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BigQuery’s User-friendly SQL is Like a Cool Drink for Hot Summer

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Presenting three new BigQuery SQL launches – Powerful Analytics Features, Flexible Schema Handling, and New Geospatial Tools. Learn more about the BigQuery updates and latest announcements.

With summer just around the corner, things are really heating up. But you’re in luck because this month BigQuery is supplying a cooler full of ice cold refreshments with this release of user-friendly SQL capabilities. 

We are pleased to announce three categories of BigQuery user-friendly SQL launches: Powerful Analytics Features, Flexible Schema Handling, and New Geospatial Tools.

Powerful Analytics Features

These powerful SQL analytics features provide greater flexibility to analysts for organizing, filtering, and rendering data in BigQuery than ever before. You can enable spreadsheet-like functionality on summarized data using PIVOT and UNPIVOT and filter irrelevant data in analytic functions using QUALIFY.

Through this section, we will become familiar with these new features through examples using the BigQuery Public dataset, usa_names.

PIVOT/UNPIVOT (Preview)

One of the most time-consuming tasks for data analytics practitioners is wrangling data into the right shape. SQL is great for wrangling data, but sometimes you want to reformat a table as you would in a spreadsheet, pivoting rows and columns interchangeably. To support this use case, we are pleased to introduce PIVOT and UNPIVOT operators in BigQuery. PIVOT creates columns from unique values in rows by aggregating values, and UNPIVOT reverses this action.The example below uses PIVOT on bigquery-public-data.usa_names.usa_1910_current to show the number of males and females born each year, representing each gender as a column. Then UNPIVOT reverses this action.

Language: SQL

  -- we start with SQL to create a simple table
-- we only include gender, year, and number. 
CREATE TABLE
  mydataset.sampletable1 AS (
  SELECT
    Gender,Year,SUM(Number) AS Number
  FROM
    `bigquery-public-data.usa_names.usa_1910_current`
  WHERE
    Year >= 2017
  GROUP BY
    Gender, Year);
-- The resulting table:
--+----------------------------------------+
--|   Gender   |    Year    |    Number    |
--+----------------------------------------+
--|      F     |    2019    |    1353716   |
--|      F     |    2017    |    1403989   |
--|      F     |    2018    |    1380382   |
--|      M     |    2018    |    1568678   |
--|      M     |    2019    |    1538056   |
--|      M     |    2017    |    1604609   |
--+----------------------------------------+
-- use PIVOT to create columns for “female” and “male” 
CREATE TABLE
  mydataset.Pivoted AS
SELECT
  year, male, female
FROM
  mydataset.sampletable1 
PIVOT( SUM(Number) FOR gender IN ('M' AS male,
  'F' AS female))
ORDER BY
  year;
-- The resulting pivoted table:
--+----------------------------------------+
--|    Year    |   female   |     male     |
--+----------------------------------------+
--|    2017    |   1403989   |    1604609  |
--|    2018    |   1380382   |    1568678  |
--|    2019    |   1353716   |    1538056  |
--+----------------------------------------+
-- UNPIVOT reverses the row/column rotation of PIVOT.
SELECT
  *
FROM
  mydataset.Pivoted 
UNPIVOT(number FOR gender IN (male AS 'M',
  female AS 'F'));

QUALIFY (Preview)

More advanced users of SQL know the power of analytic functions (aka window functions). These functions compute values over a group of rows, returning a single result for each row. For example, customers use analytic functions to compute a grand total, subtotal, moving average, rank, and more. With the announcement of support for QUALIFY, BigQuery users can now filter on the results of analytic functions by using the QUALIFY clause. 

QUALIFY belongs in the family of query clauses used for filtering along with WHERE and HAVING. The WHERE clause is used to filter individual rows in a query. The HAVING clause is used to filter aggregate rows in a result set after aggregate functions and GROUP BY clauses. The QUALIFY clause is used to filter results of analytic functions. 

To show the utility of QUALIFY, the example below uses QUALIFY to return the top 3 female names from each year in the last decade using from bigquery-public-data.usa_names.usa_1910_current. 

Language: SQL

  -- QUALIFY filters the result of the RANK function
SELECT
  name,year,SUM(number) AS total,
  RANK() OVER (PARTITION BY year 
  ORDER BY SUM(number) DESC) AS rank
FROM
  `bigquery-public-data.usa_names.usa_1910_current`
WHERE
  gender = 'F'
  AND YEAR >= 2010
GROUP BY 1,2 
QUALIFY RANK <= 3
ORDER BY 2,4;

Flexible Schema Handling

New SQL for administrators and data engineers enables table renaming for data pipeline processes, as well as flexible column management.

