With barriers to entry dropping, now is the time for enterprises to make the move to data warehouse modernisation and AI/ML adoption, says Gregory MacLenna, CEO and Louis van Schalkwyk, head of Technical Operations at Digicloud Africa, Google’s chosen enablement partner in Africa.
“Companies need to move faster now, and they have to reduce ‘time to insights’ of business intelligence and slash their execution time, ” says MacLennan. “But many South African companies don’t have the capacity to move quickly because of legacy data centres with infrastructure that could be decades old. They may have upgraded over the years, but traditional data warehouses were never designed to handle and interrogate petabytes of data at speed, run advanced analytics, or scale quickly and cost-effectively.” Says van Schalkwyk: “It is important for organisations to modernise their data warehouses now, because they need to achieve real-time reporting and analytics from a variety of internal and external data sources and many organisations want to take advantage of AI and ML. Only modern data warehouse solutions can offer this simply, securely and cost effectively.”
Digicloud believes Google Cloud Platform lowers the barriers to modernisation and AI/ML, giving local organisations the fastest time to value and simplest way to overcome legacy hurdles.
MacLennan says: “Google makes the modernisation path as seamless and secure as possible, with end to end partner support and out of the box migration tools that automate tedious processes. Google has also ensured that it reduces the risk of migrations, with expert-designed guidelines and proven methodologies. Digicloud’s Google Cloud partner ecosystem across Africa has proven experience in helping organisations modernise and scale their data warehouse solutions.”
Van Schalkwyk notes: “Google is a world leader when it comes to data analytics and machine learning. In fact, Google founded Tensorflow – the de facto standard for machine learning. Using Google cloud you train your ML models using purpose-built hardware to greatly speed up time to train models.”
“Google’s not-so-secret offering with BigQuery really makes it stand out from the competition when it comes to large datasets with terabytes or petabyte of data and billions of rows. BigQuery is serverless and users can load petabytes of data, either in batch or streaming mode. Once your data is in storage you can keep it there for future use or use the query engine to run standard SQL queries against your data. BigQuery typically executes queries of terabytes of data within seconds, for much faster analysis and time to insight.”
They add that in addition to extremely fast data querying, having their data in BigQuery also allows customers to create machine learning models. Customers can use built in ML with BigQuery to create basic models using only SQL commands, or progress to custom models with easy integration to train their models from Jupyter Notebooks.
But what really helps Google stand out from the competition is that it enables customers to make a zero-commitment, no-expense entry into the environment, he says. It’s a no initial capital expense model, and it’s 100% serverless, so customers can just start using it and pay at the end of the month. It can be as simple as using a web interface – you can simply import your data and explore it without having to write code. MacLennan adds that the fact that there is a free tier to get customers started has also dropped the barrier to entry.
According to Digicloud, Google’s Data Warehousing solution stands out from the competition, as noted by Forrester: BigQuery was named a Leader in The Forrester WaveTM: Cloud Data Warehouse, Q1 2021.
To see real-world examples of the advantages of migrating legacy data warehouses to Google Cloud, see The Enterprise Strategy Group’s report ‘The Economic Advantages of Migrating Enterprise Data Warehouse Workloads to Google BigQuery.’
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