Google Cloud Data Heroes is a series where we share stories of the everyday heroes who use our data analytics tools to do amazing things. Like any good superhero tale, we explore our Google Cloud Data Heroes’ origin stories, how they moved from data chaos to a data-driven environment, what projects and challenges they are overcoming now, and how they give back to the community.

For our first issue, we couldn’t be more excited to introduce Google Cloud Data Heroine Lynn Langit. Lynn is a seasoned business woman in Minnesota beginning her eleventh year as the Founder of her own consulting business, Lynn Langit Consulting LLC. Lynn wears many data professional hats including Cloud Architect, Developer, and Educator. If that wasn’t already a handful, she also loves riding her bike at any given season of the year (pictured on the right), which you might imagine gets a bit challenging when you have to invest in bike studded snow tires!

Tell us how you got to be a data practitioner. What was that experience like and how did this journey bring you to GCP?

I worked on the business side of tech for many years. While I enjoyed my work, I found I was intrigued by the nuanced questions practitioners could ask – and the sophisticated decisions they could make – once they unlocked value from their data. This initial intrigue developed into a strong curiosity and I ultimately made the switch from business worker to data practitioner over 15 years ago. This was a huge change in career considering I got my bachelor’s degree in Linguistics and German. And so I started small. I taught myself most everything both at the beginning and even now through online resources, courses, and materials. I began with database and data warehousing, specifically building and tuning many enterprise databases. It wasn’t until Hadoop/NoSQL became available that I pivoted to Big Data…

Back then, I supplemented my self-paced learning with Microsoft technologies, even earning all Microsoft certifications in just one year. When I noticed the industry shifting from on premise to cloud, I shifted my learning from programming to cloud, too. I have been working in the public cloud for over ten years already!

“I started with AWS, but recently I have been doing most everything in GCP. I particularly love implementing data pipelining, data ops, and machine learning.”

How did you supplement your self teachings with Google Cloud data upskilling opportunities like product deep dives and documentation, courses, skills, and certificates?

One of the first Google Cloud data analytics products I fell in love with was BigQuery. BigQuery was my gateway product into a much larger open, intelligent, and unified data platform full of products that combined data analytics, databases, AI/ML, and business intelligence.

I’ve achieved Skills Badges in BigQuery, Data Analysis, and more. I’ve also achieved Google’s Professional Data Engineer Certification, and have been a Google Developer Expert since 2012. 

Most recently, I was named one of few Data Analysis Innovator Champions within the Google Cloud Innovators Program, which I’m particularly excited about because I’ve heard it’s a coveted spot for data practitioners and necessitates a Googler nomination to move from the Innovator membership to Champion title!

You’re undoubtedly a data analytics thought leader in the community. When did you know you moved from data student to data master and what data project are you most excited about? 

I knew I had graduated, if you will, to the data architect realm once I was able to confidently do data work that matters, even if that work was outside of my usual domains: adTech and finTech.. 

For example, my work over the past few years has been around human health outcomes, including combatting the COVID-19 pandemic. I do this by supporting scientists and bioinformatic researchers with genomic-scale data pipelines. Did I know anything about genomics before I started? Not at all! I self-studied bioinformatics and recorded my learnings on GitHub.  Along the way  I  adopted my learnings into an open source GCP course on GitHub aimed at researchers who are new to working with GCP. What’s cool about the course is that I begin from the true basics of how to set up a GCP account. Then I gradually work up to mapping out genomic-scale data workflows, pipelines, analyses, batch jobs, and more using BigQuery and a host of other Google Cloud data products. 

Now, I’ve received feedback that this repository has made a positive impact on researchers’ ability to process and synthesize enormous amounts of data quickly. Plus, it achieves the greater goal of broadening accessibility to a public cloud like GCP. 

In what ways do you think you uniquely bring value back to the data community? Why is it important to you to give back to the data community? 