Table Rename (GA)

In data pipeline processes, tables are often created and then renamed so that they can make way for the next iteration of the pipeline run. To accomplish this, customers need a mechanism by which they can create a table and then subsequently rename it. Now if customers want to change this name using SQL, they can. Using the simple syntax that ALTER TABLE RENAME TO provides, customers will be able to rename a table after creation to clear the way for the next iteration of tables in the data pipeline.

Language: SQL

  -- create a sample table “tablename” in “mydataset”. 
-- You will rename this table.
CREATE OR REPLACE TABLE dataset.tablename(
    col1 STRING, 
    col2 NUMERIC);
-- if this table “tablename” becomes obsolete
-- perform Table Rename to “obsoletetable”
ALTER TABLE
  mydataset.name RENAME TO obsoletetable;

DROP NOT NULL constraints on a column (GA)

While BigQuery has historically provided many tools available in the UI, CLI and APIs, we know that many administrators prefer interfacing with the database using SQL. BigQuery recently released DDL statements which enable data administrators to provision and manage datasets and tables, greatly simplifying provisioning and management. Today, we continue the next addition in this line of releases by announcing ALTER COLUMN DROP NOT NULL constraint on a column:

Language: SQL

  -- create a table to store credit card numbers
-- the business requires this field, so 
-- include a NOT NULL constraint 
CREATE TABLE
  mydataset.customers(credit_card_number STRING NOT NULL);
-- if needs of the business no longer require this field,
-- the customer can allow null entries in this column
-- by dropping the constraint
ALTER TABLE
  mydataset.customers 
ALTER COLUMN credit_card_number DROP NOT NULL;

CREATE VIEW with column list (GA)

Views are used ubiquitously by BigQuery customers to capture business logic. Oftentimes, BigQuery users have business requirements to assign aliases to columns in views. Now BigQuery supports doing so upon view creation in a column name list format with the release of CREATE VIEW with column list syntax.

Language: SQL

  -- aliases list1 and list2 can be assigned in a list format
CREATE VIEW
  myview (list1, list2) AS
SELECT
  column_1, column_2
FROM
  mydataset.exampletable

New Geospatial Tools

ST_POINTN, ST_STARTPOINT, and ST_ENDPOINT

Geospatial data is incredibly valuable to data analytics customers dealing with data from the physical world. BigQuery has very strong geospatial function support to help customers process marketing data, track storms, or manage self-driving cars. Particularly for analyzing vehicle or location tracking data, we’re thrilled to provide three new functions to allow users to easily extract or filter on key points:  

For example, when working with vehicle histories,  ST_POINTN, ST_STARTPOINT, and ST_ENDPOINT allow users to extract elements such as the start and the end of a trip. For identifying origin-destination pairs these functions will make that task much easier.

Language: SQL

  -- pull the first, second, penultimate and final points 
-- from a linestring
WITH linestring as (
    SELECT ST_GeogFromText('linestring(1 1, 2 1, 3 2, 3 3)') g
)
SELECT
  ST_StartPoint(g) AS first, ST_EndPoint(g) AS last,
  ST_PointN(g,2) AS second, ST_PointN(g, -2) as second_to_last
FROM
  linestring
+--------------+--------------+--------------+----------------+
| first        | last         | second       | second_to_last |
+--------------+--------------+--------------+----------------+
| POINT(1 1)   | POINT(3 3)   | POINT(2 1)   | POINT(3 2)     |
+--------------+--------------+--------------+----------------+

As sure as a hot summer day pairs well with an ice-cold beverage, these new user-friendly SQL features in BigQuery pair well with your data analytics workflows. To learn more about BigQuery, visit our website, and get started immediately with the free BigQuery Sandbox.

Blog

Towards The Next Wave of Google Cloud Infrastructure Innovation: New C3 VM and Hyperdisk

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Google Cloud redefines cloud infrastructure with the cutting-edge C3 VMs and Hyperdisk storage, accelerating performance, improving efficiency and paving the way for automation in cloud management. Read more...

Meeting the rapidly growing demands of our customers’ high performance computing and data-intensive workloads requires deep innovation — at Google Cloud, we know we can’t rely on ever-faster CPUs alone, like Moore’s Law has enabled in the past. Customers can either optimize their workloads for a given platform, or we can offer them a platform that is optimized for their specific needs. At Google Cloud, we choose the latter.

