Data Cloud Skills to Pick Up in 2022: Google Experts Recommended
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It’s 2022 and nanosatellites, NFTs, and autonomous cars that deliver your pizza are in full force. In a world where people rely on simple technology to untangle complex problems, companies must deliver simple experiences to be successful in today’s landscape. For many cloud providers this means enabling tightly integrated data offerings that simplify the data delivery process without losing sight of the sophisticated needs of the modern data consumer.
But while the name of the game is helping companies reach informed decisions from their data simpler and faster, what about the data practitioners – data analysts, data engineers, database administrators, developers, etc – who use these cloud data tools and technologies everyday? To proactively stay ahead of data cloud market trends in 2022 should data practitioners invest their time in specializing their data cloud skill sets (e.g. go deep in, say, data pipelining skills) or instead invest their time generalizing their data cloud skill sets (e.g. growing proficiencies in a mix of data analytics, databases, AI/ML, and more domains)?
Skill deep or wide with data – that is the question
For Abdul Razack, VP, Solutions Engineering, Technology Solutions and Strategy at Google Cloud, the answer is a bit of both.
“Data practitioners need to be broad in terms of their technology skills, but specialized with respect to the domain or domains in which they apply them. The reason why is because many things that used to be separate skill sets are now converging – like business analytics, streaming, machine learning, data pipelines, and data warehousing. Data practitioners need to be able to implement end-to-end workflows that solve specific business problems using skills from each category.”
It’s true, thousands of customers are choosing Google’s data cloud because it offers a unified and open approach to cloud that enables their practitioners to break down silos, begin and end projects without leaving the data platform, and innovate faster across their organization.
The data practitioners who mirror Google data cloud’s frame of mind of being smart and agile across data domains in their skilling and learning will reap the benefits of solving more nuanced problems – building out internet-scale applications, fine tuning smart processes with analytics and AI, constructing data meshes that make product building simple, etc – at a larger scale than they would if they specialized in just one or two areas alone.
“Of course at the end of the day it depends on what tools a data practitioner is using to complete their workflows. There’s only so much you can learn and skills you can develop when you’re using limited tools. Growing data proficiencies across the board is made a lot easier when you’re using a data platform like BigQuery to address all these needs. BigQuery eliminates the choices you have to make – for instance you don’t have to choose between streaming data and data at rest, batch and realtime, or business intelligence and data science. This freedom gives data professionals a huge advantage when they’re building their skill sets and taking on more complex projects.” -Abdul Razack – VP, Solutions Engineering, Technology Solutions and Strategy, Google Cloud
Knowing your value is half the battle when upskilling
While some experts think technology is the limiting factor of whether or not you can even go wide or go deep in the first place, others like Google Cloud’s Head of Data and Analytics Bruno Aziza purport that it also depends on who you are, who you wish to be, and what investments your company is making to ensure you can become that person.
“If you wish to set yourself up to be a Chief Data Officer, then you’ll want to understand how technologies fit together across your data estate first” said Aziza. “Only after you feel like you’re the go-to ‘data person’ can you then decide which part of the technology stack you want to double-down on.”
But technology isn’t everything. Aziza notes, “Make sure you focus on the business impact that your data work provides. You want to spend as much time as you can with your business counterparts to understand their business goals and challenges. The Harvard Business Review provides great guidance on how to succeed as a Chief Data Officer.”
Even if you don’t have your sights set on a C-suite role, both Aziza and Razack contend that the number one skill data practitioners should tackle in 2022 is actually a broad and perhaps abstract one: develop and exercise the curiosity to solve problems with a data-driven strategy.
That is, today’s data practitioners should always be interested in educating themselves in the industry and continually upskilling in something. And their employers should also be invested in helping practitioners develop those interests, most likely through exposure to learning materials, engaging in career conversations, subsidized courses, or incentives attached to pursuing a new certification or skill.
“Every industry is going through a digital transformation and the ability to identify what data to collect, how to prepare the data, and how to derive insights from it is critical. Therefore, the ability to find business challenges and formulate a data-driven approach to address those problems is the most important skill to have.” Abdul Razack – VP, Solutions Engineering, Technology Solutions and Strategy, Google Cloud.