I stay busy always sharing my learnings back to the community. I record Cloud and Big data technical screencasts (demos) on Youtube, I’ve authored 25 data and cloud courses on LinkedIn Learning, and I occasionally write Medium articles on cloud technology and random thoughts I have about everyday life. I’m also the cofounder of Teaching Kids Programming, with a mission to help equip middle and high school teachers with a great programming curriculum on Java.

Begin your own hero’s journey

Ready to embark on your Google Cloud data adventure? Begin your own hero’s journey with GCP’s recommended learning path where you can achieve badges and certifications along the way. Join the Cloud Innovators program today to stay up to date on more data practitioner tips, tricks, and events.

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. Lynn will take the main stage as an emcee for this event – you won’t want to miss!

Finally, if you think you have a good Data Hero story worth sharing, please let us know! We’d love to feature you in our series as well.

Organizations rely on data warehouses to aggregate data from disparate sources, process it, and make it available for data analysis in support of strategic decision-making. BigQuery is the Google Cloud enterprise data warehouse designed to help organizations to run large scale analytics with ease and quickly unlock actionable insights. You can ingest data into BigQuery either through batch uploading or by streaming data directly to unlock real-time insights. As a fully-managed data warehouse, BigQuery takes care of the infrastructure so you can focus on analyzing your data up to petabyte-scale. BigQuery supports SQL (Structured Query Language), which you’re likely already familiar with if you’ve worked with ANSI-compliant relational databases. 

BigQuery unique features

BI Engine – BigQuery BI Engine is a fast, in-memory analysis service that provides subsecond query response times with high concurrency. BI Engine integrates with Google Data Studio and Looker for visualizing query results and enables integration with other popular business intelligence (BI) tools. 

BigQuery ML: BigQuery ML is unlocking machine learning for millions of data analysts. It  enables data analysts and data scientists to build and operationalize machine learning models directly within BigQuery, using simple SQL.

BigQuery Omni – BigQuery Omni is a flexible, multi-cloud analytics solution powered by Anthos that lets you cost-effectively access and securely analyze data across Google Cloud, Amazon Web Services (AWS), and Azure, without leaving the BigQuery user interface (UI). Using standard SQL and familiar BigQuery APIs, you can break down data silos and gain critical business insights from a single pane of glass. 

Data QnA: Data QnA enables self-service analytics for business users on BigQuery data as well as federated data from Cloud Storage, Bigtable, Cloud SQL, or Google Drive. It uses Dialogflow and enables users to formulate free-form text analytical questions, with auto-suggested entities while users type a question.

Connected Sheets -The native integration between Sheets and BigQuery makes it possible for all business stakeholders, who are already quite familiar with spreadsheet tools, to get their own up-to-date insights at any time.

Geospatial data – BigQuery offers accurate and scalable geospatial analysis with geography data types. It supports core GIS functions – measurements, transforms, constructors, and more – using standard SQL.

How does it work?

Here’s how it works: You ingest your own data into BigQuery or use data from the public datasets. Storage and compute are decoupled and can scale independently on demand. This offers immense flexibility and cost control for your business as you don’t need to keep expensive compute resources up and running all the time. As a result, BigQuery is much more cost-effective than traditional node-based cloud data warehouse solutions or on-premises systems. BigQuery also provides automatic backup and restore of your data. 

You can ingest data into BigQuery in batches or stream real-time data from web, IoT, or mobile devices via Pub/Sub. You can also use Data Transfer Service to ingest data from other clouds, on-premises systems or third-party services. BigQuery also supports ODBC and JDBC drivers to connect with existing tools and infrastructure. 

Interacting with BigQuery to load data, run queries, or create ML models can be done in three different ways. You can use the UI in the Cloud Console, the BigQuery command-line tool, or the API via client libraries available in several languages.

When it comes time to visualize your data, BigQuery integrates with Looker as well as several other business intelligence tools across the Google partner ecosystem.

What about security?

BigQuery offers built-in data protection at scale. It provides security and governance tools to efficiently govern data and democratize insights within your organization. 