Today, we have an exciting new release resulting from these efforts: the new C3 machine series powered by the 4th Gen Intel Xeon Scalable processor and Google’s custom Intel Infrastructure Processing Unit (IPU). Along with the recently announced Hyperdisk block storage which offers 80% higher IOPS per vCPU for high-end database management system (DBMS) workloads when compared to other hyperscalers. C3 machine instances can deliver strong performance gains to enable high performance computing and data-intensive workloads. Customers such as Snap, for example, have seen approximately a 20% increase in performance for a key workload over the previous generation C2.

The C3 machine series is just the latest example of this architectural approach. For over two decades, Google has purpose-built and designed some of the world’s most efficient and scalable computing systems to meet the needs of our customers. We built the Tensor Processing Unit (TPU) to power real-time voice search, photo object recognition, and interactive language translation; unveiled Titan, a secure, low-power microcontroller to help ensure that every machine boots from a trusted state; and launched Video Coding Units (VCUs) to enable video distribution that addresses a range of formats and client requirements. We engineer golden paths from silicon to the console, using a combination of purpose-built infrastructure, prescriptive architectures, and an open ecosystem to deliver what we term workload-optimized infrastructure.

Getting to know the C3 machine series

The Compute Engine C3 machine series, now available in Private Preview, is the first VM in the public cloud with the 4th Gen Intel Xeon Scalable processor and with Google’s custom Intel IPU. C3 machine instances use offload hardware for more predictable and efficient compute, high-performance storage, and a programmable packet processing capability for low latency and accelerated, secure networking.

“We are pleased to have co-designed the first ASIC Infrastructure Processing Unit with Google Cloud, which has now launched in the new C3 machine series. A first of its kind in any public cloud, C3 VMs will run workloads on 4th Gen Intel Xeon Scalable processors while they free up programmable packet processing to the IPUs securely at line rates of 200Gb/s. This Intel and Google collaboration enables customers through infrastructure that is more secure, flexible, and performant.” – Nick McKeown, Senior Vice President, Intel Fellow and General Manager of Network and Edge Group

The System on a Chip hardware architecture introduced in C3 VMs can enable better security, isolation, and performance. In the future, this purpose-built architecture will also allow us to offer a richer product portfolio, such as support for native bare-metal instances.

Hyperdisk block storage and 200 Gbps networking

Block storage and VMs go hand in hand. Last month, we announced the Preview release of Hyperdisk, our next-generation block storage. The new architecture decouples compute instance sizing from storage performance to deliver 80% higher IOPS per vCPU than other leading hyperscale cloud provider. And compared with the previous generation C2, C3 VMs with Hyperdisk deliver 4x higher throughput and 10x higher IOPS. Now, you don’t have to choose expensive, larger compute instances just to get the storage performance you need for data workloads such as Hadoop and Microsoft SQL Server.

To enable high performance computing workloads, C3 VMs also feature 200 Gbps low-latency networking powered by our custom IPU, as well as line-rate encryption using the open source PSP protocol.

What our customers and partners are saying


“We were pleased to observe a 20% increase in performance over the current generation C2 VMs from Google Cloud in testing with one of our key workloads. These continued performance improvements enable better end user experience and application cost efficiency.” – Aaron Sheldon, Sr. Software Engineer, Snap Inc.

“Based on the initial performance data, running weather research and forecasting (WRF) on C3 clusters can deliver as much as 10x quicker time to results for about the same computational cost. This will significantly accelerate R&D for our customers in weather, environment, and engineering domains.” — Michael Wilde, CEO, Parallel Works Inc.

“In early testing with our flagship products, including Ansys Fluent, Mechanical and LS-DYNA, on the new Google Cloud C3 VM, we’re seeing up to 3x performance gains over C2 VMs due to higher memory bandwidth and lower network latency.” – Shane Emswiler, Senior Vice President of Products, Ansys

Where we are headed

With the exponential rise in the complexity of cloud infrastructure, we as an industry must turn to automation to manage these platforms efficiently at scale. Along with Infrastructure as Code, custom chips like the Titan, the TPU and the IPU, pave the way for a not-so-distant future where we’ll automate over half of all infrastructure decisions, configuring systems dynamically in response to usage patterns. At Google Cloud, we are committed to continuing our long history of hardware innovation with a focus on workload optimization and automation.

To learn more about C3 VMs and Hyperdisk, check out our session at NEXT ‘22, How Google Cloud optimizes infrastructure for your workloads. To request access to the C3 VMs or Hyperdisk, please reach out to your sales representative or account manager.

