etl – Etlworks Blog

What is reverse ETL? And why is it valuable?

Reverse ETL is a new key component of the modern data stack that enables “operational analytics.”

ETL/ELT

Before defining “reverse ETL”, let’s briefly talk about plain old ETL. Extract, transform, and load (ETL) is a data integration methodology that extracts raw data from sources, transforms the data on a secondary processing server, and then loads the data into a target database.

ETL is nothing new. The concept was actually popularized in the 1970s.

More recently, with the rise of cloud data warehouses, extract, load, transform (ELT) is beginning to replace ETL. Unlike the ETL method, ELT does not require data transformation before the loading process. ELT loads raw data directly into a cloud data warehouse. Data transformations are executed inside the data warehouse via SQL pushdowns, Python scripts, and other code.

ETL and ELT both transfer data from third-party systems, such as business applications (Hubspot, Zendesk, Salesforce) and/or databases (Oracle, MySQL), into target data warehouses. But with reverse ETL, the data warehouse is the source, rather than the target. The target is a third-party system.

What is reverse ETL?

Reverse ETL is the exact inverse process of ETL covered above. Simply put, it’s the process of copying data from the data warehouse to Saas products used by organizations.

Why would I move data out of the warehouse? Well, companies today increasingly engage in operational analytics – an approach consisting in making data accessible to “operational” teams, for operational use cases (sales, marketing, ..). We distinguish it from the more classical approach of using data stored in the warehouse only for reporting and business intelligence. Instead of using data to influence long-term strategy, operational analytics informs strategy for the day-to-day operations of the business. To put it simply, it’s putting the company’s data to work so everyone in your organization can make smarter decisions.

Reverse ETL is the tool responding to the new practice of operational analytics. It can be seen as a bridge between your data warehouse and cloud applications. Reverse ETL tools move the data out of your warehouse to the SaaS products your team loves and uses. For example, the Sales team wants the list of webinar attendees to import as leads into Salesforce, the Support team wants to see on Zendesk the data about accounts with premium support, Finance team wants a CSV of rolled up transaction data to use in Excel or Google Sheets.

The data needed is already available in the data warehouse and with reverse ETL, all you really need is to extract data from the warehouse and sync it to external tools, making it the simplest solution.

Etlworks and reverse ETL

Creating a Reverse ETL pipeline from scratch for such data is a complex process since businesses will have to utilize a high amount of resources to develop it and then ensure that it can keep up with the increased data volume and Schema variations.

Etlworks is a cloud-native data integration platform that allows you to perform ELT or reverse ETL with the help of your cloud data platform.

Etlworks helps you directly transfer data from a source of your choice, such as Snowflake, Amazon Redshift, etc., to any SaaS applications, CRMs such as Salesforce, HubSpot, etc., Support tools such as Zendesk, Jira, etc., in a fully automated and secure manner without having to write the code repeatedly. It will make your life easier and make data migration hassle-free.

Want to take Etlworks for a ride? Sign Up for a 14-day free trial and see the difference! Check out the pricing model to get a better understanding of which plan suits you the most.

What is a Data Pipeline?

Etlworks is the leading provider of cloud-based managed data pipelines.

To gain business insights for competitive advantage every business these days is seeking ways to integrate data from multiple sources. Data and data analytics are critical to business operations, so it’s important to engineer and deploy strong and maintainable data pipelines.

A data pipeline is a set of actions that ingest raw data from disparate sources and move the data to a destination for storage and analysis. Most of the time, though, a data pipeline is also to perform some sort of processing or transformation on the data to enhance it.

Data pipelines often deliver mission-critical data and for important business decisions, so ensuring their accuracy and performance is required whether you implement them through data integration and ETL platforms, data-prep technologies, or real time data streaming architectures.

How is a data pipeline different from ETL?

You may commonly hear the terms ETL and data pipeline used interchangeably. ETL stands for Extract, Transform, and Load. The major dissimilarity of ETL is that it focuses entirely on one system to extract, transform, and load data to a particular data warehouse. Alternatively, ETL is just one of the components that fall under the data pipeline.

ETL pipelines move the data in batches to a specified system with regulated intervals. Comparatively, data pipelines have broader applicability to transform and process data through streaming or real-time.

Data pipelines do not necessarily have to load data to a database or data warehouse. It might be loaded to any number of targets, such as an AWS bucket or a data lake, or it might even trigger a webhook on another system to kick off a specific business process.

