Data is now running the enterprise. No new news there. For a long time, enterprises have been dependent on traditional on-premises data warehouses for collecting and storing data. But is this enough to meet the needs of the enterprise of today?
Your data wants to work hard but is your data warehouse letting it?
Data warehouses now need to be flexible, accessible and easy to maintain. They need to be scalable not only to store the vast volumes of data that enterprises are collecting and storing but also to leverage advanced analytics.
The advent of the cloud has assuaged some of these requirements. But with the proliferation of advanced analytics platforms that leverage AI and machine learning, the typical cloud-based data warehouse also has to get a makeover. That is if you want to explore the disruptive potential of these new technologies.
Google’s Big Query comes as an answer to address the changing demands of the data warehouse.
Google BigQuery – what is it?
Big Query is Google Cloud’s serverless, highly- scalable and low-cost data warehouse that takes a managed services approach to data analytics.
It simplifies how enterprises manage and analyze multi-terabyte datasets. As agility becomes essential for survival, BigQuery provides a unique approach to data management as it allows the enterprise to scale its use of hardware and software in their system. BigQuery runs on the Google Cloud Storage infrastructure and can be accessed easily using a REST-oriented application program interface (API).
What are the benefits?
Here is what makes Big Query ideal for your enterprise –
BigQuery gives enterprises freedom from the tyranny of VMs and CPU/RAM sizing. It is incredibly elastic, boasts of some of the highest levels of manageability, abstraction, and automation.
The Serverless Service model that BigQuery employs allows enterprises to scale data storage seamlessly with zero management. Since storage capacity is not directly linked with memory and compute power, enterprises can scale more easily and according to their data budgets.
Formatting data and provisioning resources constitute a major part of data warehouse management. Even with cloud solutions, enterprises have to spin up or wind down the machine clusters assigned for a particular task.
Since BigQuery is all about ad-hoc queries, it dispenses both these concepts and places an emphasis on ‘query’ over ‘administration’. All the enterprise needs to bother about is connecting the right data sources and running the query. Google manages all the provisioning and maintenance operations.
This is great for today’s data warehouses as enterprises can run their data analytics operations super-fast and without the need of a database admin.
Speed and Flexibility
Agility is a business need. And for business success, the data warehouse needs to be agile too. With Google’s BigQuery, enterprises can extend speed and flexibility to their warehouse as it allows ad-hoc queries on multi-terabyte datasets.
Its Streaming APIs allow users to load up to 100,000 rows per table per second for immediate access. By sharing across several tables, users can also achieve millions of rows per second. Sporting a familiar SQL-like query syntax and intuitive web UI, BigQuery is easy to use. It also allows enterprises to join humongous fact tables to most lookup tables which contributes to its speed and flexibility.
Creating a highly available analytics service can be hard. BigQuery wins here as it seamlessly replicates customer data geographically. The SRE’s manage where queries execute. This could mean that you start your day in one data center and seamlessly end up in another data center a few hours later.
Great integration and accessibility
Data analysts can drive massive datasets in BigQuery with ease directly from a spreadsheet interface since it provides integration with Google Spreadsheets. It has interactive dashboards (you can also build your own easily using Google AppEngine) and enables smooth data export to Google Cloud Storage. It also provides a web UI to enable interactive querying and command-line interface.
Self-optimizing storage engine
The amazing storage engine of BigQuery continuously evolves and optimizes storage according to the needs of the enterprise – and that too, at no additional cost andwith zero disruptions.
BigQuery stores all its data in Colossus in the capacitor format. The Capacitor does a number of optimizations under the hood without impacting the query performance or your bill. Capacitor takes an opinionated approach to storage. The background processes evaluate usage patterns continuously and automatically optimize the data sets to improve performance.
Worried about re-materialize your data if your tables are powered by multiple small files? BigQuery resolves this problem by automating this alleviating a major concern of analysts. Users also do not have to defrag, vacuum, or re-upload your datasets to BigQuery – it is completely automated and transparent.
Enterprise-grade data sharing
Owing to its serverless architecture, BigQuery also allows sharing of petabyte sized datasets with others as easily as you would share Google Spreadsheets.
BigQuery also does not use VMs for its storage layer. This alleviates concerns such as locking and hot spotting. It also allows enterprises to share data with other organizations easily without forcing them to create their individual clusters. No one pays for idle clusters.
Security and Dependability
BigQuery ably navigates the security and safety space as well.
Given the complexity of data, enterprises can never be careful enough about the safety and security features of their data warehouse. BigQuery employs custom-defined ACLs for controlling fine-grained data access.
Even in the event of extreme failure modes, the quality, availability, and durability of the data will not be compromised. This is because the data is replicated securely across multiple locations. Given the multiple layers of security employed in BigQuery, enterprises can be assured of data security without ever worrying about data lock-in.
It also encrypts all data, whether at rest or in motion by default. Since BigQuery is compliant with Google Cloud’s IAM policies it has been able to carve our high-granularity roles and controls for their users. It employs two general modes of authentication – OAuth (the 3-legged user-involved auth approach) and Service Accounts (headless through a secrets file). Additionally, BigQuery’s Audit Logs provides the paper trail of all the activity that happens inside it.
Competitive and flexible pricing
When it comes to pricing, things couldn’t be simpler with BigQuery. Pricing consists of two components – query processing and storage.
BigQuery understands that running data analysis does not essentially need operating a data center. Hence, you pay only for what you use. The cloud-native pay-per-query model and Enterprise-grade Flat Rate model are the two main pricing models. It also has a flat rate model where you pay a flat fee and all queries are free.
Enterprises gain full and complete visibility into usage and costs. They also don’t have to navigate any up-front risks and do not have any capital costs to mitigate. Google also gives enterprises the flexibility to terminate BigQuery services anytime, jump from one model to the next to suit their budgets and also completely remove their data any time.
Lower total cost of ownership
According to a report by independent analyst firm Enterprise Strategy Group (ESG),enterprises stand to gain a lot from BigQuery. The firm developed a three-year total-cost-of-ownership (TCO) model that compared the expected costs and benefits of upgrading from an on-premise data warehouse, migrating to a cloud-based solution such as AWS or migrating to Google’s BigQuery.
The report revealed that by moving to BigQuery, organizations could reduce their overall costs by 52% as compared to the on-premise deployment. The elimination of the hardware investment, its related operations, and maintenance costs contribute to this. Additionally, BigQuery’s underlying architecture, its decoupling, and processing capability and immense storage capacity also contributed to lowered cost of ownership.
Google’s BigQuery has been designed keeping the needs of the new-age enterprise in mind. At this present moment, there is hardly anyone who can offer the equivalent of what BigQuery does. So, if you want to give your data warehouse the 21st-century boost, now is the ideal time to get started…with BigQuery.