Case Study

How a Mid-Sized Firm is Shrinking Inventory Carryovers 50% with AI

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In addition, the 25-man manufacturing company is able to compete with much larger e-commerce retailers by being able to improve the accuracy of demand planning, make quicker decisions, and make better pricing decisions to optimize profitability--all thanks to AI.

For more than 10 years, NMK Textile Mills has manufactured bed linens for major retailers in the United States and Canada. As e-commerce exploded, the company’s co-founder saw an opportunity to grow the business. So, in addition to manufacturing bed linens wholesale for retail customers, NMK Textile Mills reworked its complete supply chain to manufacture products for California Design Den, which became an e-commerce retailer selling its own fashion-forward products directly to consumers online.

With California Design Den’s push into e-commerce, it became apparent the SMB company (with about 250 global employees) faced the same tough supply chain questions as large retail customers, including maintaining enough inventory to meet customer demand in a timely, efficient way.

California Design Den depended upon a myriad of systems to track its complex forecasting and reordering processes. Team members typically planned inventory manually using desktop spreadsheet software, which could lead to excess inventory. Accurately forecasting demand and supply was essential to the company’s financial success—but it was also a challenge.

A couple years ago, California Design Den partnered with Pluto7, a technology solutions provider that offers a software as a service (SaaS) called Planning In A Box. Leveraging Google Cloud Platform machine learning and artificial intelligence, Planning In A Box intelligently helps predict demand and balances it with supply.

But that was just the beginning. “Along the way, we realized that to compete with larger retailers, make quicker decisions, and move faster, we needed to go further,” says Deepak Mehrotra, Co-founder and Chief Adventurer at California Design Den.

With guidance from Pluto7, California Design Den began migrating its database to Google Cloud Platform. Using Google BigQuery, Google Compute Engine, Google Cloud SQL, and Google Cloud Storage, and experimenting with Google Cloud Vision and Google Cloud AutoML, the company is reducing inventory carryovers by more than 50%, improving the accuracy of demand planning quarter over quarter, and gaining granular insights into how individual SKUs are performing.

“We would need an army of data scientists to make faster decisions on pricing and inventory levels. With Google Cloud Platform machine learning and artificial intelligence, we don’t need that. We can make much faster pricing decisions to optimize profitability and move inventory.”

—Deepak Mehrotra, Co-founder and Chief Adventurer, California Design Den

No need for army of data scientists

“Using Google Cloud Platform machine learning and AI was essential for California Design Den if it was to compete successfully with larger retailers,” Deepak says.

For example, tastes and fashions in bed linens can change quickly and consumer prices fluctuate. With more than 2,500 SKUs, it wasn’t possible for California Design Den’s team to continuously monitor product demand and experiment with competitive pricing in real time.

“We would need an army of data scientists to make faster decisions on pricing and inventory levels,” says Deepak. “With Google Cloud Platform machine learning and artificial intelligence, we don’t need that. We can make much faster pricing decisions to optimize profitability and move inventory.”

By integrating all its data onto Google Cloud Platform, California Design Den’s team gains deeper insights into product sales over time, which in turn helps the company improve demand planning by better determining which styles to manufacture and sell in the future.

“When experienced employees leave, their knowledge leaves with them. By having all data in one place, and with machine learning and AI, California Design Den can go back in its history, look at products made or sold years ago, and analyze product performance.”

—Deepak Mehrotra, Co-founder and Chief Adventurer, California Design Den

Merging visuals with data

Before Google Cloud Platform, team members had to dig through spreadsheets and run scenarios to get a sense of how particular products had sold. The next step was to perform keyword searches across the company’s photo library in the cloud to find each product’s image. From there, a team member would insert the product images into a presentation, along with relevant data points, to provide a report for stakeholders on how particular styles performed.

Today, California Design Den, with the help of Pluto7, is integrating its entire product image library with its database on Google Cloud Platform. Experimenting with Google Cloud Vision and Google Cloud AutoML, California Design Den is moving towards a day when team members can run sales scenarios and get deep background data on individual product performance while viewing images of the relevant products.

Merging product visuals with data will help designers and team members better understand sales patterns over time and in context. In the past, making correlations between things like which sheet colors sold well in California, compared to how the same sheet colors performed on the East Coast, was something that California Design Den employees primarily did in their heads.

“When experienced employees leave, their knowledge goes with them,” says Manjunath Devadas, Founder and CEO at Pluto7. “By having all data in one place, and with machine learning and AI, California Design Den can go back in its history, look at products made or sold years ago, and analyze product performance.”

“We are literally growing the complexity of our business on all levels, including designing, manufacturing, selling, reordering, inventory holding—everything.”

