Best Data Warehouse software of 2020
Ranking the best data software in the marketplace...
Data warehouse software gives you the tools to build and manage a data warehouse, providing you with a centralized data storage solution.
As business intelligence and analytics become crucial parts of most business operations, centralized storage solutions are becoming necessary to handle the large amounts of data required. By keeping everything one place, data is accessible across multiple departments and applications, providing more convenient and holistic analytics as a result.
Data warehouses aren’t only useful for business intelligence, of course; data scientists, statisticians, and anyone else handling large amounts of data can benefit from this centralized storage solution. Plus, as the sheer quantity of data continues to increase, data warehouses will become a very necessary storage solution for anyone handling data.
Why use data warehousing software?
While you can build a data warehouse “from scratch,” data warehouse software makes the process much easier. Plus, after building the warehouse, your software can also help streamline management, analytics, and data formatting— all things crucial to supporting data-rich business intelligence and analytics.
Building a data warehouse from scratch can be difficult and unreliable
Would you want to build a content management system? What about your very own business intelligence platform?
If you’re like most people, the answer to this question is a hard “no.” While you can build these things from scratch, doing so would be needlessly time-consuming. Plus, instead of doing important things, you’d be spending most of your time maintaining and updating your creations.
The same applies to data warehouses—and the potential impacts are far greater. Since data warehouses thrive on maintainability and ease-of-use, self-built solutions rarely meet long-term requirements without massive amounts of time and money. As a result, you’re almost always better off using pre-built data warehouse software.
The benefits, of course, aren’t limited to easy builds: Since data warehouse software is built by entire teams of dedicated professionals, most software packages are “automatically” maintained and updated over time. With these teams backing your software, you can rest assured that your data warehousing operations remain functional, maintainable, and at the cutting edge.
Data warehousing increases convenience and saves time
While every organization gets its data from different places, one thing is usually true: An organization’s data doesn’t come from just one place. Most successful applications of data analytics require multiple sources of data, and, with the size of today’s data pipelines, the amounts of data coming through can be overwhelming.
Despite being so overwhelming, however, these large amounts of data continue to empower and improve analytics; companies now have access to more data about more things than they’ve ever had, allowing them to extract even the most subtle insights.
The only caveat, of course, is managing and storing the massive flow of data. This point is where data warehousing comes in.
Previously, analysts with multiple data sources might store data from each in separate locations. For example, data from a stock ticker could’ve been stored in one location, while shareholder dividends were stored in another—and so on. To use both datasets in one application, analysts would have to collect them from separate locations, filter through unnecessary fields, and then (finally!) begin their analysis.
Today, data warehouses automate these processes and keep everything stored in one place, allowing multiple departments and personnel to share from the same pool(s) of data. As a result, analytics becomes far more convenient and departments don’t need to waste time gathering data—everything’s waiting and ready in the warehouse.
Perhaps the biggest part of this convenience, however, is how data warehouses help maintain compatibility between different datasets.
Data warehouse software streamlines formatting and ensures future compatibility
Formatting is a crucial part of the data warehousing process, especially when working with multiple sources. Here, data from one source will usually have fields (or “columns”) different from those of data from some other source. As a result, warehousing data from multiple sources often require adjustments in formatting to ensure future compatibility.
While some data warehouses keep “raw” copies of data (a good practice, in fact), it’s usually preformatted in preparation for future use. When data isn’t formatted this way, it often needs to be reformatted “on the fly” for individual applications. This extra step often requires analysts to spend a lot of time and, by extension, a lot of frustration on what could’ve been an unnecessary step.
Further, by maintaining consistent formats, the same data is easily used between different applications. By ensuring this level of compatibility, departments can utilize more data to generate more insights.
Thankfully, data warehouse software helps with this formatting process, allowing you to select which fields or columns are most appropriate for future use.
Data warehouse software streamlines analytics and improves insights
At their best, data warehouses serve as a single repository of company data—regardless of source. With the entirety of company data stored in a central location and ready to use, departments have more data to work with than ever before.
The benefits to analytics and insights are primarily two-fold: convenience for analysts and, perhaps more importantly, better insights.
For analysts, data warehouses provide unparalleled convenience. As discussed in the previous section, having pre-formatted data in a single place makes data sourcing easier than ever, potentially saving analysts tons of time and hassle.
For insights, while quantity does not always lead to quality, the quantity can help—especially if the data is relevant to the analytics being performed. With a greater quantity and greater variety of data than ever before, analysts can now generate subtle (but impactful) insights by identifying connections in (seemingly) unconnected data.
Consider previous scenarios where analysts had to work with compartmentalized data from individual sources; while they were often able to extract some insight, it often wasn’t enough to identify more holistic trends across the entire organization.
Efficient data management
Just like how centralizing data storage makes data gathering easier for analysts, centralization can also help data managers, scientists, and IT professionals manage their data more efficiently.