• Within BigQuery, users can assign dataset-level and project-level permissions to help govern data access. Secure data sharing ensures you can collaborate and operate your business with trust.
• Data is automatically encrypted both while in transit and at rest, ensuring that your data is protected from intrusions, theft, and attacks. 
• Cloud DLP helps you discover and classify sensitive data assets.
• Cloud IAM provides access control and visibility into security policies.
• Data Catalog helps you discover and manage data. 

How much does it cost?

The BigQuery sandbox lets you explore BigQuery capabilities at no cost and confirm that BigQuery fits your needs. With BigQuery you get predictable price-performance: you pay for storing and querying data, and for streaming inserts. Loading and exporting data are free of charge. Storage costs are based on the amount of data stored, and have two rates based on how often the data is changing. Query costs can be either:

• On-demand – you are charged per query by the amount of data processed
• Flat-rate – if you prefer to purchase dedicated resources 

You can start with the pay-as-you-go, on-demand option and later move to flat-rate if that better suits your usage. Or, start with flat-rate, get a better understanding of your usage and move to the pay-as-you-go models for additional workloads. 

To explore BigQuery and its capabilities a bit more, check out the sandbox; and when you’re ready to modernize your data warehouse with BigQuery then check out the documentation to streamline your migration process here. https://www.youtube.com/embed/So-tVyBQt8E?enablejsapi=1&

For more #GCPSketchnote, follow the GitHub repo. For similar cloud content follow me on Twitter @pvergadia and keep an eye out on thecloudgirl.dev.

The music industry is rapidly changing. Only 20 years ago, the availability of music and the infrastructure that was required to make an album a sales success were incredibly complex and expensive. With the decline of physical sales and a fundamental shift to digital, music streaming now accounts for more than half of all sales globally.

At the same time, technology has democratized music-making; in many ways, it has made the musical landscape more diverse. Artists can upload their music with the click of a button. But while it’s easier for creators to share their songs with audiences, getting paid has become more fragmented.

Although music is booming, people no longer buy it outright. Instead, listeners download music digitally or subscribe to streaming services to have their libraries with them at all times. To monetize digital content effectively, artists need to know when, where, and how often their songs are played on each service. To help them navigate this complicated royalties landscape and maximize their profits, Berlin-based international music company BMG provides customized, transparent, and fair services to songwriters and artists.

With publishing and recording divisions under one roof, the subsidiary of international media giant Bertelsmann works with both emerging artists and established stars, including John Legend, Kylie Minogue, Mick Jagger, and Keith Richards. With the MyBMG web and mobile application, clients can view and analyze their royalty details in real time and collect payment. When a new record is released, BMG uses data to maximize its impact and revenue for its creators.

“We make sure that everyone who uses our clients’ music knows who needs to be paid the associated royalties, then we collect these royalties and share them out quickly and transparently,” explains Sebastian Hentzschel, Chief Information Officer at BMG. “When our artists release new music, we make sure that it’s marketed and promoted effectively around the world.”

“We needed a scalable solution for our royalty workloads that was intuitive for our developers. We also wanted a partner, not a client-vendor relationship. With autoscaling via BigQuery, excellent customer support, and a clean and simple user interface, Google Cloud ticks every box for us.”

—Gaurav Mittal, Vice President Group Technology, BMG

Getting up to speed with a new way of paying artists

In this digital world, artists aren’t just paid every time a fan buys an album—they’re paid a small amount, or royalty, for each song downloaded or streamed by a listener. So, when the industry shifted to digital, the volume of data that BMG needed to handle grew exponentially. “One CD sale is equivalent to about 1,500 streamed songs or plays,” says Gaurav Mittal, Vice President Group Technology at BMG. “That means IT departments have to process 1,500 times the amount of data to calculate payments for artists, and this makes scalable micropayment processing very important.”

Until 2019, BMG’s infrastructure was entirely hosted on-premises. Hardware limitations made it challenging to scale on-demand, making it harder to handle the data peaks that royalty processing can bring. “With our on-premises infrastructure, we were going to hit a ceiling in a few years,” says Gaurav. “We still managed to process royalty payments for our clients, but it was increasingly time consuming and expensive. To keep focusing on our clients, rather than our infrastructure, we decided to migrate to Google Cloud.”