Take the example of the “Data Mesh” I just wrote about in VentureBeat. You’ll find 3 types of attitudes towards this new concept. There are Disciples who encourage continued learning only from the source – like the author of a new book or the creator of a theory. There are Distractors who tell you that new skills, trends, and technologies are fake news. And there are Distorters like vendors who will sell you one easy fix solution. But it’s the data practitioner who needs to proceed with caution when interacting with all three types and forge their own path to discovering the truth when they’re learning and building skills. And for better or worse, this comes with trial and error, experimentation, and an eagerness to grow relative to where they began.”
Ready to start data upskilling? Start here.
For those interested in keeping up their data curiosities, check out our Data Journeys video series. Each week Bruno Aziza investigates a new authentic customer’s data journey – from migrating to cloud or building a data platform to carrying out new data for good initiatives. Learn how they did it, their data dos and don’ts, and what’s next for them on their journey. These videos include a flavor of both specializing your data competencies and broadening your data competencies.
For those interested in deepskilling, connect with Google’s data community at our upcoming virtual event: Latest Google Cloud data analytics innovations. Register and save your spot now to get your data questions answered live by GCP’s top data leaders and watch demos from our latest products and features including BigQuery, Dataproc, Dataplex, Dataflow, and more.
If you have any questions or need support along your learning journey – we’re here for you! Sign up to be a Google Cloud Innovator, and join the Google Cloud Data Analytics Community.
Two Ad Agencies Leverage BigQuery to Support Next-gen Ad Campaigns
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Advertising agencies are faced with the challenge of providing the precision data that marketers require to make better decisions at a time when customers’ digital footprints are rapidly changing. They need to transform customer information and real-time data into actionable insights to inform clients what to execute to ensure the highest campaign performance.
In this post, we’ll explore how two of our advertising agency customers are turning to Google BigQuery to innovate, succeed, and meet the next generation of digital advertising head on.
Net Conversion eliminated legacy toil to reach new heights
Paid marketing and comprehensive analytics agency Net Conversion has made a name for itself with its relentless attitude and data-driven mindset. But like many agencies, Net Conversion felt limited by traditional data management and reporting practices.
A few years ago, Net Conversion was still using legacy data servers to mine and process data across the organization, and analysts relied heavily on Microsoft Excel spreadsheets to generate reports. The process was lengthy, fragmented, and slow—especially when spreadsheets exceeded the million-row limit.
To transform, Net Conversion built Conversionomics, a serverless platform that leverages BigQuery, Google Cloud’s enterprise data warehouse, to centralize all of its data and handle all of its data transformation and ETL processes. BigQuery was selected for its serverless architecture, high scalability, and integration with tools that analysts were already using daily, such as Google Ads, Google Analytics, and Data Hub. After moving to BigQuery, Net Conversion discovered surprising benefits that streamlined reporting processes beyond initial expectations. For instance, many analysts had started using Google Sheets for reports, and BigQuery’s native integration with Connected Sheets gave them the power to analyze billions of rows of data and generate visualizations right where they were already working.If you’re still sending Excel files that are larger than 1MB, you should explore Google Cloud.
Kenneth Eisinger
Manager of Paid Media Analytics at Net Conversion
Since modernizing their data analytics stack, Net Conversion has saved countless hours of time that can now be spent on taking insights to the next level. Plus, BigQuery’s advanced data analytics capabilities and robust integrations have opened up new roads to offer more dynamic insights that help clients better understand their audience.
For instance, Net Conversion recently helped a large grocery retailer launch a more targeted campaign that significantly increased downloads of their mobile application. The agency was able to better understand and predict their customers’ needs by analyzing buyer behavior across the website, mobile application, and their purchase history. Net Conversion analyzed website data in real-time with BigQuery, ran analytics on their mobile app data through the Firebase’s integration with BigQuery, and enriched these insights with sales information from the grocery retailer’s CRM to generate propensity behavior models that accurately predicted which customers would most likely install their mobile app.