—Deepak Mehrotra, Co-founder and Chief Adventurer, California Design Den

Reimagining supply-demand balancing

Pluto7’s mission statement is to democratize supply demand balancing with machine learning and AI. California Design Den is a case in point, as the combination of Planning In A Box and Google Cloud Platform gives the company greater control over its destiny.

“Big retailers used to tell us what to manufacture and how much they would pay for it,” says Deepak. “That was our primary business, and if we didn’t accept the terms, a competitor would.” Today, in addition to continuing to make products for retailers, California Design Den can design, make, and sell a variety of designs for itself, including custom and limited-edition products, thanks to Google Cloud Platform and Pluto7 software offerings. The payoff is not only in having a more diversified business. California Design Den also receives more favorable profit margins by selling its own products.

“We are literally growing the complexity of our business on all levels, including designing, manufacturing, selling, reordering, inventory holding—everything,” Deepak says. “We can make smaller batches. We can connect directly with consumers. We can identify the missing pieces—what should we produce next, when, and how much? We otherwise couldn’t afford the level of talent it would take to do this.”

Cutting through the noise

Google and Pluto7 software helped California Design Den reduce inventory carryovers by more than 50%. Inventory tracking and distribution, along with insights and visibility into product sales, are faster, more efficient, and accurate. Google Cloud Platform flexible pricing, speed, reliability, security, and scalability enable California Design Den to stay relevant and be more competitive.

In addition to benefiting from Google Cloud Platform, California Design Den relies on G Suite—also part of Google Cloud—to enhance collaboration among its global teams. Previously, the company’s email server would sometimes crash, due to the heavy load of sharing product photos and other data. “Gmail and Google Drive handle the everyday demands on the business effortlessly and reliably,” Deepak says.

“Google machine learning and AI enable us to cut through all the noise from raw data, so we can see what’s important. We can focus on analytics to guide us to success today and in the future.”

—Deepak Mehrotra, Co-founder and Chief Adventurer, California Design Den

The company is exploring additional ways to leverage Google Cloud Platform in the near future. For example, one possibility is to import customer reviews from sites where products are sold into Google BigQuery, and to use that data to perform sentiment analysis via Google Cloud Natural Language. It could provide another valuable data source to help California Design Den’s team decide where to focus future designs.

“Google machine learning and AI enable us to cut through all the noise from raw data, so we can see what’s important,” Deepak says. “We can focus on analytics to guide us to success today and in the future.”

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Google Cloud’s Autism Career Program to Nurture Neurodiverse Talent

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About 2% of the population has autism and these findings could be confounding as many individuals go undiagnosed. Of the diagnosed individuals, only 29 per cent have stable employment. To close this gap, Google Cloud introduces Autism Career Program.

My passion for neurodiversity began 10 years ago, when I became involved with Els for Autism, an organization that works with children and adults who have autism, as well as their families. At the time, I had a friend who was struggling to find resources for his son with autism. The foundation promotes acceptance and inclusion for people on the spectrum, helping them live independently and find jobs that harness their talents and skills. The organization’s focus on autism in the workplace resonated deeply with me, due to the rich experiences I had working with individuals with autism over the course of my career.

Approximately two percent of the population has autism, but it’s estimated this number is actually quite low as many individuals go undiagnosed. Of those that have been diagnosed, only 29% have had any sort of paid work in their lives. Personally, I find this tragic, because individuals with autism can be highly-functioning and contributing professionals in any organization. Too often, though, the interview process can pose challenges due to unconscious bias from a hiring manager or interviewer, for example, if the candidate doesn’t look an interviewer in the eyes or asks for additional time to complete a test. This bias often unintentionally marginalizes great candidates and means businesses miss out on valuable talent who can contribute and enrich the workplace. 

Introducing Google Cloud’s Autism Career Program 

It is in that spirit that I am excited to announce the launch of Google Cloud’s Autism Career Program, designed to hire and support more talented people with autism in the rapidly growing cloud industry.

We are working with experts from the Stanford Neurodiversity Project (part of the Stanford University School of Medicine), which provides consultation services to employers to advise on opportunities and success metrics for neurodiverse individuals in the workplace.

One key pillar of our program is to train up to 500 Google Cloud managers and others who are involved in hiring processes. Our goal is to empower these Googlers to work effectively and empathetically with autistic candidates and ensure Google’s onboarding processes are accessible and equitable. Stanford will also provide coaching to applicants, as well as ongoing support for them, their teammates and managers once they join the Google Cloud team.

