Managed Data Stack

Managed data stack helps you set up essential elements of the modern data stack and sometimes the entire data stack that you need.

What is a Managed Data Stack?

Companies that seek to become data-driven implement a modern data stack, which is the collection of tools needed to centralize and organize data for efficient use. We’ll get to some of these specific components later — what’s important to understand is this collection of tools is needed to answer more complicated data questions that require information from multiple sources.

Because modern data stacks are necessary data infrastructure for any company seeking to undertake complex analysis, but not every company has the expertise to navigate infrastructure decisions alone, the need for a solution that is more user friendly became evident. Enter the managed data stack.

What is the managed data stack and how does it differ from a modern data stack?

The managed data stack is designed to make it quick and easy to set up the data stack your business needs. You receive essential elements of the modern data stack or, in some cases, the entire data stack.

The fact that the data stack is managed sets it apart from other methods of implementing a modern data stack. Currently, companies have the option of building their own modern data stack, hiring a consultant or agency to build something, or purchasing the different components of the stack individually.

With a managed data stack, one vendor offers multiple components and manages the tools for you. This includes maintaining or accounting for API or schema changes that can require additional technical expertise.

The phrase “out-of-the-box data stack” is another way of describing a managed data stack.

How does it work?

Managed data stack providers exist as SaaS companies that offer most or all components of the modern data stack for quick implementation. The difference is typically the data warehouse — some managed data stack providers don’t provide you with a data warehouse, while others consider the data warehouse to be a core part of the product offering and directly sign their customers up for a data warehouse.

Companies typically offer free trials for you to try the product before you upgrade to their paid service, while others offer a “freemium” product with paid access to their full product offering.

Once you sign up with one of these companies, you can begin to connect your data sources to the platform and get your data projects underway.

The key components of the managed data stack

The components of the managed data stack and the modern data stack are the same.

  • ETL/ELT: Standing for Extract, Transform, and Load and Extract, Load, and Transform, respectively, these methods allow users to efficiently organize data in their data warehouse. In practical usage, companies offer a tool to automate this process for you, so you can connect all of your chosen data sources to your data warehouse.
  • Data warehouse: This is very much what it sounds like — one place to store and organize all of your data. A properly organized data warehouse allows users to easily work with multiple data sources and manage large data sets. They can transform their data or query it using their preferred business intelligence tool for analysis.
  • Data transformation: Transformation is the process of changing some attributes of raw data, such as its structure, format, or even values. With transformation, you can organize your data to create a valuable universal source of truth, so all teams can confidently work with the same data. A robust transformation tool automates many of these processes, including cleaning and preparing data for analysis. You can also automatically capture a record of your data at regular intervals for auditing and history tracking, which is known as snapshotting.
  • Business intelligence (BI) tool: The last component of the managed data stack is a BI tool, allowing companies to more easily analyze and create helpful visualizations of the organized data in their warehouse.

The benefits of incorporating a managed data stack within a data-driven organization

There are many benefits to implementing a managed data stack

  • Fastest time to value: Because the managed data stack is the fastest way to implement critical data infrastructure, it gives companies the ability to more quickly start working with their data. Buying data stack components, negotiating contracts with multiple vendors, and assembling all the pieces takes at least a few weeks. Building a data stack can take months or even years. With a managed data stack, you can get set up in as little as an hour and immediately start on foundational data projects
  • Managed support for API and schema changes: Customers that buy components of their data stack separately or build their data stack will have to navigate API and schema changes themselves. This means spending a lot of time and engineering resources to troubleshoot and directly contacting a number of vendors every time there’s an issue.
  • Easy process for connecting data sources: Managed data stacks typically offer the fastest and easiest way to perform the ETL or ELT process described above. You should be able to begin loading data into your warehouse with just a few clicks if you have credentials for your different sources, so all of your data can continue to live in the same place.
  • Simpler transformation tooling: Data transformation can seem like a daunting task to undertake, especially for less experienced data users. Because managed data stacks are usually built for users of all technical levels, they often have simple SQL editors designed to make this process easier. Some even offer “low-code” or “no-code” transformation tools.
  • Automation made easy: With a managed data stack, it’s easier to automate every step of the process because most or all of your tools live on one platform. You can set sources to automatically sync, so your data is always up-to-date. You can automate transformation so information sent to your BI tool is always accurate. And with a supported BI tool, you can automate dashboards, so you don’t have to constantly pull data manually.
  • Work with one vendor: Instead of navigating multiple contracts and contacting more than one customer support team for every issue, you can work with a single vendor. As an added bonus, many managed data stacks offer data analyst support, either included in your initial cost or as an additional feature that can be purchased. For less-experienced teams, this can be a great way to get some of the foundational data transformations set up, so your team can begin to use your data sooner.

What to look for when choosing a managed data stack

There are a number of considerations for choosing a managed data stack:

  • The technology being offered: It’s important to understand the tools that make up the managed data stack because they need to work with the rest of your tech stack. The ETL tool that’s provided should support all of your data sources (or all of the most critical ones) and any BI integrations you have. Also, the quality of these tools is important. Look for best-in-class tools so you’re not settling for convenience without quality.
  • Scalability: You need tools that will be sufficient as your organization matures. If the managed data stack can’t scale as your volume of data grows, your data needs change, and the number of people who are accessing and using data increases, it’ll become difficult to work with your data or you’ll need to find a new solution sooner than you’d like. 
  • Customer service: Because all or most of your data stack is being provided by one company, it’s important to get the best support possible. You should be confident during the sales process and any trial period that you’ll be supported when issues come up and as your data needs grow. As organizations try to become data-driven, they’ll run into challenges and expert support can make difficult projects much more manageable.
  • Implementation time: Understanding timeline is important for planning critical data projects. You should also know what resources you will need and how that may affect implementation time. Most data sources should be easy to connect, for example, but if there will be technical challenges, you should look to address them early.
  • Cost: Organizations choosing a managed data stack over other setup options are frequently start-ups or other organizations that are concerned with the resources required to implement the data infrastructure they need. Pricing models for managed data stacks differ from vendor to vendor, so it’s important to understand what a managed data stack will cost and how those costs can fluctuate. For example, if the company will be providing a data warehouse, the amount of stored data and computational power needed to run queries will likely affect your cost. A provider that doesn’t offer a data warehouse can likely offer a lower price, but that means you'll have a separate bill for your data warehouse that needs to be accounted for.

Managed data stack tools that are creating a splash in the market today

There are a number of managed data stack tools that stand out as great options.

Mozart Data offers an out-of-the-box modern data stack that provides companies with integrations for over 300 data sources, a Snowflake data warehouse, easy-to-use data transformation tools, and expert data analyst support. It only takes an hour to set up your data stack.

Prequel is another out-of-the-box option that provides the core components of the modern data stack, as well as dashboard capabilities to make data visualization easier.

5x offers a managed data stack targeted towards enterprise companies, as well as engineering and analyst support.

View the rest of the managed data stack tools on the market here.

Peter Fishman
Mozart Data

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