WITHIN helped companies weather the COVID storm
WITHIN is a performance branding company, focused on helping brands maximize growth by fusing marketing and business goals together in a single funnel. During the COVID-19 health crisis, WITHIN became an innovator in the ad agency world by sharing real-time trends and insights with customers through its Marketing Pulse Dashboard. This dashboard was part of the company’s path to adopting BigQuery for data analytics transformation.
Prior to using BigQuery, WITHIN used a PostgreSQL database to house its data and manual reporting. Not only was the team responsible for managing and maintaining the server, which took focus away from the data analytics, but query latency issues often slowed them down.
BigQuery’s serverless architecture, blazing-fast compute, and rich ecosystem of integrations with other Google Cloud and partner solutions made it possible to rapidly query, automate reporting, and completely get rid of CSV files.
Using BigQuery, WITHIN is able to run Customer Lifetime Value (LTV) analytics and quickly share the insights with their clients in a collaborative Google Sheet. In order to improve the effectiveness of their campaigns across their marketing channels, WITHIN further segments the data into high and low LTV cohorts and shares the predictive insights with their clients for in-platform optimizations.
By distilling these types of LTV insights from BigQuery, WITHIN has been able to use those to empower their campaigns on Google Ads with a few notable success stories.
- WITHIN worked with a pet food company to analyze historical transactional data to model predicted LTV of new customers. They found significant differences between product category and autoship vs single order customers, and they implemented LTV-based optimization. As a result, they saw a 400% increase in average customer LTV.
- WITHIN helped a coffee brand increase their customer base by 560%, with the projected 12-month LTV of newly acquired customers jumping a staggering 1280%.
Through integration with Google AI Platform Notebooks, BigQuery also advanced WITHIN’s ability to use machine learning (ML) models. Today, the team can build and deploy models to predict dedicated campaign impact across channels without moving the data. The integration of clients’ LTV data through Google Ads has also impacted how WITHIN structures their clients’ accounts and how they make performance optimization decisions.
Now, WITHIN can capitalize on the entire data lifecycle: ingesting data from multiple sources into BigQuery, running data analytics, and empowering people with data by automatically visualizing data right in Google Data Studio or Google Sheets.A year ago, we delivered client reporting once a week. Now, it’s daily. Customers can view real-time campaign performance in Data Studio — all they have to do is refresh.
Evan Vaughan
Head of Data Science at WITHIN
Having a consistent nomenclature and being able to stitch together a unified code name has allowed WITHIN to scale their analytics. Today, WITHIN is able to create an internal Media Mix Modeling (MMM) tool with the help of Google Cloud that they’re trialing with their clients.
The overall unseen benefit of BigQuery was that it put WITHIN in a position to remain nimble and spot trends before other agencies when COVID-19 hit. This aggregated view of data allowed WITHIN to provide unique insights to serve their customers better and advise them on rapidly evolving conditions.
Ready to modernize your data analytics? Learn more about how Google BigQuery unlocks the insights hidden in your data.
11 Reasons Why Developers and DB Admins in APAC Are Moving to Google Cloud SQL From AWS RDS
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We wanted to find out which of the most popular database services on two of the biggest cloud provider (AWS and Google), developers preferred to use.
More specifically, we wanted views from enterprises in the region (it’s hard to impossible to find data that’s specific to India.)
To simplify our quest, we looked at the most popular database services on AWS and then looked at the equivalent Google Cloud offering.
That’s how we came to pit AWS RDS and Google Cloud SQL.
For the uninitiated: Cloud SQL is a fully managed database service that makes it easy to set up and manage your relational PostgreSQL, MySQL, and SQL Server databases in the cloud.
It is ideal for: WordPress, backends, game states, CRM tools, MySQL, PostgreSQL, and Microsoft SQL Servers.
Based on real feedback from companies in the APAC region, there are 11 reasons why Google Cloud SQL is better.
In addition, according to Stackshare and ITcentralstation, the biggest reason developers prefer Google Cloud SQL? Because it’s fully managed, easy to set up, easy to manage and it’s really scalable.