The benefit isn’t limited to storage, however; data warehouse software can also help optimize and improve management tasks such as labeling, segmenting, and controlling inflows and outflows. Plus, with everything in one place, data warehouse software can provide holistic statistics and snapshots in an instant.
Data warehouse software is necessary for keeping up with business intelligence trends
While business intelligence trends come and go, one thing usually remains the same: Business intelligence operations continue to need more data.
It’s not hard to see why this is true. With more (relevant) data, business intelligence and analytics can potentially extract greater insights and unlock subtle connections between different datasets. While companies have done well to grow their data pipelines, they haven’t always done so well to manage the inflow.
With ever-growing quantities of data, companies are quickly finding that data warehousing is not just a convenient tool— it’s mandatory. Backed with good data warehouse software, companies can more effectively manage their data and perform modern (read: full-scale) business intelligence and analytics.
Who uses data warehousing software?
Data warehouses may seem like an analyst’s dream—and, frankly, they are. But there are still plenty of other people who can benefit from the efficiency and convenience of data warehouse software.
Business Intelligence (BI)
BI and data warehousing have long been synonymous. Even as the two slowly gain independence from one another, data warehouses still form the backbone of many BI applications—and will continue to do so well into the future.
Most BI frameworks follow a relatively straightforward pattern: Identify data sources, extract data from the sources, store the data, and then perform analysis. Here, data warehouses mostly fulfill the storage requirements of this process; however, with advancements in data warehouse software, data warehouses are beginning to fulfill other requirements as well.
For example, since importing data is crucial to the warehousing process, many examples of data warehouse software come equipped with tools capable of extracting, loading, and transforming data. Even more robust software packages are beginning to assist with basic analysis—at least analyses related to the warehouse itself.
In any case, data warehouses are crucial to business intelligence. With more data than ever being used in business intelligence, proper storage is essential to performing clean and holistic analyses. While some business intelligence applications have seen some benefit from other storage solutions (such as data lakes and data marts), many of these are already integrable into most data warehouses—not to mention that data warehouse software can help with these very processes!
The use of data warehouses for business intelligence highlights the importance of proper data storage. However, business intelligence isn’t the only beneficiary—many data scientists use data warehouses for similar purposes.
Many data scientists are now using data warehouses as their primary storage architectures. However, data scientists weren’t always so keen on data warehouses—especially for those involved with machine learning. With files such as XML and Apache Parquet, why would machine learning efforts bother with an otherwise constrained data warehouse?
As data warehouses find new degrees of flexibility, many data scientists have made the switch. In time, data science will likely become the primary driver of business decisions over business intelligence—both of which strongly rely on data warehouses. Even as data science gradually replaces aspects of business intelligence, new data science initiatives will often have to use previously built data warehouses.
Thankfully, these processes are further streamlined with the help of data warehouse software.
It’s also worth noting some key differences between data science and business intelligence; despite their similarities, they have some key distinctions which often dictate the appropriateness of using a data warehouse.
Business intelligence thrives on static, structured data. This quality makes business intelligence an ideal candidate for data warehouses, whose foundations are built consistent formatting and organizational structures.
By contrast, most data science applications work with large amounts of fluid, unstructured data from a multitude of sources. In essence, data warehouses act as a “filter” between “raw” data and the applications that use it.
As a result, data warehouses are sometimes seen as unnecessary for many data science applications. However, this perception is likely to change; as data becomes increasingly complex, some data science applications may not be able to handle unstructured data in the same ways they used to. In these cases, data warehouses and data warehouse software may become essential tools for data scientists, helping to preprocess and store data for vital applications.
IT Departments and System Administrators
As business intelligence and data science become integral parts of many organizations, IT departments are often given the task of maintaining the required data storage solutions. With other forms of data storage often too limited or unsuited for the task, data warehouses are usually the solutions of choice—and the task of managing them is often placed on the IT department.
Thankfully, with data science software, IT departments, and system administrators can manage and configure data warehouses with relative ease. Plus, as many warehouses move to the cloud, IT teams can usually integrate data warehousing into existing cloud solutions.
Every Department (and Everyone)
Ultimately, the benefits of data warehouses fall on the departments and people they serve. While data warehouse software still helps the people directly responsible for configuring the warehouses, the warehouses themselves are what ultimately benefits every department’s unique data-rich applications and analytics.
As a result, everyone can benefit from data warehouse software—even executives making decisions based on warehouse-backed analytics. Despite such varying applications, however, most data warehouse software shares a few common features.
Data warehouse software should work with any number of data sources and pipelines. Since data warehouses act as a centralized storage solution, your choice of software (and, by extension, your data warehouse) should work with every one of your data sources and pipelines. Without this level of flexibility, extra steps could be required to extract, load, and eventually transform certain formats from certain sources—a potentially time-consuming process.