We’re taking this approach to break down the barriers that candidates with autism most often face. In addition to bias, there may be challenges with how interviews are structured or conducted without the right tools. For these reasons, we will offer candidates in this program reasonable accommodations like extended interview time, providing questions in advance, or conducting the interview in writing in a Google Doc rather than verbally on a call. These accommodations don’t give those candidates an unfair advantage. It’s just the opposite: They remove an unfair disadvantage so candidates have a fair and equitable chance to compete for the job.

This program is just one example of Google Cloud’s commitment to inclusion, and it is an important step forward to building a more representative team and creating value for customers and stakeholders.

How-to

Cloud FinOps: Maximizing Business Value and Optimizing Cloud Spend

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Discover how Cloud FinOps can maximize business value and optimize cloud spend. Explore the five building blocks and access valuable resources to embark on your FinOps journey.

We’ve been saying it for years, the benefits and potential of the cloud abound. 

And yet, more than 80% of respondents in a survey of 753 business leaders point to managing cloud spend as their top organizational challenge, and these same respondents estimate that nearly 1/3 of their cloud spend is inefficient or wasted (Flexera, 2023). Many organizations are new to optimizing cloud costs and ensuring resources are used efficiently.

As your organization digitally transforms you may be realizing what other organizations are realizing too: When it comes to business value, simply migrating to the cloud isn’t enough. Achieving the full benefits of cloud requires fundamental changes to both mindset and behaviors around existing financial-management practices. It requires changing the way your disparate teams work together. 

Enter the Cloud FinOps Building Blocks

https://storage.googleapis.com/gweb-cloudblog-publish/images/1_Cloud_FinOps_Building_Blocks.max-2000x2000.jpg

Cloud FinOps is a framework, discipline, and cultural shift combining people, processes, and technology to drive financial awareness and accountability. FinOps practices align engineering, finance, technology and business leaders and teams under a primary objective: to maximize business value from the cloud. With Cloud FinOps practices, every business stakeholder is charged not only to take responsibility for their spending and costs, but also to optimize them. These practices enable businesses to manage consumption and make sound, data-informed cloud-spend decisions. Cloud FinOps is comprised of five building blocks:

  1. Accountability and enablement
    Establishing governance and policies to manage cloud spend and realize business value.
  2. Measurement and realization
    Driving financial accountability and value realization with a defined set of KPIs and success metrics.
  3. Cost optimization
    Providing financial visibility and recommendations of IT resource usage to optimize cloud spend.
  4. Planning and forecasting
    Modernizing budgeting, forecasting, and chargeback methods to allow for iterative, innovative and cost effective development practices.
  5. Tools and accelerators
    Deploying and integrating a set of cloud cost tooling to effectively manage and track cloud spend. Learn more here. 

For a general overview of the Cloud FinOps framework and more on the five building blocks, check out these resources:

Importantly, Cloud FinOps isn’t about saving money; it’s about making money. It’s about promoting a cost-conscious culture, financial accountability, and business agility in the cloud. Whatever stage of the cloud journey you’re at, cloud FinOps practices will help you get the most value out of Google Cloud. This framework can help to remove blockers, implement the building blocks, and empower your teams to make better business decisions. 

The Cloud FinOps Journey 

Implementing Cloud FinOps is neither a destination nor a box your organization will check then archive. Rather, Cloud FinOps is an ongoing journey and discipline. It’s inherently iterative. As such, growth and maturity across processes, capabilities, and domains requires action, repetition, and continuous learning. 

Across the five FinOps building blocks, we’ve identified 50 subprocesses to best understand organizations’ FinOps proficiency, capabilities, practice domains, and blind spots. We scale them from 1 to 5 and categorize them in one of three phases of maturity: Crawl, Walk, or Run. Organizations in the Crawl phase tend to focus on technical problem solving and cloud-cost visibility. Organizations in the Walk phase emphasize strategic improvements such as employing cost visibility dashboards to realize better business value. And organizations in the Run phase are focused primarily on transformational change and strategic innovation, factoring cost considerations into both processes and cloud architecture. 

Through this “crawl, walk, run” maturity model, we can evaluate proficiency, establish a benchmark, and recommend a targeted action plan for FinOps adoption. And whatever your level of maturity, your organization can take quick scalable action not only to foster improvement but also to evaluate outcomes and gain insights. 

The key here is that regardless of your organization’s Cloud FinOps maturity level, you can take small steps now toward continuous improvement. Here are some common focus areas and several more resources organized by maturity level that you can access.

https://storage.googleapis.com/gweb-cloudblog-publish/images/2_Cloud_FinOps_Building_Blocks.max-1100x1100.png

Crawl phase 

Improve cloud-cost visibility. 