As one user says:
Its most valuable feature is that it’s scalable. I can start off with a base of a lot of data and move as much as I want and it’s the same as if asked to do a lot of infrastructure changes… it’s easy to use, simple, and user-friendly. The setup was straightforward. Just a couple of clicks, and we were done. My suggestion to anyone thinking about this solution is to jump into it head-first!
Google Data Cloud: The Catalyst for Modern App Development and Innovation
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97 zettabytes was the estimated volume of data generated worldwide in 20221. This sort of explosion in data volume is happening in every enterprise. Now imagine being able to access all this data you own from anywhere, at any time, analyze it, and leverage its insights to innovate your products and services? One of the biggest barriers to fulfilling this vision is the complexity inherent in dealing with data trapped across silos in an enterprise. Google Data Cloud offers a unified, open, and intelligent platform for innovation, allowing you to integrate your data on a common architectural platform. Across industries, organizations are able to reimagine their data possibilities in entirely new ways and quickly build applications that delight their customers.
Siloed data: The barrier to speed and innovation
Digital technologies ranging from transaction processing to analytics and AI/ML use data to help enterprises understand their customers better. And the pace of innovation has naturally accelerated as organizations learn, adapt, and race to build the next generation of applications and services to compete for customers and meet their needs. At Next 2022, we made a prediction that the barriers between transactional and analytical workloads will mostly disappear.
Traditionally, data architectures have separated transactional and analytical systems—and that’s for good reason. Transactional databases are optimized for fast reads and writes, and analytical databases are optimized for analyzing and aggregating large data sets. This has siloed enterprise data systems, leaving many IT teams struggling to piece together solutions. The result has been time consuming, expensive, and complicated fixes to support intelligent, data-driven applications.
But, with the introduction of new technologies, a more time-efficient and cost-effective approach is possible. Customers now expect to see personalized recommendations and tailored experiences from their applications. With hybrid systems that support both transactional and analytical processing on the same data, without impacting performance, these systems now work together to generate timely, actionable insights that can be used to create better experiences and accelerate business outcomes.
According to a 2022 research paper from IDC, unifying data across silos via a data cloud is the foundational capability enterprises need to gain new insights on rapidly changing conditions and to enable operational intelligence. The modern data cloud provides unified, connected, scalable, secure, extensible, and open data, analytics, and AI/ML services. In this platform, everything is connected to everything else.
Why reducing data barriers delivers more value
The primary benefit of a unified data cloud is that it provides an intuitive and timely way to represent data and allows easy access to related data points. By unifying their data, enterprises are able to:
- Ingest data faster – for operational intelligence
- Unify data across silos – for new insights
- Share data with partners – for collaborative problem solving
- Change forecasting models – to understand and prepare for shifting markets
- Iterate with decision-making scenarios – to ensure agile responses
- Train models on historical data – to build smarter applications
As generative AI applications akin to Bard, an early experiment by Google, become available in the workplace, it will be more important than ever for organizations to have a unified data landscape to holistically train and validate their proprietary large language models.
With these benefits enterprises can accelerate their digital transformation in order to thrive in our increasingly complex digital environment. A survey of more than 800 IT leaders indicated that using a data cloud enabled them to significantly improve employee productivity, operational efficiency, innovation, and customer experience, among others.
Build modern apps with a unified and integrated data cloud
Google Cloud technologies and capabilities reduce the friction between transactional and analytical workloads and make it easier for developers to build applications, and to glean real-time insights. Here are a few examples.
- AlloyDB for PostgreSQL, a fully managed PostgreSQL-compatible database, and AlloyDB Omni, the recently launched downloadable edition of AlloyDB, can analyze transactional data in real time. AlloyDB is more than four times faster for transactional workloads and up to a 100 times faster for analytical queries compared to standard PostgreSQL, according to our performance tests. This kind of performance makes AlloyDB the ideal database for hybrid transactional and analytical processing (HTAP) workloads.