As a result, make sure that your data warehouse software and the warehouse it ultimately builds works to maintain compatibility across multiple sources. This flexibility is especially useful as data sources increase in number, size, and complexity.
Data warehouse software should be compatible with your organization’s formats and file types. Your organization is likely to have a preferred file type of format. As a result, your data warehouse should be compatible with your preferred format(s), and your choice of data warehouse software should also be capable of converting raw data into the format(s) of your choice. Thankfully, most data warehouse software is compatible with most known formats.
Data warehouse software should help you maintain raw copies of data. While formatting is a crucial part of the data warehousing process, different applications may call for different formats; for example, one application might require every available field, while another application might require just a small handful.
In any case, keeping raw copies of data is crucial for maintaining flexibility across any number of applications and departments. As a result, choose data warehouse software that makes it easy to maintain raw copies and backups.
Data warehouse software should offer robust data management tools. It’s not enough to simply load and store data, nor is it enough to simply transform your data into a specific format. As data continues to increase in quantity and complexity, data warehouse software must be increasingly capable of performing a multitude of data management tasks.
Data warehouse software should encourage compatibility with end-user applications and analytics. Just as data warehouse software should work between multiple data sources, it should also work with multiple data applications. In some respects, data warehouses act as reliable “intermediaries” between a multitude of different sources and a multitude of different applications; the value of the warehouse rests on its ability to keep data centralized and compatible for any number of applications.
While keeping raw copies of data is one way to ensure compatibility, the warehouse itself should be compatible with outgoing data pipelines and applications. If specific departments, users, or applications can’t access data conveniently, then the entire purpose of having a data warehouse is more or less defeated.
Data warehouse software should promote accessibility and flexibility in design. As sources and requirements change, so will the look and design of your data warehouse. Despite sometimes maintaining consistency to the point of constraint, data warehouses should ultimately act as “fluid” structures that change to fit new sources and applications. As a result, your choice of data warehouse software should help encourage this level of flexibility.
Q: What is a data warehouse?
A: A data warehouse is a centralized storage solution for large quantities of data. Ideally, data warehouses act as an intermediary between data sources and applications, working to establish and maintain compatibility across different data sets.
Q: What does EDW stand for?
A: EDW stands for “enterprise data warehouse.” This is a common abbreviation for data warehouses, particularly those used at the enterprise level.
Q: How does data warehouse software help with data warehousing? Is it necessary?
A: Data warehouse software makes it easy to build and manage a data warehouse. While it’s possible to build a data warehouse from scratch, doing so would be incredibly time consuming and inflexible; by using data warehouse software instead, you can easily configure data warehouses of any size and stay on top of future changes.
Data warehouse software can also help with other aspects of data management, specifically ETL (extract-transform-load) processes.
Q: What is a data warehouse vs data lake? Which one should I use?
A: Both data warehouses and data lakes are used to store large amounts of data, but they differ in how they structure (or don’t structure) their data. Where data warehouses maintain collections of structured, filtered data, data lakes are simply collections of unstructured data.
As many organizations transform/format data “on the fly” for specific purposes, data lakes are becoming the primary repositories for data. However, for companies with more structured applications, data warehouses are often the preference due to the convenience of having pre-formatted data.
Despite these differences, however, data warehouses and data lakes are often used together. In these cases, data lakes are used to store raw copies of data, and one or more data warehouses are then used to store specially-formatted versions of this data. This way, enterprises can have the best of both worlds.
Q: Are there cloud data warehouse solutions?
A: Yes! As storage requirements increase, many data warehouses are moving to the cloud. This trend is not only helping to fulfill ever-growing storage requirements, but it’s also helping to maintain flexibility in the future.
As requirements and applications change over time, so should the structure of your warehouse. As a result, utilizing cloud-based data warehouse solutions can help maintain the necessary flexibility while also minimizing hardware investments.
Popular cloud data warehouse solutions include Snowflake, Microsoft Azure SQL Data Warehouse, and Vertica.
Q: What is Snowflake? Should I use a Snowflake cloud data warehouse?
A: Snowflake is a popular data warehouse solution designed for use in the cloud. Unlike other data warehouse solutions that use pre-existing databases or data software platforms, Snowflake utilizes its SQL engine with a unique architecture designed specifically for the cloud.
While Snowflake is an excellent choice for cloud-based warehouses, many other data warehouse software packages are beginning to deploy their own cloud-based solutions. As a result, you may want to compare different software packages to find the one best suited to you and your organizations.
Q: What is SAP Data Warehouse Cloud?
A: SAP Data Warehouse Cloud is another popular cloud-based data warehouse solution. Designed for the enterprise level, SAP Data Warehouse Cloud functions as an end-to-end data management tool with incorporated analytics. As a result, it has become a popular “all in one” package for cloud-based data warehouses.
Data warehouse software allows you to build and manage data warehouses with relative ease. With a data warehouse in place, you and your organization can enjoy the convenience of having pre-formatted data available from a single location.