In the complex landscape of data management, many tool vendors promise an easy path. Yet even the most advanced data integration software requires expert hands to unlock its full potential.
In today's data-driven world, you might find tool vendors promising that everything will be easy. Yet, achieving true success with data integration and data extraction goes beyond mere tools.
It requires a well-coordinated combination of technology, process, and people. That's where the challenge—and opportunity—lies for you. With Datateer, you can discover a consistent approach that simplifies the complexities. We use leading commercial and open-source data extraction tools configured to work together. This modern data architecture allows you to focus on what truly matters: driving business value.
Flexible, adaptable, and attuned to your unique needs, Datateer enables you to connect to any data source, putting you in control and unlocking your data's potential.
Data Integration, Extraction & Replication Solutions
Navigating the world of data management requires powerful data integration, extraction, and replication solutions. Using the right data integration platform, you can connect various data sources with efficiency and precision. From employing effective data extraction software to implementing secure data replication, the journey to harnessing your data's full potential becomes smoother and more manageable.
Datateer brings you the best tools–already integrated together–and a proven process to overcome the inevitable obstacles to clean, consolidated data.
Manage & Connect to Big Data Anywhere (Cloud, etc.)
With the rise of cloud technology and big data ETL tool offerings, managing and connecting to your data has never been more accessible. Whether it's on the cloud or on-premises, Datateer's data integration software ensures that you have constant and reliable access to your essential data, fostering seamless collaboration and insight-driven decision-making.
We partner closely with multiple vendors who provide ETL/ELT tools that we integrate into the platform. With over 500 connectors at our disposal and an open-source framework to create new ones, you will always have a way to integrate your data sources.
Expert Assistance with ETL & ELT Tools
ETL tools and ELT tools are critical in getting data out of operational systems, databases, SaaS products, and APIs. However, understanding how to leverage them effectively can be a challenge.
Why spin your wheels for months just trying to figure out how to apply tools and technology? you can have data flowing into a data warehouse immediately, with production-ready data pipelines managed and monitored for you.
We manage hundreds of data sources and automated extractions to make sure your analytics are always fresh, accurate, and secure. With Datateer's expertise, you'll find tailored solutions that use top-of-the-line ETL software and ETL platforms to transform, load, and extract data in a way that suits your unique requirements.
Secure Data Movement & Replication
Data security is paramount, and secure data movement and replication are crucial aspects of any data management strategy. Datateer's data replication solutions and robust processes ensure that your data is not only moved securely but also replicated accurately across systems, safeguarding its integrity and availability.
We pay attention to how your data flows to the warehouse, where it goes, how it is encrypted, and who can access it. Every time we make a platform improvement to our data security, all of our customers benefit.
Simplify with a Single Data Analytics Platform
Simplification is key in today's complex data landscape. Datateer's unified data integration platform provides a one-stop solution for all your data needs. From data extraction to integration, our ETL solution streamlines your data processes, allowing you to focus on deriving meaningful insights and driving real business impact.
"We created 100 different metrics very relevant to our customers. We have seen significant growth in our key accounts."
Paul Harty - Chief Strategy Officer @ Motion Recruitment
"We were data rich but information poor. When you are moving at the speed we are, you can't just throw people at that problem. Datateer gives us a full picture and saves us a ton of time.”
Kelsey Waters - Senior Director of Operations @ Equinix
"Datateer understood our data and consolidated all that information in a way that dramatically improved the speed and quality of client conversations."
Devin Mulhern - Managing Director @ Denver South EDP
FAQ (Frequently Asked Questions)
Data integration is bringing together data from multiple places and combining all the data together. This is especially useful in data analytics so that you can answer questions across all your business systems.
For example, many businesses have a CRM for sales, a helpdesk product for customer support, and a billing system for sending invoices. Each tool has its own internal database, and they do not talk to each other. So, asking holistic questions is impossible.
