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

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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

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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, 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!

Learn More about Log-Based CDC with Etlworks for Real-Time Data Replication

Contact Us! 

Selecting the right Data Integration tool

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What is Data Integration?

Data integration is the process of retrieving data from multiple source systems and combining it in such a way that it can yield consistent, comprehensive, current and correct information for business reporting and analysis.

Data integration is challenging. The number of sources and types of data continue to grow, and the data is often autonomous and may be in a variety of formats.

Is Data Integration a necessity?

Just think about it, the data integration market is expected to grow from USD 6.44 Billion in 2017 to USD 12.24 Billion by 2022, at a Compound Annual Growth Rate (CAGR) of 13.7%.

The major factor driving this market is the high demand for tools that can combine numerous heterogeneous data sources, enabling users to get a consolidated view of data and extract valuable business insights.

The growing importance of data analytics and BI applications in taking strategic business decision has made data integration’s role crucial. From collecting data, transforming it into useful insights and delivering it to the users require effective data integration tools.

How do you find the right Data Integration tool?

It is not an easy task to go and find the best tool out there.

“The goal is to turn data into information, and information into insight.”

Once you figure out what are your needs and what projects you want to start with, you can start doing your own research by googling data integration (ETL) tools or asking around what others have been using, what they would use, etc.

Things to consider while choosing an ETL tool:

  • Price: some ETL tools are free, but there are hidden costs, and actual costs. Prices vary according to usage. The more money you spend, the lower the unit price. So most vendors have tailored prices, very few have standard prices across all usages/volumes. The key point here is to know your limits.
  • Scalability: as data sources, volumes, and other complexities increase, scaling and managing ETL process becomes increasingly difficult. ETL tools, especially cloud-based ETL tools, remove this obstacle as they scale as your needs grow.
  • Data Sources: an ability to connect to the sources that you need now and could potentially want. Think of it as an investment in your data journey.
  • Simplicity: the end users must be able to devote a little extra time to learn their way around point-and-click interfaces in an ETL tool so that they are the primary ones in charge of the tool. With cloud-based ETL tools, one tool can be used to manage the entire process, reducing extra layers of dependencies.
  • Real-time: building a real-time ETL process manually is a challenge. With ETL tools handling this for you, having real-time data at your fingertips, from sources throughout the organization, becomes a lot easier.
  • Maintenance: instead of your development team constantly fixing bugs and errors, making use of ETL tools means that maintenance is handled automatically, as patches and updates propagate seamlessly and automatically.
  • Security: the chosen ETL tool must have high-security standards and ensure that you are on the right side of compliance.

There are a broad variety of ETL tools available, each with its own advantages and disadvantages. Gaining an understanding of these differences can help you choose the best ETL tool for your needs.

Data Integration can save the day

It is always a pleasure to get feedback from happy customers – and the road to customer satisfaction in the data integration business is long. To ensure satisfaction, we work with you closely from the very beginning, when you just research your different alternatives.

A data integration project requires commitment and great communication from both parties – when it is given, investing in an integration platform can be the best decision you have ever made.

We provide you with the product and solution demos, explain core features of the platform, answer your technical team’s questions, scope the first pilot together, define how we should move forward from there and the goal is always to develop the best possible solution.

Ready to get started?

Contact us  or request a demo to get your data integration project up and running in minutes.

Etlworks Marketo Integration

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What is Marketo?

Marketo is a cloud-lead management and marketing solution. The product range of Marketo is provided on a subscription basis and covers Lead Management, Sales Insights, Revenue Cycle Analytics and Social Marketing applications. It helps organizations automate and measure marketing engagement, tasks, and workflows, including those for email, mobile, social, and digital ads.

What is Etlworks?

Etlworks is a cloud-native integration platform helps businesses automate manual data management tasks, ensure data that are far more accurate, accelerate business workflows, and provide greater operational visibility to an organization.