- Datastream for BigQuery, a serverless change data capture and replication service, provides simple and easy real time data replication from transactional databases like AlloyDB, PostgreSQL, MySQL and Oracle directly into BigQuery, Google Cloud’s enterprise data warehouse.
- And, query federation with Cloud Spanner, Cloud SQL, and Cloud Bigtable, make data available right from the BigQuery console allowing customers to analyze data in real-time in transactional databases.
Speed up deployments and lower costs
By reducing data barriers, we’re taking a fundamentally different approach that allows organizations to be more innovative, efficient, and customer-focused by providing:
- Built-in industry leading AI and ML that helps organizations not only build improved insights, but also automate core business processes and enable deep ML-driven product innovation.
- Best-in-class flexibility. Integration with open source standards and APIs ensures portability and extensibility to prevent lock-in. Plus, choice of deployment options means easy interoperability with existing solutions and investments.
- The most unified data platform with the ability to manage every stage of the data lifecycle, from running operational databases to managing analytics applications across data warehouses and lakes to rich data-driven experiences.
- Fully managed database services that free up DBA/DevOps time to focus on high-value work that is more profitable to the business. Organizations that switch from self-managed databases eliminate manual work, realize significant cost savings, reduce risk from security breaches and downtime and increase productivity and innovation. In an IDC survey, for example, Cloud SQL customers achieve an average three-year ROI of 246% because of the value and efficiencies this fully managed service delivers.
Google Data Cloud improves the efficiency and productivity of your teams, resulting in increased innovation across your organization. This is the unified, open approach to data-driven transformation bringing unmatched speed, scale, security, and with AI built in.
In case you missed it: Check out our Data Cloud and AI Summit that happened on March 29th, to learn more about the latest innovations across databases, data analytics, BI and AI. In addition, learn more about the value of managed database services like Cloud SQL in this IDC study.
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What is Cloud Spanner and Is It the Right Database for You?
You’ve probably heard about Cloud Spanner, but what exactly does it do that is so different from other databases out there?
How do you know if it’s worth spending your valuable time trying out?
Cloud Spanner is the first enterprise-grade, globally distributed, and strongly consistent database service built for the cloud specifically to combine the benefits of relational database structure with non-relational database scale.
Phew! That was a mouthful.
So, what really differentiates it? It’s unique in the marketplace in that it combines transactions, SQL query, and relational structure with the scalability you typically associate with non-relational or NoSQL databases.
Databases are part of every application. So it’s very important to pick the right database for your application.
With Cloud Spanner, you enjoy all the traditional benefits of a SQL database; transactions, high availability, schema changes without downtime, and SQL queries. But unlike many relational databases, Cloud Spanner scales horizontally from one to thousands of servers, an arbitrarily large number.
With fully automatic data replication and server redundancy, Cloud Spanner delivers up to five nines of availability in multi-region instances and four nines of availability in regional instances.
In fact, Google’s internal Spanner instance has been handling millions of queries per second from many Google services for years.
In this video, you’ll find out what Cloud Spanner is, what it’s useful for, why it’s unique, and the challenges it solves.
Gartner Positions Google as the Leader for Cloud Database Management Systems!
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We are thrilled that Gartner has positioned Google as a Leader for the second year in a row in the 2021 Gartner® Magic Quadrant™ for Cloud Database Management Systems (DBMS).
We believe the report evaluated Google’s unified capabilities across both transactional and analytical use cases, and showcases innovation progress in areas like data management consistency, high speed processing and ingestion, security, elasticity, advanced analytics, and more.
With the recent announcement of Dataplex, organizations can centrally manage, monitor, and govern their data across data lakes, data warehouses, and data marts with consistent controls. Solutions like BigQuery ML provide a “built-in” approach for advanced analytics capabilities and Analytics Hub offer the infrastructure customers need to share data analytics solutions securely and at scale in ways never before achieved. For example, over a seven-day period in April, more than 3,000 different organizations shared over 200 petabytes of data using BigQuery.
