How to Approach Data Integration Project

Today, in the internet-focused world we live in, data is widely considered to be among the world’s most valuable resources because of how much potential revenue and the business value it can provide. Data has become a valuable commodity, similar to commodities like oil and gold. As you can see, the data is priceless, and companies are worried about putting their data at risk when attempting to connect systems that collect data. 

Data integration is the process of combining data from several different sources into one unified view, to make the data more actionable and valuable to an enterprise. It’s about efficiently managing data and making it available to those who need it.

The benefits of data integration are numerous.  Here are just a few:

  • Better data integrity and data quality
  • Seamless knowledge transfer between systems
  • Easy available, fast connections between data stores
  • Increased efficiency and ROI
  • Better customer and partner experience
  • Better decision-making
  • Complete view of business intelligence, insights, and analytics

That’s great; but before any data integration occurs careful planning and a range of assessments need to take place. The steps below illustrate the proven plan that we follow to deliver integrated data successfully to our clients.

Define the project

Clearly define your goals and your purpose of the integration. Some questions to answer include:

  • What are you trying to achieve?
  • What are your expectations of this integration?
  • What is the desired workflow?

After asking these questions, document comprehensive functional and non-functional requirements for clarity of scope and deliverables. See examples of the objectives for our data integration projects.

Know and understand the systems

Review all the systems involved with the data, from extraction to every system that uses the final, consolidated output, and have a definite understanding of the business processes involved. 

Connect the systems via data integration platform

Identify any network or firewall configuration necessary for data transfer. Do database or FTP ports that need to be opened? Are data connectors, Web services, APIs, and SDKs available to extract data programmatically?

Determine if there is a flat or structured file exchange and whether txt, csv and xml are an option. Consider whether there is any configuration, modification or programming of the system itself needed to enable data interchange. The objective here is to enable applications and services to communicate with each other.

Implement the project

Identify a project champion. This person will engage all the right stakeholders in the most effective and efficient manner to remove roadblocks and track progress.

 Identify the stakeholders. Which departments within the organization use the data or systems and should be involved in the project? Who within those departments will be involved in the design and implementation of the project? Which senior leaders will have input and oversight?

Test the systems thoroughly before implementation, using sample data. This ensures that data quality rules and data mappings have been implemented correctly.

Go-Live

When everything is completed, you can start relaxing and switch to support mode. If you are using the Etlworks data integration platform, it is easy. Our dedicated support team is here for you 24/7.

Etlworks is also a great choice for your data integration project because we can connect anything. We support the widest range of data formats and targets while maintaining low latency and accuracy.

Lastly, Etlworks can scale large and small. Etlworks supports any number of inputs, from low to high volume, so you can start small and scale up over time.

Contact us for a demo and 14-day trial to see if Etlworks works for your organization.

Data Integration Project: Steps to Success

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.

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.

Data integration platform vs. in-house solution

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.

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.

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.

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