Key questions to ask before selecting a cloud database

Businesses should carefully evaluate how cloud databases and analytics tools meet today’s needs while balancing cost, complexity, performance, and flexibility. Steve Sarsfield, Director of Product Marketing at Vertica, shares key questions to ask before selecting a cloud database.

Whether you plan to move your analytics workload to a single public cloud provider, multiple cloud providers, on-premises, or hybrid infrastructure, the database you choose has a significant impact on cost, productivity, and value. commercial. With so many databases and analytics providers on the market in various stages of development, here are some key questions IT teams can ask before focusing on a cloud database.

Can it deploy anywhere?

Some SaaS platforms require all data to be loaded into a particular cloud. This locks the customer into a single solution, not allowing them to easily move to another cloud or take advantage of lower-cost computing when it is available. If a SaaS solution is labeled “cloud-native”, it may actually mean “cloud-only” and it may not be able to run workloads in other locations. This is because you are forced to load data into a cloud and analyze it using a single engine. Billing convenience could be an advantage here. However, the company will only be limited to one type of cloud deployment.

Learn more: Top 5 Reasons to Migrate Databases to the Cloud

Can you use data from anywhere?

External data and data lakes are increasingly common in the enterprise, but analytics solutions can vary widely in how they handle workloads and data storage. You want to be able to access data both in the database and externally. You want to give users access to more data sources, even if they are not loaded into the database. How much time you’ll spend loading data into the database and how much an organization will pay while the data lives in the cloud can make a big difference. Storing all data in one type of database is flawed because most modern solutions have combined data warehouse and data lake for analytics.

Is it adjustable and elastic?

When users try to solve slow queries, it is common for cloud-only databases to offer node-based optimization. If your queries are running slowly, many cloud-based systems will add more nodes to increase computing power. That said, analytical workloads are not universal, and database performance can be affected by quarterly reports, a successful marketing campaign that resulted in more data being generated, or poorly written queries.

That’s why it’s important to understand what options are available to speed up queries. Look for systems that offer a massively parallel architecture (the architecture may require manual partitioning and special query modification to take advantage of the cluster); node scaling (scaling nodes and also controlling node size or configuration); workload management (mapping query resources such as memory and CPU to specific query types or a particular set of users); separation of compute and storage (data is persisted in object storage while compute nodes spin up to serve concurrency, backup, dashboards, and data science) and query optimization (schedulers queries that find the best way to limit data reads and memory needed to answer the question).

Does it support various analytical user roles?

As cloud database becomes popular in the organization, be prepared to support a wide range of demands from various users (business users, analysts, data scientists). Are the features offered and at what price?

One must always take into account the depth of the analytical functions offered. Functions can include things like:

    • Time series: SQL functions are built into the database to log recorded data over defined time intervals.
    • Geospatial: SQL functions are based on latitude, longitude and altitude.
    • machine learning: The ability to train, manage and deploy machine learning models.
    • Alternative frames: Support for data science and additional languages ​​besides SQL.

Does it help you control costs?

When moving analytics to the cloud, expenses can quickly spiral out of control and businesses can find themselves locked into long-term contracts. Make sure the solution allows users to slow down computing when not in use and has the ability to set clear limits on spending so there are no surprises at the end of the month. IT teams need to understand how the database automatically adapts to long or complicated queries or when many concurrent workloads hit the system at once. If the database automatically launches additional nodes, these additional nodes are automatically billed in monthly segments.

The ability to have shared storage is also essential as it allows multiple teams to use the same data without making copies. More copies equals more storage equals more money. Finally, many providers levy egress fees, charged per megabyte, on data pulled from their platform. Beware of platforms that will charge you for data recovery.

The fact is that business, technology, consumer expectations, and the regulatory environment are changing so rapidly that no one can predict analytics and storage needs for the future. Today we could move data to the cloud. Tomorrow we could bring it back to site, and some days we would like to use both. This is exactly why the database you choose should be flexible, scalable, and ready for the future.

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