ETL (extract, transform, load) tools help organizations assimilate diverse—and incompatible—data sources into a single data warehouse. By integrating vast pools of dissimilar information like this, ETL allows organizations to expose their data to business intelligence tools to derive accurate insights and reporting.

The need for ETL is clear, but extract-transform-load tools come in many shapes and sizes. So how do you make sense of the current ETL technology?

We wrote this guide this help you understand the ETL technologies and trends in 2019 and beyond. Feel free to skip to sections that interest you most:

  1. Batch Processing ETL
  2. Cloud-Based ETL
  3. Free, Open-Source ETL
  4. Streaming ETL: The Future of Data Integration

Batch Processing ETLimage2.png

For more than two decades, "batch processing ETL" pipelines have allowed organizations to efficiently update their data warehouses. At one time, these systems operated through onsite servers. Now, most batch processing ETL solutions are moving to the cloud.

Just as the term implies, batch processing strategies involve the saving of data until a specific time, and processing it together (like delivering a batch of blueberries). Batch processing updates might happen during off-peak hours for massive nightly uploads, but they can also happen on an hourly, even minute-by-minute basis, when dealing with smaller batches.

As the most efficient and time-proven way to load data into a data warehouse, there are many reasons to rely on batch processing today:

  • Efficient and stable: Instead of carrying out an ETL process multiple times—i.e., one time for every transaction—batch processing lets you carry out the ETL process once to update numerous transactions at the same time. This reduces the burden on system resources, offering a more efficient and stable data ecosystem.
  • Can achieve near-real-time updates: Although batch processing ETL isn’t “real-time” per se, uploading batches every 60 seconds offers the benefit of a stable and secure ETL pipeline for near-real-time updates.
  • Reduce load on the system: Batch processing lets you carry out data system updates during off-peak hours to limit the burden on server resources during daytime business hours.
  • A long history of use and a well-understood process: Batch processing has a long history of being used by banks and other large organizations. Banks use batch-processing during the overnight hours to handle tasks like payroll, transaction settlements, and month-end reconciliation.

Apache Hadoop is one of the most popular tools for handling batch processing on large datasets. According to this Hadoop enthusiast:

Hadoop MapReduce is the best framework for processing data in batches. Hadoop is based on batch processing of big data. This means that the data is stored over a period of time and is then processed using Hadoop.”

Apache Kafka is another ETL solution that allows organizations to process and upload tiny batches of new data as they arrive—to achieve a streaming data integration experience.   

Cloud-Based ETLundefined

Onsite data warehouse servers are, for the most part, falling to the wayside. That’s because cloud-based data warehouses are more cost-effective and require zero maintenance for the user. Software service providers have also moved to the cloud or begun to offer cloud-services.

Newsday writes the following about this trend:

“Well over three-quarters of businesses are using the cloud (Internet-based computing services) in some way, be it for email, customer relationship management or a host of other functions. With a multitude of growing options, small firms may want to assess what data and applications it pays to keep on the premises and what it pays to move to the cloud to improve efficiencies.”

To make the cloud-migration process easier, cloud-based ETL solutions like Xplenty offer automatic data integrations, intelligent schema detection, and automatic ETL for the most popular SaaS cloud-service providers—such as Salesforce, Google Analytics, Heap, Facebook Ads, Chartio. This makes it possible to ingest all the data from your various SaaS platforms into a single data warehouse for analysis (with the touch of a button).

If you haven’t moved your data ecosystem to the cloud, this cost calculator for the cloud-based server Microsoft Azure is a great way to explore how much money cloud migration can save. Because most cloud servers offer pay-per-use pricing (and the ability to scale services up or down), you only pay for what you need, when you need it.

Free, Open-Source ETLundefined

There’s nothing better than free, and that’s what you get with open-source ETL tools (if you have the requisite data engineering skills to use them). Most open-source ETL tools assist with the management of batch processing and streaming scheduled workflows.

Scheduled workflow ETL technology—like Apache Kafka and Apache Airflow—allows you to automate the streaming of information from one data system to another. When building a data warehouse for machine learning insights, these workflows are essential.

Apache Airflow is one of the most popular of these open-source workflow automation resources that uses Directed Acylic Graphs (DAGs) to support your ETL pipelines. Apache Airflow is also useful when building data pipelines to a data warehouse for machine learning analysis because it includes the hooks and operators required to connect with AWS (Amazon Web Services) and Google Cloud—the two most popular data warehousing services.