From the outset, Gaurav and his team had a clear vision for the partnership: “Most importantly, we needed a scalable solution for our royalty workloads that was intuitive for our developers. We also wanted a partner, not a client-vendor relationship,” he says. “With autoscaling via BigQuery, excellent customer support, and a clean and simple user interface, Google Cloud ticks every box for us.”

Keeping artists happy with business-as-usual payouts during migration

To move applications to the cloud while keeping payment cycles on track for its artists, BMG teamed up with Google Cloud partner Rackspace Technology. “We selected Rackspace Technology because it combines strong technical muscle and a global footprint, with the customer service of a local boutique firm,“ shares Gaurav.

BMG’s own technology team put together the outline for the Google Cloud architecture, which they passed on to Rackspace Technology for optimizations and the ultimate stamp of approval. Whenever Gaurav and his developers needed support, Rackspace Technology was ready to go. “So far, we’ve migrated 17 applications successfully, and Rackspace Technology has been 100% spot-on with each suggestion,” says Gaurav. “I can’t recall a single flaw in a Rackspace Technology-approved architecture, and that really says something.”

When BMG began its migration in August 2019, the team developed an ambitious two-year plan. Only 14 months later, however, the project is more than 75% complete. Among the solutions that BMG is using today are Cloud Storage to securely store 130 TB of data, and Cloud SQL as its standard database technology. The web applications run on Compute Engine, App Engine, and Google Kubernetes Engine.

“After successfully moving a few applications, it was clear that with the strong teamwork of BMG and Rackspace Technology, together with the ease of use of Google Cloud, we could speed up the project without sacrificing quality,” says Gaurav. “We’re set to complete our migration six months before schedule, helping us to quickly move out of our hybrid environment.”

“Our income-tracking teams are very savvy on the data, and the simplicity of Google Cloud empowers them to self-serve analytics, rather than wait for IT. Our teams are much more productive.”

—Gaurav Mittal, Vice President Group Technology at BMG

Royalty reporting and processing with BigQuery and Dataproc

So far, all of BMG’s critical workloads are up and running on Google Cloud. Royalty calculations, for example, which require incredible processing power and the collection of many micropayments to ensure full and timely payout, run entirely on Dataproc with output stored on BigQuery for downstream integration and reporting.

Enabling more harmonious workflows through self-serve analytics

As the new beating heart of BMG’s royalty reporting, BigQuery changed the rhythm of collaboration company-wide. In the past, income tracking teams had to contact IT departments if they needed deeper data insights for their work. By integrating Data Catalog with BigQuery, BMG has made the data more accessible to all teams. This helps them detect missing income and new revenue streams independently, maximizing profits for artists.

“Our income-tracking teams are very savvy on the data, and the simplicity of Google Cloud empowers them to self-serve analytics, rather than wait for IT,” says Gaurav. “Our teams are much more productive.”

“Google Cloud enables us to be more client focused and deliver better features faster. We believe it’s just the beginning. We offer rights and royalty services for music publishing, recorded music, neighboring rights, and books. Without scalability limitations, it’s absolutely conceivable to offer our platform as a service to other companies or industries, such as gaming. Google Cloud has opened a world of possibilities.”

—Sebastian Hentzschel, Chief Information Officer, BMG

With a leaner IT environment, BMG can focus its effort on the needs of its clients. Beyond improvements in royalty processing, it can concentrate on app development, releasing new features and enhancements more frequently. By hosting applications on Google Kubernetes Engine, App Engine, and Compute Engine, BMG has built a CI/CD pipeline with automated deployments and testing to significantly speed up workflows.

“In our old system, it could take several weeks to set up an environment,” says Gaurav. “With Google Kubernetes Engine, any of our developers can complete the process in a few clicks. Having that autonomy makes our developers more motivated and self-driven.”

