrelevant information by selecting only certain rows, columns, and fields from a larger dataset.

Step 5: Data Aggregation

Data aggregation combines data from multiple sources so that it can be presented in a more digestible, understandable format.

The 5 steps above are just a small sample of how you can merge and transform data from different sources during ETL. For more information about how to combine and merge data from multiple sources, check out our article Data Transformation: Explained.

What Are The Challenges with Using Data from Multiple Sources?

Using data from multiple sources is necessary for modern BI and analytics, but it can lead to data quality issues if you’re not careful. The challenges associated with using data from multiple sources include:

Problem 1: Heterogeneous Data

Different data sources may store data in different ways, using different data formats. This issue is known as “heterogeneous data.” For example, you may need to take data from files, web APIs, databases, CRM systems, and more. What's more, this information may be structured, semi-structured, or unstructured data.

Solution 1: Increased Visibility

Solving the challenge of heterogeneous data requires you to know exactly which data sources you'll pulling from, and how each data source stores information. These answers are crucial in order to know how you will treat each data source during the extract and transform phases of ETL.

Problem 2: Data Integrations

Each data source you use needs to be integrated with the larger ETL workflow. Not only is this a complex and technically demanding undertaking, but it can also break your ETL process if the structure of the underlying data source changes. What's more, the problem scales as you add more data sources.

Solution 2: Greater Connectivity

Since every data source is different, you may get lucky if the source already has an existing API or connector—or in the worst case, you may have to build your own custom integrations, which is very time- and labor-intensive. Instead, it's better to have a robust solution for ETL data integration that can automatically connect to a wide variety of data sources.

Problem 3: Scalability

As your business grows, you’ll likely want to integrate data from more data sources. If you don’t plan for efficiency and scalability, however, this can majorly slow down your ETL process—especially if you’re working with big data.

Solution 3: Good System Design

When it comes to scalability challenges, the good news is that you can use both horizontal scaling (adding more machines) and vertical scaling (adding more resources to a machine) for your ETL workflow. For example, you can use techniques such as massively parallel processing (MPP) to simultaneously extract information from many different sources.

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What Are Some Problems with Merging Data?

Even once you’ve collected data from multiple sources, the potential obstacles aren’t over. When merging data, look out for the following challenges:

Problem 1: Duplicate and Conflicting Data

Multiple sources may have the same data, requiring you to detect and remove these duplicates. Even worse, the sources may not agree with each other, forcing you to figure out which of them is correct.

Solution 1: Clear Transformation Rules

Solving the problem of duplicate and conflicting data requires you to have well-defined, robust transformation rules that can detect these problems. Data integration tools like Xplenty come with features and components that help you detect and filter duplicate data.

Problem 2: Reconciling Information

Two different data sources may refer to the same entity differently. For example, one source may record users’ gender as “male” and “female,” while another records gender as “M” and “F.” And this is just a simple case—reconciling data consistency issues can be a lot more complicated.

Solution 2: Clear Transformation Rules

Again, defining clear transformation rules will help you automate the vast majority of the data reconciliation process. As you get more familiar with the ETL process, you'll get a better sense of which kinds of reconciliations need to be performed over your data sources. It's a good idea to use an ETL data integration solution that can perform many of these reconciliations automatically.

Problem 3: Slow Join Performance

Joining data is often notoriously slow. For example, left joins in SQL have a reputation for being significantly slower than inner joins. The poor performance of joins can be attributed to both poor ETL design and to the inherent slowness of the join operation.

Solution 3: Avoiding Joins (When Possible)

Avoid unnecessary joins when possible. This is especially true for cross joins, which take the Cartesian product of two datasets, and nested loop joins, which can be inefficient on large result sets. In addition, try to reduce your usage of in-memory joins and merges.

Get Big Data from Multiple Sources with Xplenty

If you want to learn how to get data from multiple sources, remember the following tips:

  • Big data is more complex in terms of its volume, variety, velocity, and variability, but it can be efficiently processed using ETL.
  • Extracting data from multiple sources requires you to decide which sources to use and the extraction method before you begin.
  • Merging data from multiple sources involves several steps, such as cleansing, reconciling, and aggregating data.
  • The difficulties of using and combining data from multiple sources include scalability, heterogeneous data types, duplicate and conflicting data, and slow performance.
  • Avoid issues that arise from combining data from multiple sources by applying ETL best practices.

Dealing with the challenges of getting data from multiple data sources is one reason why we created the Xplenty data integration platform. Xplenty includes more than 100 built-in connections and a simple drag-and-drop interface so even non-technical users can build powerful, robust data pipelines.

Ready to learn more about how Xplenty can help your business needs and objectives? Schedule a call with our team today for a chat about your needs and a free trial of Xplenty.