Data pipeline solutions

The nature and functional response of data pipeline would be different from cloud tools to migrate data to outright use it for a real-time solution. The following list shows the most popular types of pipelines available. Note that these systems are not mutually exclusive. For example, you might have a data pipeline that is optimized for both cloud and real-time.

Cloud-Based

The cost-benefit ratio of using cloud-based tools to integrate data is quite high. These tools are hosted in the cloud, allowing you to save money on infrastructure and expert resources because you can rely on the infrastructure and expertise of the vendor hosting your pipeline.

Batch

Batch processing allows you to easily transport a large amount of data at interval without having to necessitate real-time visibility. The process makes it easier for analysts who combine a multitude of marketing data to form a decisive result or pattern.

Real-Time or Streaming

Real-time or streaming processing is useful when an organization processes data from a streaming source, such as the data from financial markets or internet of things (IoT) devices and sensors. Real-time processing captures data as it comes off the source systems in real time, performs rudimentary data transformations (filters, samples, aggregates, calculates averages, determines min/max values) before firing off data to the downstream process.

Open Source

Open Source tools are ideal for small business owners who want lower cost and over-reliance on commercial vendors. However, the usefulness of such tools requires expertise to use the functionality because the underlying technology is publicly available and meant to be modified or extended by users.

Data pipeline use cases

Data Migration

Data pipelines are used to perform data migration tasks. These might involve moving data from databases, e.g. MongoDB, Oracle, Microsoft SQL Server, PostgreSQL, and MySQL into the cloud. Cloud databases are scalable and flexible and enable for easier creation of other data pipelines that use real-time streaming.

Data Warehousing and Analysis

Probably the most common destination for a data pipeline is a dashboard or suite of analytical tools. Raw data that is structured via ETL can be loaded into databases for analysis and visualization. Data scientists can then create graphs, tables and other visualizations from the data. This data can then be used to inform strategies and guide the purpose of future data projects.

AI and Machine Learning Algorithms

ETL and ELT pipelines can move data into machine learning and AI models. Machine learning algorithms can learn from the data, performing advanced parsing and wrangling techniques. These ML models can then be deployed into various software. Machine learning algorithms fed by data pipelines can be used in marketing, finance, science, telecoms, etc.

IoT Integration

Data pipelines are frequently used in IoT systems that use networks of sensors for data collection. Data inducted from various sources across a network can be transformed into data available for ready analysis. For example, an ETL pipeline may perform numerous calculations on huge quantities of delivery tracking information, vehicle locations, delay expectations, etc, to form a rough ETA estimate.

Getting started with a data pipeline

Setting up a reliable data pipeline doesn’t have to be complex and time-consuming. Etlworks can help you solve your biggest data collection, extraction, transformation, and transportation challenges. Sign up for Etlwoks for free and get the most from your data pipeline, faster than ever before.

What is Data Integration?

In 2020, it is definitive to argue that one of a company’s most significant assets is its Data.

Data integration involves collecting data from different sources, combining this data into a unified set that can easily be used for a predetermined business function, and enabling you to query and manipulate all of your data from a single interface and derive analytics and statistics. While the sources and types of data continue to grow, it becomes increasingly important to be able to perform quality analysis on that data.

Benefits of Data integration

Data integration is becoming more and more common, as numerous apps and companies race to meet consumer demand to have all of their data collected in one place and in a useful format.

From small businesses that want to spend less employee time creating reports, to large corporations taking a deep dive into an array of user statistics, data integration is indispensable for businesses that want to compete in today’s economy.

Here are the primary benefits of data integration offered to businesses.

Integrate data from multiple sources

Businesses are using tons of applications, systems, and data warehouses within your organization. Nevertheless, these data sources are disparate and siloed. A data silo, is a repository of data that is isolated. Generally, in businesses, this means that the information is under the control of a business unit or department and is not available across the organization. When systems containing valuable data are integrated across an organization, then the data gathered by one app or department can benefit the company as a whole, not just a team or individual.

Inter-system cooperation ensures that your company is free from information silos that benefit the few, and allow data gathered across the organization to be viewed and used by anyone who needs access to it.

For example, integrating data from multiple online stores can give you a more complete understanding of customer behavior and payment processing preferences.