“Google Cloud enables us to be more client focused and deliver better features faster,” adds Sebastian. “We believe it’s just the beginning. We offer rights and royalty services for music publishing, recorded music, neighboring rights, and books. Without scalability limitations, it’s absolutely conceivable to offer our platform as a service to other companies or industries, such as gaming. Google Cloud has opened a world of possibilities.”

With the migration almost complete, BMG is looking forward to its next technology project. It plans to leverage AutoML to further scale and automate royalty tracking with machine learning. On the marketing side, advanced analytics will help BMG determine the effectiveness of promotional campaigns around the world, further increasing profits for artists. By connecting Google Data Studio to BigQuery, BMG will increase the quality of its analyses, helping musicians better understand the reach of their music around the world.

In the end, Gaurav shares, helping musicians is what it all comes down to. “We’re a new kind of music company because we build our services around our artist, songwriter, and publisher clients, not the other way around,” he says. “Google Cloud is helping us maintain strong relationships with our clients, and that’s music to our ears.”

Like virtually every business across the globe, The New York Times had to quickly adapt to the challenges of the coronavirus pandemic last year. Fortunately, our data system with Google Cloud positioned us to perform quickly and efficiently in the new normal. 

How we use data

We have an end-to-end type of data platform; on one side we work very closely with our product teams to collect the right level of data that they’re interested in, such as which articles people are reading, and how long they’re staying onsite. We frequently measure our audience to understand our user segments, and how they come onsite or use our apps. We then provide that data to analysts for end-to-end analytics. 

On the other side, the newsroom is also focused on audience, and we build tools to help them understand how Google Search or different social promotions play a role in a person’s decision to read The New York Times, and also to get a better sense of their behavior on our pages. With this data, the newsroom can make decisions about information that should be displayed on our homepage or in push notifications. 

Ultimately, we’re interested in behavioral analytics—how people engage with our site and our apps. We want to understand different behavioral patterns, and which factors or features will encourage users to register and subscribe with us. 

We also use data to create or curate preferences around personalization, to ensure we’re delivering to our users fresh content, or content that they may not have normally read. Likewise, our data also gets used in our targeting system, so that we can send out the right messaging about our various subscription packages to the right users.

Choosing to migrate to Google Cloud

When I came to The New York Times over five years ago, our data architecture was not working for us. Our infrastructure was gathering data that proved harder for analysts to crunch on a daily basis. We were also hitting hang ups with how that data was streaming into our system and environment. Back then we’d run a query and then go grab some coffee, hoping that the query would finish or give us the right data by the time we came back to our desks. Sometimes it would, sometimes it wouldn’t.

We realized that Hadoop was definitely not going to be the on-premises solution for us, and that’s when we started talking with the Google Cloud team. We began our digital transformation with a migration to BigQuery, their fully managed, serverless database warehouse. We were under a pretty aggressive migration timeline, focusing first on moving over analytics. We made sure our analysts got a top-of-the-line system that treated them the way that they themselves would want to treat the data. 

One significant prominent requirement in our data architecture choice was to enable analysts to be able to work as quickly as they needed to provide high-quality deliverables for their business partners. For our analysts, the transition to BigQuery was night and day. I still remember when my manager ran his very first query on BigQuery and was ready to go grab his coffee, but the query finished by the time he got up from his chair. Our analysts talk about that to this day.

While we were doing the BigQuery transition, we did have concerns about our other systems not scaling correctly. Two years ago, we weren’t sure we’d be able to scale up to the audience we expected on that election day. We were able to band-aid a solution back then, but we knew we only had two more years to figure out a real, dependable solution. 

During that time, we moved our streaming pipeline over to Google Cloud, primarily using App Engine, which has been a flexible environment that enabled quick scaling changes and requirements as needed. Dataflow and Pub/Sub also played significant roles in managing the data. In Q4 of 2020 we had our most significant traffic ever recorded, at 273 million global readers, and four straight days of the highest traffic we’ve had compared to other election weeks. We were proud to see that there was no data loss.