Questions like “Do my customers who drag their feet to make an initial purchase also put a strain on my support team?” are impossible unless all the data comes together.
Data extraction is a step in data analytics to get data out of source systems and into a central warehouse. Sometimes it is referred to as “data replication,” because data is copied from the source system, not removed from it.
Extracting data with an ELT tool or ETL tool is the first step in centralizing data for comprehensive analysis. This data movement from the source systems, applications, SaaS products, or data brokers gets data into a central location such as a data lake or data warehouse.
Data replication is a step in data analytics to get data out of source systems and into a central warehouse. This is also referred to as “data extraction.”
Data movement from source systems into a central repository uses pre-built or custom data extraction software. Often these refer to three distinct steps:
- Source system
- Target system
- The job or flow, that runs the code to move data from the source system to the target system
ETL stands for Extract, Transform, Load, and it's a process used to manage big sets of data. Here's how it works. First, "Extract" means pulling data from various sources, which could be databases, websites, or other platforms. Then, "Transform" takes that extracted data and cleans it up or changes its format so it all fits together neatly. This makes it easier to work with later. Finally, "Load" means taking that clean, organized data and storing it in one central place, like a data warehouse.
This way, the data is ready and easy to use for making important decisions or running reports. So, ETL helps in data integration by collecting data from many places, making it uniform, and then storing it where it can be easily accessed and used.
ETL in a data warehouse is a specialized process designed to move and organize data efficiently.
"Extract" means pulling data from various operational systems or data sources you use in your business.
"Transform" then cleans, formats, and enriches this data, making it uniform and ready for analysis. This is critical because data warehouses are structured to support fast queries and complex calculations.
"Load" adds the data to the warehouse itself, organizing it in a way that makes it fast and easy to retrieve when you need to make data-driven decisions.
The ETL process is essential for creating a robust data warehouse that serves as the backbone for your analytics, reporting, and decision-making needs.
ETL tools or software automate the process of extracting, transforming, and loading data into a database or data warehouse. Although ETL processes can be programmed from scratch, when it comes to extracting, transforming, and loading data, a purpose-built tool can save a lot of time and reduce errors.
These tools pull data from different sources, clean and format it, and then insert it into a destination database. This automation speeds up complex data tasks and helps ensure accuracy, making it easier to use data for analytics and decision-making. ETL tools often come with pre-built connectors for various data sources and offer features like data mapping, scheduling, and monitoring.
Reverse ETL takes data from a data warehouse and pushes it into operational systems, like CRMs or marketing platforms.
“Regular” ETL moves data into a warehouse for analytics; reverse ETL takes those insights and applies them where they can drive action. This enables businesses to better synchronize their data across various tools, improve automation, and make data-driven decisions in real time.
For audiences who need data insights, having those insights pushed into the tools they use day in and out is extremely productive. The alternative is going to find answers in reports or dashboards.
Essentially, reverse ETL closes the loop between analytical insights and operational activities.
ELT stands for Extract, Load, Transform. Unlike traditional ETL, where data is transformed before it's loaded into a data warehouse, in ELT the data is first loaded into the warehouse. The transformations are done after the data is already in the warehouse.
This approach takes advantage of the robust computing power of modern data warehouses to handle large datasets and complex transformations.
ELT is often faster and more scalable, making it a good fit for big data scenarios. By separating the extraction and loading from the transformations, these can be managed and scaled independently. It has also allowed specialized tools and vendors have come up that focus on pieces of the data analytics process.
ELT (Extract, Load, Transform) refers to the practice of first loading raw data into your data warehouse and then transforming it there. Unlike ETL, where transformations happen before loading, ELT leverages the power of the data warehouse to handle the transformation work.
Because modern data warehouses are built using the scalability of cloud platforms, this can offer speed and performance benefits. If you're looking for a data warehouse, consider whether it supports ELT capabilities, as this could influence the speed and flexibility of your data operations.