Data integrity and data quality

Data integrity is an important element of data integrations. Data integrity is the assurance of the consistency and quality of the data through its entire lifecycle.
Nevertheless, most of the data is still of poor quality. Eliminating errors in data sets used for business intelligence and decision making is one of the primary advantages of data integration.

When data is gathered manually, then every tool, database and user account must be properly accounted for and set up prior to collecting this data, meaning that any data source overlooked or added last minute results in an incomplete data set as the end result. This also means that reporting protocols must be re-worked whenever a new data source is added.

When data systems are integrated properly, however, errors from overlooked sources do not occur and reports can be run and accessed in real time.

Save time and boost efficiency

When a company takes measures to integrate its data properly, it cuts down significantly on the time it takes to prepare and analyze that data. The automation of unified views cuts out the need for manually gathering data, and employees no longer need to build connections from scratch whenever they need to run a report or build an application.

Additionally, using the right tools, rather than hand-coding the integration, returns even more time (and resources overall) to the dev team.
When systems are properly integrated, collecting data and converting it into its final, usable format becomes a quick, easy task instead of a treasure hunt across your company’s data assets.

All the time saved on these tasks can be put to other, better uses, with more hours earmarked for analysis and execution to make an organization more productive and competitive.

Increase competitiveness

This is the most straightforward from all the other benefits of data integration.

Having a data integration strategy in place can help you to plan what actions you need to take to improve the accessibility of data both externally and internally, you will be able to influence many vital parts of your business. Yes, the overall goal is to generate more profit. An essential part of that is delighting your customers, so offering better services than your competition cannot be overlooked.

Tools for Data Integration

There are numerous tools available in the market that would help us query out the Data effectively since our Data is not going to integrate itself. To name a few, we have some Open Source Data Integration Tools, Cloud-based Data Integration Tools and the On-premises data integration tools.

Again, the question is that how to and which one to choose among those various tools available in the market. The features you should look for in a data integration tool are:

A lot of connectors. There are many systems and applications in the world; the more pre-built connectors your Data Integration tool has, the more time your team will save.

Portability. It is important, as companies increasingly move to hybrid cloud models, to be able to build your data integrations once and run them anywhere

Ease of use. Data integration tools should be easy to learn and easy to use with a GUI interface to make visualizing your data pipelines simpler.

A transparent price model. Your data integration tool provider should not ding you for increasing the number of connectors or data volumes.

Cloud compatibility. Your data integration tool should work natively in a single cloud, multi-cloud, or hybrid cloud environment.

Business intelligence, analytics, and competitive edges are all at stake when it comes to data integration. That is why it is critical for your company to have full access to every data set from every source.

Simple data integration with Etlworks

Etlworks is an all-in-one, any-to-any data integration platform for all your ETL projects, regardless of the complexity, data location, format and volume. There is no code to write — with just a few clicks, Etlworks will extract your data from wherever it lives and get it ready to be analyzed, understood, and acted upon.

Etlworks offers a free 14-day trial. Give it a try today!

Version Control and Why You Need It

data-integration-essential-questions

Data integration is becoming a hot topic.

Business intelligence applications can make use of a comprehensive set of information provided through data integration to derive important business insights from a company’s historic and current data. By providing executives and managers with an in-depth understanding of the company’s current operations, as well as the opportunities and risks it faces in the marketplace, data integration can have a direct bottom-line impact.

In the past, the job of integrating systems was a complex, often costly, frequently cumbersome process, involving on-premise integration tools. These days a new breed of applications, SaaS (Software-as-a-service), is taking over as the integration platform of choice for new integration projects. There is a lot to consider when the developers and IT management approach a data integration project.

The features like ease of use, scalability and flexibility, real-time integration, security, and compliance are essential. This blog will talk about the importance of one feature that often gets overlooked – version control.

Why the version control is important?

Because the data integration tool is a software that is used in performing a data integration process on the data source moving the data to the destination. And, as you are planning to work on a big software project that consists of technical concepts, requires a collaboration of different team members and needs frequent changes, you need to use a version control system (VCS).

Version control is essential to track, organize and control changes over source code and avoid confusion, especially for large, fast-changing projects, like data integration.

Etlworks is a modern, cloud-first, any-to-any data integration platform that supports version control for connections, formats, listeners, flows & macros.

