Traditional data warehouses are melting under the data growth and facing scaling and performance constraints. It’s time to reimagine your data warehouse and move your complex analytics processes to actionable insights with Google Cloud and its serverless model.
Moving from your legacy data warehouse to a modern infrastructure can be quite overwhelming. Choosing among the different cloud vendors and why you would want to choose the one over the other can become quite challenging. We can guide your organization to reimagine your data warehousing and move your complex analytics processes to actionable insights.
The first thing we want to do is understand the environment we live in today. I think most of you know already that the world is generating more data than ever before.
An IDC report talks about how in 2018, we had about 33 zettabytes of the world’s data available. And in 2025, that’s going to grow to an astonishing 175 zettabytes, which includes everything from edge computing, cloud computing, core infrastructure, everything altogether. So essentially that’s a lot of data.
Furthermore, 69% of companies report that they haven’t even created a data-driven organization and 71% say that they have yet to forge a data-driven culture. Google cloud is perfectly positioned to help modernize your data and analytics space, helping your organization accelerate the adoption of modern data analytics strategies.
And at the heart of Google cloud smart analytics platform is BigQuery.
Google cloud consists of a serverless data warehouse providing you the ease of moving your complex analytics processes to actionable insights with BigQuery. Hence your organization can modernize their infrastructure with the greatest simplicity.
As you leverage the managed serverless infrastructure of BigQuery, you can reap the rewards of having great operational efficiency gains that enable you to serve your customers more efficiently and drive better margins for your business.
Secondly, you’re able to create more value and expand the pie as your existing resources are freed up to offer services like Machine learning and advanced analytics capabilities that exist in BigQuery today. Your organization is therefore enabled to further increase revenue.
BigQuery is Google’s enterprise data warehouse solution. It’s fully managed and serverless, which means that BigQuery is able to automate several database operations that in a traditional or legacy environment customers today are struggling with. Imagine your organization not having to worry about patching provisioning, doing performance maintenance and tuning, and a lot of manual operational overhead if you have a fully managed data warehouse like BigQuery.
BigQuery also offers real-time insights over streaming and batch data. We have to remember that traditional data warehouses were designed for batch and legacy BI. So now we have to make sure that we can help businesses modernize and actually be able to ingest streaming and batch data to get real-time immediate insights
BigQuery has built-in machine learning capabilities for predictive analytics. The amount of prediction models that it supports right out of the box covers a wide range of use cases. Other cloud data warehouse offerings have some ML capabilities, but they require bolted down integrations, or they just lack mature ML models in the first place. Google cloud’s data warehouse has all of that out of the box and in standard SQL.
Furthermore, BigQuery has an in-memory BI engine for fast dashboarding and interactive analysis. This enables businesses to modernize and discovering new and improved insights while reducing total cost of ownership.
Also important to note is that BigQuery is a highly reliable, very secure data warehouse with encryption at rest and in transit. BigQuery scales up to petabytes on demand. So you get rid of a lot of the operational overhead, as well as performance and scaling constraints that you have in legacy environments.
What we need to emphasize here is that in a traditional data analytics world, your data warehouses usually had an army of DBS that were focused on performance tuning and resource provisioning and a bunch of deployment configuration work. This will be a hassle of the past
At the end of the day, customers tell us that they spend about 15% of their time on analysis and insights. Compare that to a world of BigQuery where the same customers. When they migrate over to BigQuery, they’re able to focus all of their time on analytics and insights instead of trying to maintain and manage infrastructure. So this is a big win. Do you want to know more? Download the PDF.