After a few minutes setup, Etlworks replicates all your applications, databases, events and files into a high-performance data warehouse like Snowflake or Amazon Redshift, so that you can then use your favorite BI or analytics tools. Create reports, monitor custom dashboards, and more instantly from the cloud.

Connect Marketo to Anything

Etlworks offers connectivity to Marketo’s APIs enabling you to work with key Marketo entities including Lead, Activity, List, Opportunity, OpportunityRole as well as Custom Objects. Etlworks exposes both the SOAP and REST APIs for Marketo ensuring you can handle any integration task.

Use the Etlworks Marketo connector for data integration between Marketo and your CRM system, such as Salesforce, MS Dynamics, SugarCRM, HubSpot, and NetSuite; collaboration or survey tools; webinar platforms; data services; marketing databases; and more.

Etlworks Marketo connector free you to focus on insights, so your company will be faster and more efficient at optimizing your marketing performance and improving your campaigns’ ROI.

Etlworks partnered with CData to provide access to the Marketo API using industry standard JDBC protocol.

Let’s do it!

Connecting to Marketo

Step 1. Obtaining the OAuthClientId and OAuthClientSecret Values. To obtain the OAuthClientIdand OAuthClientSecret, navigate to the LaunchPoint option on the Admin area. Click the View Details link for the desired service. A window containing the authentication credentials is displayed.

Step 2. Obtaining the REST Endpoint URL. The RESTEndpoint can be found on your Marketo Admin area on the Integration -> Web Services option in the REST API section. Note the Identity Endpoint will not be needed.

Step 3. Enable Marketo connector for your Etlworks account. Contact support@etlworks.com to enable connector.

Step 4. Create a Marketo connection to work with data in Marketo.

Stored Procedures

Stored Procedures are available to complement the data available from the REST Data Model. Sometimes it is necessary to update data available from a view using a stored procedure because the data does not provide for direct, table-like, two-way updates. In these situations, the retrieval of the data is done using the appropriate view or table, while the update is done by calling a stored procedure. Stored procedures take a list of parameters and return back a dataset that contains the collection of tuples that constitute the response.

To call stored procure from the SQL flow or from Before/After SQL use EXEC sp_name params=value syntax. Example:

EXEC SelectEntries ObjectName = 'Account'

Extracting data from Marketo

Note: extracting data from Marketo is similar to extracting data from the relational database.

Step 1. Create a Marketo connection which will be used as a source (FROM).

Step 2. Create a destination connection, for example, a connection to the relational database, and if needed a format (format is not needed if the destination is a database or well-known API).

Step 3. Create a flow where the source is a database and the destination is a connection created in step 2, for example, relational database.

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Step 4. Add new source-to-destination transformation.

Step 5. Select Marketo connection created in step 1 as a source connection and select the Marketo object you are extracting data from:mceclip0 (1)

Step 6. Select TO connection, format (if needed) and object (for example database table) to load data into.

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Step 7. Click MAPPING and optionally enter Source Query (you don’t need a query if you are extracting data from the Marketo object unconditionally).

Step 8. Optionally define the per-field mapping.

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Step 9. Add more transformations if needed.

Loading data in Marketo

Note: loading data in Marketo is similar to loading data into relational database.

Step 1. Create a source connection and a format (if needed).

Step 2. Create destination Marketo connection.

Step 3. Create a flow where the destination is a database.

Step 4. Add new source-to-destination transformation.

Step 5. Select FROM and TO connections and objects (also a FROM format if needed).

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Step 6. Optionally define the per-field mapping.

Step 7. Add more transformations if needed.

Browsing data in Marketo

You must have a Marketo connection to browse objects and run SQL queries.

Use Explorer to browse data and metadata in Marketo as well as execute DML and SELECT queries against Marketo connection.

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Ready to get started?

Contact Etlworks today to connect your Marketo instance with Etlworks and unlock the ability to read and replicate many of the objects to your data destination.