A couple of years ago, on our legacy system, I was up until three in the morning one night trying to keep data running for their needs. This year, for election night, I relaxed and ate a pint of ice cream because I was able to more easily manage our data environment, allowing us to set and meet higher expectations for data ingestion, analysis and insight among our partners in the newsroom.

How COVID-19 changed our 2020 roadmap 

The coronavirus pandemic definitely wasn’t on my team’s roadmap for 2020, and it’s important to mention here that The New York Times is not fundamentally a data company. Our job is to get the news out to our users every single day in paper, on apps, and onsite. Our newsroom didn’t expect the need to build out a giant coronavirus database that would enrich the news they share every day. 

Our newsroom moves quickly, and our engineers have built one of the most comprehensive datasets on COVID-19 in the U.S. With Google, The New York Times decided to make our data publicly available on BigQuery Google’s COVID-19 public dataset. Check out this webinar for more details on our evolution architecture:https://www.youtube.com/embed/mtNlrFpschU?enablejsapi=1&

Flexible approach

We have many different teams that work within Google Cloud, and they’ve been able to pick from the range of available services and tailor project requirements keeping those tools available in mind.

One challenge we think about with the data platform at The New York Times is determining the priorities of what we build. Our ability to engage with product teams at Google though the Data Analytics Customer Council allows us to see into the BigQuery roadmap, or the data analytics roadmap, and plays a significant role in determining where we focus our own development. For example, we’ve built tools like our Data Reporting API, which reads data directly from BigQuery, in order to take advantage of tools like BigQuery BI Engine. This approach encourages our analysts to be better managers of their domains around dimensions and metrics, but not have to focus on building caching mechanisms of their data. Getting that kind of clarity helps us plan how to build The New York Times in the new normal and beyond.

If you are interested to learn more about the data teams at the New York Times, take a look at our open tech roles here and you’ll find many interesting articles at NYT data blog.

As we engage with enterprises across the globe, one thing is becoming clear: Today’s businesses are solving complex business problems that are data-intensive.

But often, their data platform infrastructure is holding them back. Data platform architectures that were designed in the 1990s are not ready to solve business problems for 2020. We don’t have to tell you about the explosive data growth that’s going on for businesses around the world. If you’re managing data infrastructure today, you already know plenty about that data growth.

Ever faster and larger data streams, global business needs, and tech-savvy users are all putting the pressure on IT teams to move faster, with more agility.

Despite all these changes, it is often the legacy, traditional data warehouse where most of the data analytics tasks take place, and they’re underprepared for those demands.

When we talk to people working in IT today, we hear a lot about the constraints that come with operating legacy technology while trying to build a modern data strategy. Those legacy data warehouses likely aren’t cutting it anymore. Here’s why—and here’s what you can do about it.  

1. Business agility is hard to achieve with legacy tools. 
Business agility is the main goal as organizations move toward completely digital operations. Think of online banking, or retailers staying ahead of always-on e-commerce needs in a competitive environment. All these great, cutting-edge innovations reflect cultural and technical change, where flexibility is essential. A business has to be able to manage and analyze data quickly to understand how to better serve customers, and allow its internal teams to do their best work with the best data available.

We hear that lots of data warehouses running today are operating at 95% or 100%, maxing out what they can provide to the business.

Whether it’s on-premises or an existing data warehouse infrastructure moved wholesale to cloud, those warehouses aren’t keeping up with all the data requests users have. Managing and preventing these issues can take up a lot of IT time, and the problems often compound over time. Hitting capacity limits slows down users and ties up database administrators too.

From a data infrastructure perspective, separating the compute and storage layers is essential to achieve business agility. When a data warehouse can handle your scalability needs and self-manage performance, that’s when you can really start being proactive. 

2. Legacy data warehouses require a disproportionate degree of management. 
Most of the reports and queries your business runs are probably time-sensitive, and that sense of urgency is only increasing as users and teams see the possibilities of data analytics.

In our engagements with customers, we often observe that they are spending a majority of the time on systems engineering, so that only about 15% of the time is spent analyzing data. That’s a lot of time spent on maintenance work.