Research shows 90% of organizations have a multicloud strategy, which is why we have invested in a cross-cloud data analytics solution for Google Cloud, AWS, and Azure with BigQuery Omni. Additionally, our progress with Anthos and our Distributed Cloud this past year further advance our ability to support multi and hybrid cloud scenarios. To gain a competitive advantage using data, organizations need a data platform that transcends transactional and analytical workloads and can be run with the highest level of reliability, availability and security. Cloud Spanner, our globally distributed relational database has redefined the scale, global consistency, and availability of Online Transaction Processing (OLTP) systems. Spanner processes over 1 billion requests per second at peak, and has been battle-tested with some of the most-demanding applications, including Google services such as Search, YouTube, Gmail, Maps, and Payments. And what’s unique about our core services Spanner and BigQuery, is that they leverage common infrastructure such as our highly durable distributed file system (Colossus), our large-scale cluster management system (Borg), and Jupiter, our high-performance networking infrastructure, enabling features such as federation between Spanner and BigQuery.
We remain focused on the integration of Google Trends, Maps, Search, Ads, and have increased industry domain expertise in areas such as retail, financial services, healthcare, and gaming. We’re continuing to develop industry white papers such as this one – How to develop Global Multiplayer Games using Cloud Spanner – and we’re proud of the work the team has done to create and share industry and horizontal architecture patterns built from industry leaders to serve as solution accelerators for customer use cases.
Innovation momentum continues with a unified and open data cloud
We continue to innovate across our data cloud portfolio, especially with the innovations we announced at Google Cloud NEXT’21. BigQuery Omni is now available for AWS and Azure, supporting cross-cloud analytics for customers. We’ve added additional capabilities for enterprise data management and governance with Dataplex, which recently went GA. We’ve made migrations to Cloud SQL easier and faster with the Database Migration Service. More than 85% of all migrations are underway in under an hour, with the majority of customers migrating their databases from other clouds. We are embracing openness with Spanner by adding a PostgreSQL interface, allowing enterprises to take advantage of Spanner’s unmatched global scale, 99.999% availability, and strong consistency using skills and tools from the popular PostgreSQL ecosystem. We are also automating data processing with Spark on Google Cloud, which enables developers to spend less time on infrastructure management and more time on data science, modeling, and delivering business value. Finally, we announced Google Earth Engine on Google Cloud, allowing customers to integrate Earth Engine with BigQuery, Google Cloud’s ML technologies, and the Google Maps Platform.
With these innovations, enterprises like PayPal, Deutsche Bank, and Equifax use Google Cloud to solve for their end-to-end data lifecycle use cases. Organizations like Telefonica use Google Cloud to deliver new customer experiences. Telefónica has transformed every aspect of how they store, share, and analyze data while doubling processing power and lowering costs.
We continue to support an open ecosystem of data partners including Informatica, Tableau, MongoDB, Neo4j, C3.ai, and Databricks, giving customers the flexibility of choice to build their data clouds without being locked into a single approach.
We are honored to be a Leader in the 2021 Gartner Magic Quadrant for Cloud Database Management Systems (DBMS), and look forward to continuing to innovate and partner with you on your digital transformation journey.
Download the complimentary 2021 Gartner Magic Quadrant for Cloud Database Management Systems report.
Learn more about how organizations are building their data clouds with Google Cloud solutions.
Gartner, Magic Quadrant for Cloud Database Management Systems, Henry Cook, Merv Adrian, Rick Greenwald, Adam Ronthal, Philip Russom, 14 December 2021.
Gartner and Magic Quadrant are registered trademarks of Gartner, Inc. and/or its affiliates in the U.S. and internationally and are used herein with permission. All rights reserved.
Gartner does not endorse any vendor, product or service depicted in its research publications and does not advise technology users to select only those vendors with the highest ratings or other designation. Gartner research publications consist of the opinions of Gartner’s Research & Advisory organization and should not be construed as statements of fact. Gartner disclaims all warranties, expressed or implied, with respect to this research, including any warranties of merchantability or fitness for a particular purpose.
This graphic was published by Gartner, Inc. as part of a larger research document and should be evaluated in the context of the entire document. The Gartner document is available upon request from Google Cloud.
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