As we wrote in a previous blog post:

“Airflow isn't an ETL tool. Instead, it helps you manage, structure, and organize your ETL pipelines using Directed Acyclic Graphs (DAGs). The easiest way to think about DAGs is that they form relationships and dependencies without actually defining tasks. The nature of DAGs gives you the room to run a single branch multiple times or skip branches of your sequence when necessary. So, you can say that A should run after B and that C should run every two minutes. Or, you can say that A and B run every two minutes and C runs after B.”

It’s interesting to note that large organizations—like Facebook, Google or Airbnb—initially develop these open-source ETL tools to solve a very specific data problem they are facing. Then they release the technology as free, open-source software. For example, Airbnb developed Apache Airflow, the U.S. National Security Agency (NSA) developed Apache NiFi, and Apache Hadoop began as a Google project.

Other free, open-source ETL tools are Talend Studio, Clover Studio, Jaspersoft ETL, KETL, Pentajo Kettle, and Scriptella. Demystifying how all of these tools work together isn’t for the faint of heart, but you can read descriptions of these packages and what data engineers use them for here and we recommend this excellent guide as well.

Streaming ETL: The Future of Data Integrationundefined

Nightly or weekly batch processing ETL is excellent for data archives that don’t need up-to-the-minute accuracy—like tax and payroll records. But if your customer orders a widget today, you want to send it immediately (not wait until tomorrow). Also, if a sudden influx of orders exhausts your inventory, you want to replenish supplies as soon as possible.

Free, open-source Hadoop and Kafka make this kind of up-to-the-minute, steaming data processing possible. Hadoop and Kafka allow you to ingest massive quantities of data (from diverse data structures) as soon as new information appears—so you’re never left in the dark. Plus, Hadoop lets you add additional nodes without needing to reprogram the whole system.

But there’s a catch: The barrier for entry is steep. As we said in another post:

[Hadoop] is so convenient…until you realize you have to train someone or pay someone to build the system, not to mention someone has to be on payroll or retainer to maintain the system.

Cloud-based ETL solutions like Xplenty solve the barrier for entry to streaming data integrations by blending Hadoop, Kafka, and Airflow, into an easy-to-use, graphical interface. That way you don’t have to be a data engineer to set up cutting-edge, streaming ETL integrations for your data warehouse.

How Xplenty Harnesses the Power of ETL Technologies

Xplenty harnesses all of the above technologies to brings you streaming ETL integrations you can rely on:

  1. Stable, efficient and reliable batch processing: we built our cloud-based ETL platform to harness the power of Hadoop, Kafka, and other open-source ETL technologies. Essentially, Xplenty is Hadoop and Kafka as-a-service, empowering novice users to create powerful, up-to-the-minute data integrations.
  2. Cloud-based ETL for the most popular cloud SaaS platforms: Check out the hundreds of ETL integrations that Xplenty offers via its easy-to-use graphical user interface. Xplenty’s automatic, out-of-the-box integrations work with names like Salesforce, Autopilot, GitHub, Google Drive, Google Sheets, Magento, MailChimp, and hundreds more.
  3. Apache Hadoop and Apache Kafka and Apache Airflow as a service: Xplenty has developed its easy-to-use services on top of open-source platforms like Apache Kafka, Hadoop, and Airflow, bringing these powerful ETL technologies to the hands of “non-data-scientists” and inexperienced users.
  4. Streaming data integrations: Xplenty achieves the perfect balance of rock-solid reliable data integration and up-to-the-minute, streaming updates.

While we’re shamelessly partial to Xplenty, we’re not without competitors in the race to provide the best out-of-the-box, streaming ETL integrations. As always, make sure you look at all the solutions available before selecting which ETL solution is right for your needs!

Xplenty: User-Friendly ETL Tools (With Awesome Customer Service)

At Xplenty, we're proud of the way our ETL tools make complicated, streaming data integrations a snap. We're also proud of the way we help our customers get the most from our solutions. If you run into a problem, we're here to hold your hand.

Check out what this user said about Xplenty's customer service in a G2Crowd review:

"As a bonus to the product features, Xplenty has excellent customer service. The team goes above and beyond to work with us to develop our data flows and answer any questions we have about the product in their real-time chat system. If bugs or feature requests are discussed, the support team works with us to find adequate workarounds and keeps us in-the-loop while the fix/feature is implemented."

If you'd like to learn more about Xplenty and our data integration tools, contact our team today!