Insights Blog

Which Database is Best for Analytics? 5 Things to Consider

When searching for a new in-memory analytic database system or types of database in general, it can be overwhelming to sift through the options if you don’t follow a defined process.

Before you can confidently say which database is best for analytics, it’s key that you identify the business applications that need to benefit from performance. Indeed, it is imperative that you clearly specify the criteria needed in a database and know what types of data you’re analyzing.

What Types of Data are you Analyzing?

The best database for analytics is highly dependent on what type of data your organization is analyzing. Different types of data have different characteristics and not all databases are created equal. Before deciding on any specific solution, you first need to determine whether you need a relational database or a non-relational database.

CriteriaRelational DatabaseNon-relational Database
DescriptionData is organized in tables, rows, and columns containing information about specific topics which are linked together by common fieldsData is not organized in tables. This type of database stores data in a non-tubular form (not in tables or rows) or has relaxed schemas that allow changes in the data structure
Query LanguageSQL (Structured Query Language)NoSQL (Not Only SQL)
Storage CapacitySuitable for medium to large data sizeSuitable for large data size
ScalabilityVertical scaling (more powerful hardware)Horizontal scaling (more servers)
PerformanceExcellent for complex queries involving multiple tablesCan be faster for simple queries with large datasets
Use CasesBest for applications with structured data and complex queriesBest for applications with unstructured or semi-structured data, and high write volume
ExamplesOracle, MySQL, PostgreSQLMongoDB, Cassandra, Redis

Relational databases are a perfect fit for an organization if they know exactly what kind of data they’re receiving and how it all ties together. Information such as emails, names, and billing plans can be organized in tables, rows, and columns. Relational databases are ideal for data sets that don’t change often.

Non-relational databases suited for organizations with large quantities of complex data to be organized. Data structures, like documents, can contain different types of information in different formats and the ability to process and organize various types of data points makes this database more flexible. Non-relational databases are also ideal for organizations that need to store data that may change frequently.

How Much Data do You Have?

Now that you understand what types of data there are, it’s time to figure out how much data your organization will be analyzing. Generally, non-relational databases have the ability to read and write large amounts of data since they aren’t structured in a rigid-row design, such as a relational database.

Considerations When Picking the Best Database for Data Analytics

Now that we’ve identified the key difference between the different databases, we can use that context to identify the five key things you should consider when determining which database is best for your analytics needs.

1. General system architecture

One of the first things to keep in mind is that every solution is different, and true analytic performance can only be guaranteed through a tight integration of in-memory computing, as opposed to just adding a cache. Through an integrated in-memory computing approach, users can run larger and more complex analytic workloads, as well as use the database for a wider range of use cases.

2. Costs and scalability

The next thing you should inquire about when researching systems are the software acquisition, or licensing costs, as well as hardware costs. Furthermore, you should ask if the solution is a scalable massively parallel (Loading...mpp/">MPP) system where additional servers can be added in easily. If not, be prepared to foresee further costs as requirements and data volumes continue to grow.

3. Integration

Next, you need to investigate whether the solution is mature enough to handle complex analytic workloads. Does it support commonly-used drivers and interfaces? Does it integrate with the most widely-used Extract, Transform, Load (ETL) and business intelligence (BI) tools, too? As analytic ecosystems adapt over time, you need to ensure that the database will still be compatible in the future.

4. Vendor maturity and proven customer success

Enquire about the vendor behind the product and the levels of ongoing support that it offers. Reaching out to existing customers to discuss the system’s true real-life advantages as well as any shortcomings is also beneficial, that is if their customers are willing to share any insight. Failing that, demand to see customer success stories. It’s not just enough to use a technically solid solution; you need to assure yourselves that they can depend on the vendor and its customer ecosystem for ongoing support.

5. Simplicity

Finally, anyone considering an in-memory analytic database needs to ask whether the solution is easy to install and operate, or whether an army of database administrators is required to tune, design and implement the database as well as ETL processes. The more automated the solution, the fewer hurdles you will have to jump over in order to find value in your analytics and BI projects.

Overall, performance and simplicity are two of the main driving factors in achieving ultimate satisfaction with an in-memory analytic database. Only when performance is super-fast can you run the most complex analyses in near real-time and concentrate on your analytics instead of your database setup.

When researching a new Loading...in-memory database system, we recommend that everyone uses the checklist above in order to ensure they have considered all of the key features they want in a new system, but also use this as an opportunity to simplify their IT infrastructure and use an in-memory database that can process larger workloads with fewer hardware resources.

Exasol offers the Best in Database Analytics

When choosing in-memory database systems, you will find that they scale extremely well, run far more analyses and allow many more users to analyze the data concurrently. This ultimately leads to less automation and less optimization of business processes allowing companies to focus on creating a data-driven business.

It’s time to kiss goodbye to slow queries and say hello to super-fast answers.

Learn more about Exasol’s in-memory analytic database and how to get started for free.