Big Data & Data Engineering
Almost every machine, gadget, household appliance, and asset is equipped with sensors these days making it “smart”. New technologies must be able to handle large volumes of data from all these sensors, often gathered in different speeds and formats. Plus, there’s data from website statistics, log data, and more. The result has been a data explosion which has caused us to rethink technology and how it fits into our data and analytics architecture. Smart companies turn these large data volumes into insights, resulting in a competitive advantage.
What is Big Data & Data Engineering?
While big data & data engineering is a difficult concept to explain in general, it’s easier if you focus on a technology perspective. Big data & data engineering refers to handling data that can’t be handled (efficiently) by traditional relational database systems such as large SQL databases. From a business perspective, big data & data engineering delivers a lot of advantages that enable new business use cases.
Why do you need Big Data & Data Engineering?
This great question is often followed by “My SQL database can also store large amounts of data”. So, let’s look at why big data & data engineering is becoming increasingly important:
- Storing large data objects in a relational database is expensive.
- Big data technologies do not force the source data to fit in a specific format. You only define the schema of the data when you need the data. This means you define your schema-on-read instead of the traditional schema-on-write.
- Storage can be separated from compute. This means that you only have to pay for the most expensive part (the compute) when you need it.
- Have we already mentioned that the storage is cheap! Depending on the use case, ranging from archiving to ultra-fast premium storage, you can store Terabytes of data for just a few euro per month.
- Data Engineering tools speak more languages! Let’s talk Python, R, Scala, and even SQL.
How can you implement Big Data & Data Engineering?
Whether you’re looking to integrate big data & data engineering into your existing architecture, or start from scratch, Cubis delivers a solution that fulfils your business needs, adds value from your data, and is as futureproof as possible. It all starts with Cubis mapping your data use cases on your technological architecture, so the right tool is used for each job.
To give some examples, big data technology is the ideal solution if you have a project in your data & analytics portfolio that needs to crunch large amounts of unstructured data every night. However, a traditional data warehouse is a better choice if you need to have an aging balance of your receivables from your ERP.
How Cubis can help me with Big Data & Data Engineering?
Cubis will help your company to get the full potential of your big data whether you’re taking your first steps towards big data or need extra guidance. Our range of services include collaborating to find the best architecture for your use case and the hands-on implementation of your selected technology. We also configure the services and connections and write the code needed to transform your data to add value to your company.