Because legacy infrastructure is complex, we often hear that businesses continue to invest in hiring people to manage those outdated systems, even though they’re not advancing data strategy or agility.

To cut time on managing a data warehouse, it helps to automate the system engineering work away from the analytics work, like BigQuery enables.

Once those functions are separated, the analytics work can take center stage and let users become less dependent on administrators. BigQuery also helps remove the user access issues that are common with legacy data warehouses. Once that happens, users can focus on building reports, exploring datasets, and sharing trusted results easily. 

3. Legacy data warehouse costs make it harder to invest in strategy. 
Like other on-prem systems, data warehouses adhere to the old-school model of paying for technology, with the associated hardware and licensing costs and ongoing systems engineering.

This kind of inefficient architecture drives more inefficiency. When the business is moving toward becoming data-driven, they’ll continue to ask your team for more data. But responding to those needs means you’ll run out of money pretty quickly. 

Cloud offers much more cost flexibility, meaning you’re not paying for, or managing, the entire underlying infrastructure stack. Of course, it’s possible to simply port an inefficient legacy architecture into the public cloud.

To avoid that, we like to talk about total cost of ownership (TCO) for data warehouses, because it captures the full picture of how legacy technology costs and business agility aren’t matching up.

Moving to BigQuery isn’t just moving to cloud—it’s moving to a new cost model, where you’re cutting out that underlying infrastructure and systems engineering. You can get more detail on cloud data warehouse TCO comparisons from ESG.

4. A legacy data warehouse can’t flexibly meet business needs. 
While overnight data operations used to be the norm, the global opportunities for businesses mean that a data warehouse now has to load streaming and batch data while also supporting simultaneous queries. Hardware is the main constraint for legacy systems as they struggle to keep up.

Moving your existing architecture into the cloud usually means moving your existing issues into the cloud, and we hear from businesses that doing so still doesn’t allow for real-time streaming.

That’s a key component for data analysts and users. Using a platform like BigQuery means you’re essentially moving your computational capabilities into the data warehouse itself, so it scales as more and more users are accessing analytics.

Unlimited compute is a pretty good way to help your business become digital. Instead of playing catch-up with user requests, you can focus on developing new features. Cloud brings added security, too, with cloud data warehouses able to do things like automatically replicate, restore and back up data, and offer ways to classify and redact sensitive data. 

5. Legacy data warehouses lack built-in, mature predictive analytics solutions. 
Legacy data warehouses are usually struggling to keep up with daily data needs, like providing reports to departments like finance or sales. It can be hard to imagine having the time and resources to start doing predictive analytics when provisioning and compute limits are holding your teams back.

We hear from customers that many of them are tasked with simplifying infrastructure and adding modern capabilities like AI, ML and self-service analytics for business users.

The best stories about digital transformation are those where the technology changes and business or cultural changes happen at the same time. One customer told us that because BigQuery uses a familiar SQL interface, they were actually able to shift the work of data analytics away from a small, overworked group of data scientists into the hands of many more workers.

Doing so also eliminated a lot of the siloed data lakes that had sprung up as data scientists extracted data one project at a time into various repositories to train ML models.

These large-scale computational possibilities save time and overhead, but also let businesses explore new avenues of growth. AI and ML are already changing the face of industries like retail, where predictive analytics can provide forecasting and other tasks to help the business make better decisions. BigQuery lets you take on sophisticated machine learning tasks without moving data or using a third-party tool. 

We designed BigQuery so that our engineers deploy the resources needed for you to scale. It means your focus can change entirely toward meeting the needs the business has put forth, and bringing a lot more flexibility.

BigQuery is fully serverless and runs on underlying Google infrastructure, so it integrates with our ecosystem of data and analytics partner tools. This architecture means you’re continually getting the most up-to-date software stack—analytics that scale, real-time insights, and cutting-edge functionality that includes geospatial and machine learning right from the SQL interface. 

Streamline your path to data warehouse modernization with BigQuery by learning about Google Cloud’s proven migration methodology and get started with your data warehouse by applying for our migration offer.