Data integration involves bringing together information from disparate sources in order to generate meaningful insight. Reconciling data generated from the software, equipment, and personnel across all of the functional areas of your business can provide you with the valuable information you need to make the right decisions.
However, combining these different sources of data can be a complex, and honestly, headache-inducing challenge.
It can be quite difficult to know the exact scope of a data integration project, particularly when data is siloed across many different functional areas of your business.
Choosing the right approach to integration can save you money and help ensure integration project success. Choosing unwisely can increase the cost of doing integration or prevent you from meeting business-related objectives. In order to maximize success and minimize re-work, a business evaluation should start and guide each integration effort.
Before initiating any data integration project, it is necessary to take the right steps in preparation. While every company has its own unique needs, there are some general tips you can follow in order to achieve smooth and successful data integration.
Find the Best Data Integration Provider
There are many vendors out there with various data integration solutions that are both efficient and resourceful. Therefore, choosing the most suitable data integration solution for your business should be your number one priority.
Finding the right vendor who can overcome all the data integration challenges while implementing the right data management strategy with timely delivery and speed is the most important piece of the puzzle.
Establish a Data Governance Process
To unlock your data’s full value, you should establish and implement a set data governance process in your organization. This process needs to prioritize and include managing risks, data quality, business processes, and data management as a whole.
Having a set data governance policy in place will help you improve your operational processes. Also, it will help you ensure that your data is present in the right format, with the right quality and maximum availability for your stakeholders.
Implement Data Security
Businesses in touch with the latest data integration trends also need to find a way to safely and securely connect on-premise data using different cloud applications and systems.
Taking action on this subject should be a priority, considering the large volume of data that keeps growing.
Data integration projects can be complex, slow, and frustrating. However, it does not have to be the reality.
With Etlworks robust and intuitive solution, data integration is quite simple and straightforward and the data integration project is easy and fast.
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 Upfor 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.
In-house data solutions – otherwise known as the small thing that someone built ages ago and now it’s somehow grown into a critical part of your data infrastructure and you’d rather not touch it because who knows how it all works? Also, some companies are afraid of risks of migration, so they are pursuing the “if it ain’t broke, don’t fix it” strategy which is not always the most efficient or cost-effective.
It’s a very common scenario. But those in-house solutions can quickly become harder and harder to manage and extend to support increasingly complicated requirements.
At some point you face the decision: do you keep improving an in-house solution or do you reassess your needs and look around for a possible off-the-shelf solution?
If you are reading this article, you are in a position when you are:
Tired of dealing with the complicated inputs and outputs and don’t want to write a custom code anymore.
Need a way to connect to all your data, regardless of its format and location.
Need a simple way to transform your data from one format to another.
Need to track changes in your transactional database and push them to your data warehouse.
Need to connect to the external and internal APIs with different authentication schemas, requests, and responses.
Want to create new APIs with just a few mouse clicks, without writing any code.
Prefer not to write any code at all.
Want not to worry about backups, performance, and job monitoring.
Want someone to manage the data integration tool for you.
By selecting Etlworks as your data integration platform, you will be able to implement complex data integration flows with fewer steps and faster.
The key advantages of the Etlworks:
It can read data from all your sources and load it into all your destinations, including most databases, file storage systems and more than 150 SaaS applications.
It automatically parses even the most complicated JSON and XML documents (as well as other formats) and can connect to all your APIs and databases.
It is built for Cloud but works equally well when installed on-premise.
You can visualize and explore all your data, regardless of the format and location, before creating any data integration flows.
You probably won’t need to write any code at all, but even if you will, it will be just a few lines in the familiar programming language: SQL, JavaScript, and Python.
You can use SQL to extract and flatten data from nested documents.
Etlworks is a full-fledged enterprise service bus (ESB) so you can create data integration APIs with just a few mouse clicks.
Etlworks can integrate data behind the corporate firewall when working together with data integration agent.
We provide world-class support for all customers.
Our service is so affordable that you won’t have to get a board approval in order to use it.
No sales call necessary! Sign up and start using it right away.
In the new post-COVID reality, the pressure to do more with less is higher than ever before. By leveraging modern managed integration solutions, your company get a chance not only to save money but also to gain a competitive advantage.
Etlworks is solving data integration challenges since 2016. We are working with companies large and small around the world, in industries such as finance, healthcare, entertainment and consulting, to help them build and manage better data pipelines and deliver better data outcomes.
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.
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.
Salesforce is the world’s #1 cloud-based customer relationship management (CRM) platform.
Salesforce offers a wide range of applications for managing business processes including sales, customer service, marketing, and e-commerce. For many organizations, Salesforce is a rich source of customer data, such as Accounts, Opportunities, Services, Community, Activities, and Leads.
On its own, Salesforce can dramatically improve how companies run their sales operations, support their customers, and provide products and services to a market. With the integration, businesses make Salesforce more valuable through data.
Through integration, you bring data from disparate sources, databases or applications, such as marketing, support, e-commerce, and sales to the data warehouse.
Effective and efficient integration of Salesforce with adjacent enterprise systems — such as databases, ERP and CRM systems, and custom applications — is critical to enabling sales teams, increasing revenue, and better serving customers. By integrating Salesforce with other applications, APIs and resources, you make Salesforce even more valuable to your employees and your organization.
Ready to get started?
Etlworks is a cloud-native data integration platform that 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.
Etlworks Salesforce connector allows fast real-time access to Salesforce data. The connector supports all objects and metadata (fields) available through the Salesforce API and works just like any other database connector. This not only makes it easier to read, insert, update and delete data, it also accelerates the time it takes to turn it into valuable, 360-degree customer insights.
You can load Salesforce Contacts, Leads, Opportunities, Attachments, Accounts, custom objects, etc. directly to/from major cloud and on-premise data sources or synchronize data in both directions. Powerful mapping settings allow you to load and synchronize Salesforce data with sources having different data structures. You can schedule your integration operation to execute it automatically.
Let’s do it!
Extracting data from Salesforce
Note: extracting data from Salesforce is similar to extracting data from the relational database.
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.
Step 5. Select Salesforce connection created in step 1 as a source connection and select the Salesforce object you are extracting data from:
Step 6. Select TO connection, format (if needed) and object (for example database table) to load data into.
Step 7. Click MAPPING and optionally enter Source Query (you don’t need a query if you are extracting data from the Salesforce object unconditionally):
You must have a Salesforce connection to browse objects and run SQL queries.
Use Explorer to browse data and metadata in Salesforce as well as execute DML and SELECT queries against Salesforce connection.
Change Replication and Data Synchronization
Loading data from Salesforce to your data warehouse is just a part of the problem. Real-time analytics require data in the data warehouse to be constantly up-to-date with Salesforce. In Etlworks, you can always have the most current data from Salesforce in your data warehouse by using High Watermark (HWM) change replication techniques.
After the first replication of all the Salesforce data, subsequent replications update the data warehouse data incrementally with refreshes from Salesforce, in near real-time. Data warehouse data will always be up-to-date in a matter of minutes automatically without any user intervention.
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