  • Whitepaper | Drive Cloud FinOps at scale with Google Cloud Tagging
    Tags and labels can be useful and flexible tools to help your organization segment cloud spend and allocate costs. This whitepaper introduces Google Cloud Tags and best practices for implementing them. It differentiates tags, which offer reliable reporting and governance features, from labels, which can be prone to problems, including poor coverage and a lack of integrity in data labeling. 
  • Whitepaper | Unlocking the value of Cloud FinOps with a new operating model
    This white paper unpacks the details of the FinOps operating model, including roles, organizational alignment, and driving culture change. It details how to establish strong financial governance and a cost-conscious culture. 
  • Whitepaper | Cloud FinOps: Shared services cost allocation
    In this whitepaper, you’ll explore the elements of cost allocation as well as the complexities and challenges associated with shared-services cost allocation. While some of these concepts and models are interchangeable between legacy and cloud environments, this whitepaper focuses primarily on cloud computing and associated services.

Walk phase

Improve business-value realization. 

  • Blog | 5 key metrics to measure Cloud FinOps impact in your organization in 2022 and beyond
    To drive business growth and topline revenue, business leaders must be able to connect cloud investments to business outcomes. As such, traditional IT metrics and KPIs must continue to evolve. In this blogpost, we’ll explore five key business-value metrics aligned to the five Cloud FinOps building blocks. 
  • Whitepaper | Maximize business value with Cloud FinOps
    The cloud introduces new complexity and challenges to traditional IT financial management. As such, it requires strategic financial governance, processes, and partnership across the organization. This whitepaper explains how Cloud FinOps helps enterprises that have invested in cloud to drive financial accountability and accelerate business value.

Run phase 

Improve strategic cloud innovation. 

  • Whitepaper | Unit costing: The next frontier in cloud
    In this whitepaper, you’ll explore the nature of and need for cloud unit costing, the standard by which FinOps practitioners obtain full business context for their cloud costs. It features examples from cloud-first organizations that have pioneered FinOps practices. Additionally, it examines several cloud forecasting and budgeting methods, ranging from least to most rigorous. 
  • Blog | You get what you pay for: Principles for designing a chargeback process
    Chargeback, a crucial Cloud FinOps capability, is the process of mapping cloud consumption to internal users within an organization. It provides transparency, facilitates accountability,  enables recovery of cloud costs, and fosters a culture of fiscal responsibility. This blogpost will walk you through some best practices in designing an effective chargeback process in Google Cloud. 

Success with Cloud FinOps

As global markets continue to face challenges, there’s never been a better time to increase the return on your cloud investments. Adopting and implementing FinOps practices will help. For some real-world examples of how organizations across a range of FinOps maturity levels have collectively saved millions of dollars on their overall cloud spend, check out these customers’ stories. 

  • Video | Next 2022: Top 10 ways to lower your costs on Google Cloud with General Mills
    In this video, which highlights ten leading cloud cost optimization practices, hear how General Mills, which is on pace to increase their cloud footprint by 60%, has approached the discipline of cost savings and accelerated their adoption of Cloud FinOps to drive waste out of their cloud usage. 
  • Video | How Nuro optimized their costs on Google Cloud
    In this video, you’ll get an overview of the Google Cloud FinOps framework, a deep dive on cost-optimization best practices, and hear about how startup Nuro AI has adopted their own cost-savings discipline and Cloud FinOps practice. 
  • Video | How OpenX reduce per unit costs by 60%
    In this video, you’ll learn how to establish a cost center of excellence within your cloud practice, explore several cost-optimization recommendations, and hear from OpenX about how they reduced their costs on Google Cloud. 
  • Case Study | How Sky saved millions with Google Cloud
    In this case study, read how a few years into their cloud adoption journey, media and entertainment company, Sky Group discovered over $1.5 million in savings and optimized costs with BigQuery, Compute Engine, and Cloud Storage. 
  • Case Study | Etsy: Doing more with less cost and infrastructure
    In this case study, read how after migrating their data center and ecommerce platform to the cloud, Etsy realized more than 50% savings in compute energy and leveraged committed use discounts (CUDs) to reduce their compute costs by 42%. 

It’s important to remember that FinOps success looks different for different organizations. It’s neither a one-time fix nor a destination reached by way of a single path. But for every organization, success requires small actions, refinement, and continuous improvement. As you leverage Google Cloud FinOps resources and tools, your organization can:

  • Drive financial accountability and visibility.
  • Optimize cloud usage and cost efficiency.
  • Enable cross organizational trust and collaboration.
  • Prevent cloud-spend sprawl.
  • Break down departmental silos.
  • Accelerate innovation. 