Etlworks Integrator automatically tracks changes for all artifacts: connections, formats, listeners, flows, macros, and schedules. There is nothing to configure except the retention policy.

Version control is supported for the following artifacts: flows, connections, formats, listeners, schedules, macros.

Using Etlwork’s built-in version control you can:

View the history of changes – who-changed-what-and-when.
Compare any two versions.
Revert to any previous version.
Add comments to the commit when saving the artifacts.

Etlworks version control

To access the version control UI click the [@] button at the bottom left corner of each screen.

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Then, click the [@] button at the bottom left corner of each screen to view the history of changes.

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You can compare any two versions by selecting versions and click [View Diff between Selected]. mceclip5

The changes are displayed in a two-panel or unified view. From this view, you can navigate back and forward in the history of changes and also revert to any version. mceclip12

To view the specific version and compare it with a previous one click the [eye] button. mceclip6

To revert to any previous version:

Step 1. Click the [Revert] button. mceclip7

Step 2. Confirm that you want to revert to the previous version by clicking the Revert button.

Step 3. Click [Save] or [Save with message] to complete the revert. mceclip9

When saving any change, to any artifact you have an option to add a message (comments) to the commit. To add a message to the commit click the [Save with message] button. mceclip10

The commit message is displayed in the Change History popup. mceclip11

In Conclusion

After reading this blog, hopefully, you have a better understanding of version control. Version control is very helpful for organizing and backing up artifacts you are working with. It is also helpful for multiple people working on one artifact. Version Control is a great way to keep your artifacts organized and backed up in case of the worse.

Begin your data journey, get 14-day free trial!

Change Data Capture (CDC) & Why It Matters

change-data-capture

What is change data capture? Does it have any importance or bearing on the work that you do? We’ll answer the first question shortly, but the answer to the second question is most certainly “yes.”

What Is Change Data Capture?

Change data capture (CDC) solves data integration problems by monitoring, inserting, updating, and deleting changes to records or activity on table data.

The first step in this process is to identify that a change has been made to a data source. To do so, a CDC procedure will inspect the relevant table to see if there is a delta between the old values and new values, based on what’s stored in the source systems, then update the pertinent record accordingly.

There are several good methods for doing this — high watermark, diff, and database triggers, for example. However, one of the superior approaches to track data changes is log-based CDC.

While interpreting the changes in the transaction log is difficult, the biggest benefit of log-based change data capture is the asynchronous nature of the CDC: changes are captured independent of the source application performing the changes. With log-based CDC, new database transactions – including inserts, updates, and deletes – are read from source databases’ transaction or redo logs. The changes are captured without making application level changes and without having to scan operational tables, both of which add additional workload and reduce source systems’ performance.

By allowing you to capture and extract only changed data, change data capture eliminates or reduces the need for batch windows, replacing bulk load updates with continuous streaming or incremental loading of data. Change data capture helps you improve efficiency by reducing redundant data replication and delivery, reducing data warehousing costs and facilitating real-time data integration across your data stores.

Why Change Data Capture Matters

There are many advantages of using transaction logs for CDC:

This solution is transparent to databases and has minimal impact on the database.
Near real-time publishing of the changes to the target system. This means the business can take accurate decisions based on the most current data.
No impact on the transactions at the source application, no additional SQL load on the system.
Because CDC transfers only incremental changes it reduced the cost of transferring data.
This approach maintains the order in which original transactions were committed. This is important when the target application depends on the order of transactions in the source system. The ordering guarantees are most often desired by target applications.
The target system can take time to process the messages.

Together these advantages enable the building of streaming data pipelines that help to share application data across a business. This means that businesses are getting fed insights that are up to date and accurate based on the latest data being fed from across many systems. The decisions made from these insights help businesses to remain competitive in their respective markets.

How Etlworks can help

Etlworks is a high-performance data integration platform with the next-generation change data capture technology.

Etlworks supports native log-based change data capture for PostgreSQL, SQL Server, MySQL, Oracle, DB2 and MongoDB databases. Our intuitive visual interface makes it easy to set up, monitor, and manage your data pipelines, eliminating the need for scripting and ensuring quick time-to-value. Unlike other tools that support CDC, there are only two moving parts – your database and Etlworks. You will be up and running with CDC in a matter of minutes!

Tag: etl

What is a Data Pipeline?

How is a data pipeline different from ETL?