First of all, there are numerous options on the market and each has a slightly different set of functionalities to consider. On top of that, setting up a data warehouse without a good complementary tool can be extremely difficult. In fact, it usually takes a lot of engineering hours to get it right.
It’s definitely not the type of thing you would want to change your mind about after you’ve started the implementation.
But, there’s no need to be afraid of it. There are ways to make the process easier. Let’s go through this issue and point everything you should consider before implementing a data warehouse at the office and choosing your data warehouse tools.
First, what is a data warehouse?
They are mainly intended to perform queries and analyses and often possess large amounts of historical data. Also, the data within a data warehouse is usually coming from a wide scope of sources such as application log files and transaction applications.
The main task of data warehouses is to centralize and consolidate large amounts of data from multiple sources.
Their analytical capabilities allow organizations to gather valuable business insights from their data to improve decision-making. A data warehouse would build a historical record over time that can be of great value to data scientists and business analysts.
Why do you need data warehouse tools?
As it was mentioned, data warehouses act like storages of information that comes from one or several sources.
For instance, an eCommerce business can use it to integrate and combine customer information. It includes customer email addresses, the cash register, comment cards, and so on.
Its main advantage is its role in streamlining data for business intelligence (BI). In other words, the process of ETL in a data warehouse is important for the smooth flow of data from one architectural tier to another.
Keep in mind that contrary to traditional ones, modern data warehouses automate the repetitive tasks involved in creating, developing, and deploying a data warehouse design to meet new business requirements.
This is the main reason why many companies use these tools to gather important insights that help them make better and more informed decisions.
What should good data warehouse tools have?
Above all, you should look for tools that have key characteristics that can help your business in more than one way:
- Data filtering. Make sure that your process can detect and remove invalid, incomplete, or outdated records from the source datasets. You can achieve this with a tool that filters the data for you.
- Data transformation and loading. This means changing data into a format that is compatible with the databases to make data loading simple. Some of these management tools offer built-in transformations.
- Business intelligence (BI) and data analysis. Data warehousing and BI are two different but connected technologies that assist businesses in making better decisions. Make sure your tools have good BI functionalities because they can help generate better business insights.
When you choose your next data warehouse tool, make sure that you look for thorough comparisons online. Try searching the web to find the best Bigquery vs Redshift, Snowflake vs Redshift, or Snowflake vs Bigquery reviews. That way you will get professional insights from people who give an objective analysis of the different tools.
Is it scalable?
Take a step back and think about your company’s growth. Is it expanding quickly? Or is it slowly but surely making progress? It’s in your best interest to select a tool that scales along with your business.
Choose one that offers quick and seamless cluster resizing, with no continuous monitoring. You need this to ensure compliance with the dataset requirements. Determine how scalable an integration tool is based on simplicity, resources, and cost.
Some tools require more maintenance but are very cost-effective, while some tools are horizontally scalable. This means that they offer optimal performance even if users add more nodes to the data warehouses.
If you manage to optimize them properly, they can be quite economical.
Let’s start this off by saying that the traditional approach to data warehousing has already been replaced by its automated alternative. In fact, it needed to happen to address the growing needs of data volume and allow faster time-to-insight. These tools help to automate the repetitive steps involved in designing, developing, and deploying a data warehouse.
The tool should be able to automate the data cleansing process right from the profiling of source data to its validation before loading. It needs to happen to make sure that error-free data is loaded into the data warehouse.
You should also consider choosing a modern tool that supports workflow automation and data model design patterns. It should offer automation at each step from designing the data warehouse to mapping and generating ETL code to load information in a data warehouse.
Streamline the process and you will lower the time, expenses, and risks of data warehousing projects.
As a business expands, it usually brings new integrations with the growth. An expansion almost always involves new integrations. It means integrating diverse data sources, such as cloud sources, in-memory formats, databases, and so on.
Integrations usually lead to growing volumes of diverse data. For a scenario of that kind, it is essential to choose a tool that can integrate data from different applications and information systems.
That is yet another reason to choose wisely. For instance, you wouldn’t make a mistake if you selected a scalable and agile solution to integrate, store, and manage huge amounts of data.
The tool should also be able to simplify data warehousing, so have this in mind once you narrow down the list and start picking your tools.
As you can see, there are a few things that should be considered before making the final decision on your next data warehouse tool.
Establish a clear use case and you will help your organization significantly narrow down the list of tools it is using. In other words, do everything you can to ensure that your data has what it takes to meet your business needs.