Getting started with Cloud FinOps

At Google, we have a team of experts in leading FinOps practices dedicated to helping you create an actionable plan to optimize cloud spend and drive cost efficiency. We’ve created numerous resources to help you get started from any stage in the FinOps journey. 

Whitepaper | Maximize Business Value with Cloud FinOps
This whitepaper outlines steps to help your organization implement FinOps. It details required teams and processes as well as the optimal behaviors, approaches, and outcomes to help maximize your investment on Google Cloud. 

With Google Cloud FinOps, your organization can also accelerate business value in the cloud. To find out more, join us on the Google Cloud Twitter channel twice a month for open Twitter Spaces discussions or reach out to your Google Cloud Sales Representative for a 1:1 discussion.

Case Study

How the Telegraph is Reimagining Media with Google Cloud

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The Telegraph is the biggest-selling quality newspaper in the UK, an accolade which requires it to print and distribute hundreds of thousands of copies each day. Optimal management of print runs is important, and by using a combination of the cloud and machine learning, The Telegraph is better able to predict demand for physical newspapers, maximizing sales and minimizing waste.

Whether they’re reading the newspaper on the way to work, or catching up on the latest headlines on their smartphones, readers expect up-to-the-minute news wherever and whenever makes the most sense for them. As a result, media companies are increasingly looking for ways to improve, expand, and simplify their offerings, and they’re increasingly looking to the cloud to do it.

For more than 160 years The Telegraph has been counted on by readers across the United Kingdom and globally for award-winning news and journalism. An early adopter of cloud technology, it’s been a G Suite customer since 2008 and has already been using Google Cloud Platform to analyze digital behaviors to improve engagement and advertising performance since 2016.

Recently, The Telegraph announced it’s migrating fully to Google Cloud. By migrating all their production and pre-production services, they aim to deliver content faster, provide compelling experiences to readers, and reduce environmental impact.

“We are delighted to announce our newest collaboration with Google Cloud,” said Chris Taylor, Chief Information Officer, The Telegraph. “We have always worked closely with Google as they help us to provide our readers with great experiences on our digital products, collaboration software and internet scale through search. Their continued leadership in projects such as Kubernetes are enabling us to build flexible development environments that truly support DevOps.”

Powering the Digital Publishing Ecosystem

The Telegraph produces large volumes of digital content every day. It was imperative for them to find a cloud provider they could trust to support this ecosystem. By working with Google Cloud they have changed the way they see and engage with data: they can collect new information about their products every second and use that to continually hone their strategy. The Telegraph are placing more confidence and trust in the data captured about their content and now have one of the best available pieces of technology for capturing and analyzing the stories they publish in real-time.

Leveraging AI to support journalists

Time is critical when journalists are on a story, and The Telegraph wants to put important data in the hands of its journalists right when they need it. To do this, it will be using AutoML to classify content for journalists and make it more discoverable. For example, a reporter will be able to bring up relevant assets that link to their stories. It will also apply AutoML to classify Telegraph stock photos to help journalists attach compelling visual content to their stories faster.

Building compelling reader experiences with the help of APIs

Readers have an ever-increasing expectation of personalization. To meet this need, The Telegraph launched My Telegraph, currently live in beta, to offer registered readers personalized news experiences based on their interests or the particular journalists they want to follow. My Telegraph was developed on an API management platform provided by Google Cloud’s Apigee. You can learn more about how it’s applying API management to My Telegraph, in this blog post.

Working for environmental good

The Telegraph is the biggest selling quality newspaper in the UK, an accolade which requires it to print and distribute hundreds of thousands of copies each day. Optimal management of print production is important, and by using a combination of the cloud and machine learning, The Telegraph is better able to predict demand for physical newspapers, maximizing sales and minimizing waste. This makes great business sense for The Telegraph but also has great environmental benefit.

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“Google Cloud enables us to be more client focused and deliver better features faster.” —Sebastian Hentzschel, Chief Information Officer, BMG

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Scope for Tech Adoption and Advancements in Healthcare are Still High: Google Cloud Research

Since the start of the COVID-19 pandemic, there’s been a rapid acceleration of digital transformation across the entire healthcare industry. Telehealth has become a more mainstream and safe way for patients and caregivers to connect. Machine learning modeling has helped speed up innovation and drug discovery. And new levels of

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