The Managed Data Warehouse service is designed for companies that do not have the time, expertise, or resources to effectively manage a cloud data warehouse. As businesses navigate the complex terrains of data-driven strategies, having a robust data warehouse service is non-negotiable.
Our Managed Data Warehouse is a cloud data warehouse consulting service that offers robust data warehouse management so that you can focus on making an impact with your data.
Whether you're migrating to a cloud data warehouse solution or seeking expert cloud data warehouse consulting services, our team has the knowledge and tools to guide you. Trust in our experience as we elevate your enterprise data warehouse solutions and propel your organization toward data excellence.
Data Warehouse as a Service
Adopting new technology is exciting. It opens up new capabilities for your business. Datateer’s Managed Warehouse is a data warehouse consulting service that gives you peace of mind. We take care of organizing the warehouse for performance and usefulness. And we handle keeping it secure and monitoring costs.
Data Warehouse Strategy & Design
Organize your data and maximize its impact on your business, without worrying about technical constraints. Our data warehouse consulting services include best practices for warehouse design, automatically deployed during onboarding to the Managed Warehouse.
As your data capabilities expand, this data warehouse solution ensures your cloud data warehouse stays organized and optimized.
Data Warehouse Development, Migrations & Support
Most likely, you aren’t starting from scratch. Our cloud data warehouse consulting services can consolidate your current state onto this data warehouse solution. The result is a centralized, consistent solution based on best practices for modern data warehouse design.
As your data strategy grows and changes, Managed Warehouse ensures you stay agile and flexible, with high-touch support.
Data Warehouse Management & Optimization
In any complex technology system, things can and will go wrong. Managed Warehouse monitors security and cost, so you are aware of things that need your attention.
Our best practices for optimal organization get applied automatically as part of the Managed Warehouse cloud data warehouse solution.
Data Warehouse Technology
Managed Warehouse supports Snowflake and Google BigQuery. Take advantage of modern best practices from our cloud data warehouse solution. You can use the best data warehouse technology available, without worrying about all the constraints and nuances to maximize your investment in data and analytics.
"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)
A data warehouse is a specialized database designed for data analytics. This is best understood by comparing a data warehouse to an application database.
Web and mobile applications use a database to store data. An application database like Postgres, MySQL, SQL Server, or Mongo DB is designed for speed and accuracy for applications. The reading and writing transactions in an application database update only a few records at a time. An example might be an e-commerce customer saving a new order.
In contrast, a data warehouse is designed for aggregated analytics. An example from e-commerce might be getting the average order size. This requires reading all records and performing a calculation across millions or billions of records. Similar to the example of reading data, data warehouses are designed to load data in bulk from application databases or other data sources.
So, data warehouses are designed to support data analytics operations. They perform faster, are more cost-effective to scale, and are easier to manage for data analytics.
A data warehouse’s primary purpose is to power data analysis. It is a database that is central to any data architecture. Raw data flows into the warehouse, is transformed into metrics and insights, and flows out of the warehouse to the audiences that need it.
A related and important purpose for a data warehouse is to be a place that provides access to consistent, fresh metrics and KPIs for a business to use in decision-making.
Often organizations will attempt to use some other technology for data analytics purposes. And often they make good progress towards their goals.
However, they quickly realize that the performance, scalability, and maintenance of other technologies are not as good as using a data warehouse technology.
Any organization attempting to combine data from multiple systems and databases for the purpose of data analytics should use a data warehouse.
The difference between an “enterprise” data warehouse from a “regular” data warehouse is often just marketing speak.
All cloud data warehouse providers offer a tiered subscription model or a way to get additional “enterprise” features beyond the core data warehouse services. Enterprise data warehouse solutions include things like single sign-on, audit logs, stronger encryption, and finger-grained permission management.
Because these features are available in the SaaS model of the cloud data warehouse vendors, you can upgrade in the future if you do not need them now.
A data lake is a cloud storage solution designed to be a place to store structured and unstructured data in a single location. The benefit of this is that all your data is in one place. However, using the data is a technical exercise, not as easy as might be assumed.
A data warehouse is designed to be a relational database, making querying and reporting straightforward.
The lines between a data lake and data warehouse are no longer as clear as they once were, with vendors of each providing features of the other.
To decide between the two, remember:
- A data warehouse provides more mainstream functionality required for typical data analytics
- They are not mutually exclusive. In Datateer’s platform and cloud data warehouse consulting services, we use a data lake to easily ingest data. The data lake then feeds the data warehouse for easy querying and reporting.
In any cloud data warehouse solution, data must be replicated from where it originates. This process is commonly referred to as extracting (from the source system) and loading (into the data warehouse.
The T stands for transformation. This is the process of combining data from multiple data sources into a single reporting model and performing any necessary calculations.
ETL is a decades-old pattern common in cloud data warehouse consulting services and with data teams. It stands for “extract, transform, load” and indicates the process that takes place in a data warehouse to prepare data for reporting.
- Extract - pull data from source systems like SaaS products, APIs, and databases
- Transform - combine data and calculate metrics
- Load - deposit the transformed data into a data warehouse
A more recent, improved process is ELT. The same steps are performed, but in a different order:
- Extract - pull data just as before
- Load - BEFORE any transformations, load the raw data into the lake or warehouse
- Transform - do transformations after the data is loaded
This is a significant industry shift because the older ETL pattern results in very complicated, monolithic data pipelines. These are full of bugs and hard to troubleshoot because everything is so tightly coupled together.
ELT splits the extraction and loading apart from the transformation. The result is raw data is loaded into the warehouse, and only then is it transformed. Troubleshooting, data quality, and maintenance are all much simplified and easier than with an ETL pattern.
Snowflake is a data warehouse as a service vendor. Snowflake’s major innovation early in product development was to separate the storage of data from the computation of queries and data transformations.
This innovation took full advantage of some of the cloud’s strengths: cheap storage and scalable computing power. Until Snowflake, cloud data warehouse solutions were not as cost-efficient or scalable.