4 ways Google’s data cloud gives businesses a competitive edge

SADA Says | Cloud Computing Blog

By Zulfi Umrani | Senior Manager, Data Engineering

Gathering information and leveraging it to get a leg up on the competition is an age-old business strategy. However, until recently, the process of amassing, structuring, and analyzing large data sets was slow, complex, and costly. The most popular primary data collection methods — focus groups and surveys — were limited in scope to representative samples of the population. 

Today, smart devices and mobile apps directly collect mountains of minute personal details on individual customers 24/7, providing modern organizations with access to data at a scope that was unheard of even 10 to 15 years ago. The problem is that most of this information isn’t being put to use. Fewer than half of organizations are leveraging their data to drive innovation, only 29% are experiencing transformative business outcomes, and only 24% have created data-driven organizations.

Why are organizations struggling with their data? Many of them lack an integrated approach to data analytics. Of course, it’s impossible to develop an integrated approach when the data itself is siloed. Typically, organizational data is fragmented across several databases, which are spread across multiple public and private clouds.

With a mission to organize the world’s information and make it universally accessible and useful, Google is uniquely positioned to help organizations transform their big data into smart data. Google calls this approach the “data cloud,” and it empowers organizations to demolish data silos, securely unify data sets organization-wide, and glean the actionable intelligence they need to keep their competitive edge. Here’s how:

1. Enjoy unmatched speed, scale, security and capabilities

Many customers choose Google Cloud Platform (GCP) for access to specific data tools like BigQuery, Google’s serverless data warehouse solution. Originally developed for Google’s internal needs, BigQuery has a 99.99% SLA and uses automatic resource provisioning on a multi-tenant distributed architecture. This enables it to execute even complex queries on petabyte-sized data sets at incredible speeds, without any additional effort from the user. BigQuery isn’t just simple and fast; it’s also highly cost-effective, with a three-year TCO that’s up to 34% lower than cloud data warehouse alternatives.

Cloud Spanner, Google’s fully managed, relational database, is built to handle the most demanding enterprise applications. By combining the benefits of relational databases with non-relational horizontal scalability and performance, Cloud Spanner serves data with low latency while maintaining transactional consistency, an industry-leading 99.999% availability SLA, and enterprise-grade security.

GCP used its inaugural Data Cloud Summit to announce the upcoming introduction of granular instance sizing to Spanner, which will reduce the entry price by a whopping 90% while providing the same planet-size scale and 99.999% SLA. Additionally, Spanner users will soon be able to use BigQuery to query transactional data residing in Spanner, enabling them to glean richer insights in real-time. Key Visualizer, now available in preview, gives developers interactive monitoring capabilities so that they can identify usage patterns quickly.

2. Unify data wherever it lives

Traditionally, if an analyst wanted to run a BigQuery query on data stored in another cloud, they had to first move or copy it to GCP, which meant incurring egress fees. Google eliminated this stumbling block last year with the introduction of BigQuery Omni, a multi-cloud analytics solution that enables users to access and analyze data on AWS — with support for Microsoft Azure just released in preview — without having to move or copy datasets.

Life comes at us fast; so does data. Now available in preview, Datastream is Google’s new serverless Change Data Capture (CDC) and replication service that replicates data streams from Oracle and MySQL databases to GCP services, such as BigQuery and Cloud Spanner, in real-time. This enables organizations to leverage real-time analytics, database replication, and event-driven architectures while simplifying their architecture and significantly reducing latency.

3. Democratize data analytics and machine learning

Most organizations don’t have teams of data scientists on staff. They do have highly skilled analysts who know the data; they just lack data science backgrounds. 

Looker’s intuitive, self-service analytics platform solves this problem by making it easy for anyone to analyze, explore, and create visualizations, then share them with a simple link. Looker was built for multi-cloud — and it just got even better. With the introduction of Looker for Microsoft Azure, users can choose to host their Looker instance on GCP, AWS, Azure, or on-prem.

Google’s efforts to democratize data analytics extend to machine learning. BigQuery ML abstracts away the complexity of traditional ML solutions, enabling analysts to use only basic SQL to build and deploy ML models right inside BigQuery. 

What about users who don’t know BigQuery or SQL? Anyone who knows how to use a spreadsheet can use Connected Sheets to import and analyze up to 10 billion rows of BigQuery data right from within Google Sheets, using only standard pivot tables, charts, and functions.

4. Make data discoverable at scale

Data fragmentation forces enterprises to make tradeoffs to ensure that analysts can easily discover and access it. For example, they may be forced to decide whether to move or duplicate data across silos, which broadens potential analytics use cases, or leave it as-is, which hampers agility.

Dataplex, Google’s new intelligent data fabric, provides an integrated analytics experience that brings together the best of Google Cloud and open source tools so that organizations can centrally manage, monitor, and govern their data across data lakes, data warehouses, and data marts, and make high-quality data securely accessible to Google’s best in class AI/ML technologies.

Let SADA help you build the data cloud you need to stay ahead of the competition

As a trusted Google Cloud Premier Partner with a Specialization in Data Analytics, SADA has an extensive track record of helping organizations harness the power of Google Cloud to solve their data biggest challenges. Contact us to learn more about how we can help you develop a cloud-first data strategy aligned with your business objectives.

White paper: Data warehousing in the cloud

Greater speed, scale, flexibility, modern technology, and business innovation await enterprises that move their data warehouse to the cloud. Download the white paper to learn more about building an analytics strategy to power your performance.

White paper: Data warehousing in the cloud

Greater speed, scale, flexibility, modern technology, and business innovation await enterprises that move their data warehouse to the cloud. Download the white paper to learn more about building an analytics strategy to power your performance.


Our expert teams of consultants, architects, and solutions engineers are ready to help with your bold ambitions, provide you with more information on our services, and answer your technical questions. Contact us today to get started.

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