How to Get Data from Multiple Sources

Table of Contents

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.

Five things to know about how to get data from multiple sources:

ETL is the primary method for getting data from multiple sources.
Other data integration methods include ELT, ReverseETL, and CDC.
Choose a platform that automates these data integration methods so you can focus on other areas of your business.
Data integration challenges include data governance, scalability, and data integrations.
Problems associated with getting data from multiple sources include data duplication and transformation rules.

These days, organizations have more data at their fingertips than ever before and collect an incredible number of data sets from various sources.

This creates a paradox for businesses such as e-commerce retailers 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?

Integrate.io is a new ETL platform that also performs ReverseETL, CDC, and other data integration methods, allowing you to seamlessly get data from multiple sources. The Integrate.io philosophy is to simplify the ever-complicated data integration process with its jargon-free environment and no-code out-of-the-box connectors. Now you can get data from sources without data engineering or programming experience. Schedule a 14-day demo now.

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.

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

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 (and other data integration methods) can help you get data from multiple sources and generate better insights such as e-commerce metrics so you can make smarter data-driven decisions.

What is ETL?

ETL (extract, transform, load) can help you get 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 used by e-commerce retailers.
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 types, 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, or a data lake.

After loading data into a warehouse or lake, you can run that data from business intelligence (BI) tools and produce access visualizations, data models, machine learning models, and other data analysis and data management tools.

How to Integrate Data from Multiple Sources

Integrating data from multiple sources is an involved process that requires contemplation and planning:

Step 1: Decide Which Sources to Use

The first step is to identify which data you want to integrate. This may be a more difficult question than it seems, depending on your goals and use cases. For example, you might want to integrate data from a customer relationship management (CRM) system so you can generate intelligence about the customers who buy products from your e-commerce business.

Step 2: Choose a Data Integration Method

Depending on your needs, you may want to use ETL for data integration or an alternative method:

Extract, Load, Transfer (ELT) involves extracting data from a source like an e-commerce database, loading it straight into a data warehouse, and then transforming that data into the correct format for analytics. As you can see, ELT switches the ‘load’ and ‘transfer’ stages of ETL.
ReverseETL uses a data warehouse as the source of data, not the destination. This process extracts the data from the warehouse transforms that data and loads it into an operational system like a relational database or SaaS tool used by an e-commerce retailer.
Change Data Capture (CDC) is a process that syncs data in two or more systems so you can track future changes to those systems in real-time.

A platform like Integrate.io performs all of these data integration methods so you can choose the most effective one depending on the requirements of your data project.

Recommended reading: What is a Data Warehouse, and Why Are They Important?

Step 3: Estimate the Size of the Extraction

Come up with an estimate of how much data will be involved during data integration. Extremely large big data workloads will likely have to run less often to avoid overwhelming your IT resources. They may also require a different technical approach for how to get data from multiple sources.

Step 4: Connect to the Data Sources

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

Using Multiple Data Sources in Data Science Projects

Data science brings immense potential for organizations to gain powerful insight and drive better decisions - but with it, comes the challenge of efficiently managing multiple data sources like:

Databases
Streaming services
APIs
Data Warehouses

Plus more! Combining these data sources presents an opportunity to create a holistic view of customers, operations, and competitive advantages in order to maximize efficiency while optimizing internal processes.

Using multiple data sources in data science is a critical aspect of the modern data science process, as doing so can provide a wealth of information and insights that simply wouldn't be possible from relying on just one source. Prior to connecting to various sources, it's essential to accurately estimate your workload and plan ahead for which APIs or connectors need to be employed in order for maximal impact; failure could result in an inefficient project outcome. But when correctly done, using multiple data inputs greatly magnifies benefits by opening up new possibilities leading towards enriched results - success largely lies within establishing sound strategies beforehand. Having the right strategy and approach makes all the difference!

Integrate.io can help you with this process by allowing you to connect to multiple data sources and create custom-built Extract, Transform and Load (ETL) processes in just a few minutes. This can help streamline the integration process, minimize errors and reduce time spent on manual tasks. It's an easy way to take control of your data integration project and get the results you're looking for. Don't let the complexity of integrating multiple data sources stop you from achieving your goals. Schedule a 14-day demo now.

Things to Consider When Integrating Data from Multiple Sources

Depending on the data integration method you choose, here are some things you need to consider when thinking about how to get data from multiple sources:

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 an e-commerce 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. It can also involve preparing data in a way that complies with data governance standards like GDPR.

Recommended Reading: For more information about how to combine, merge, and get data from multiple sources, check out Integrate.io’s 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, e-commerce 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 be pulling from, and how each data source stores information.

Problem 2: Data Integrations

Each data source you use needs to be integrated with the larger integration workflow. Not only is this a complex and technically demanding undertaking, but it can also impact data integration success 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 how to get data from multiple sources.

Problem 3: Scalability

As your business grows, the problem of how to get data from multiple sources intensifies. If you don’t plan for efficiency and scalability, however, this can slow down the integration process.

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 data integration workflows. For example, you can use techniques such as massively parallel processing (MPP) to simultaneously ETLing information from many different sources.

Recommended reading: Data Transformation Explained

What Are Some Problems with Merging Data?

Even once you’ve learned how to get data from multiple sources, the potential obstacles aren’t over. 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. Data integration tools like Integrate.io 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 e-commerce customers’ gender as “male” and “female,” while another records gender as “M” and “F.” Data consistency issues can be complicated.

Solution 2: Clear Transformation Rules

Again, defining clear transformation rules will help you automate the vast majority of the processes involved in how to get data from multiple sources. As you get more familiar with data integration, you'll get a better sense of which kinds of reconciliations need to be performed over your data sources.

Problem 3: Slow Join Performance

Joining data is often notoriously slow. For example, left joins in SQL have a reputation for being 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.