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.

When Blue Apron had issues running its data warehouse on another cloud provider, it built an analytics platform using Looker and Google BigQuery to enable faster business decisions about food inventory.

Blue Apron’s mission is to make incredible home cooking accessible to everyone. Launched in 2012, Blue Apron is reimagining the way that food is produced, distributed, and consumed.

Industries: Retail & Consumer Goods

Location: United States

Google Cloud Results

• Enables near real-time business decisions to better manage food inventory and delivery by reducing query times exponentially
• Helps improve customer service and optimize business processes with faster insights
• Reduces costs while reclaiming up to a week per month of engineering

The popularity of meal kit delivery services has surged in recent years as consumer attitudes toward home cooking and grocery shopping have shifted. As a pioneer in the category, Blue Apron helps its customers create incredible home cooking experiences by sending culinary-driven recipes with high-quality ingredients and step-by-step instructions straight to customers’ doors. Blue Apron also offers a monthly wine subscription service and a la carte culinary tools and products through its marketplace.

If that sounds simple, it isn’t. Ingredients for the meal kits must be sourced at the right time, quality, and price. Orders must be packed efficiently and in exactly the right proportions. Most importantly, meal kits must be delivered to the customer fresh and on time.

To meet these criteria and make data meaningful and intuitive to its managers, one of the tools Blue Apron relies on is Looker, an analytics platform that lets business users explore data and ask sophisticated questions using familiar terms.

A Google Cloud partner and winner of the 2016 Google Cloud Global Partner Award for Solution Innovation, Looker integrates its solution with Google Cloud Platform to help customers modernize their analytics.

Blue Apron previously used Looker with a single database instance hosted on another cloud provider. As data volumes grew and queries became more complex, it became difficult to scale. Blue Apron’s only options were choosing ever-larger server classes and increasing storage throughput by purchasing a higher number of provisioned IOPS. To improve speed, scalability, and cost efficiency, Blue Apron moved its data warehouse to Google BigQuery.

“The combination of Looker and Google BigQuery is powerful, allowing us to get data-hungry analysts essential information much faster,” says Sam Chase, Tech Lead, Data Operations at Blue Apron. “Because we choose to pay by the query, it’s also flexible and cost effective—plus storage is cheap, so we can just put data in and query what we need.”

The analytics platform of the future

When you’re making business decisions about a customer’s dinner, speed matters. Looker takes full advantage of the power of Google BigQuery, making it easy to build a data exploration platform. Blue Apron’s applications publish event data to Kafka—approximately 140 million events per day—and data is then streamed into Google BigQuery, which performs lightning-fast queries on both streamed and static data. Now, business users and analytics teams can make decisions based on near real-time information in Looker, instead of waiting until the next business day for results.

“After we moved to Google BigQuery, query time was reduced exponentially. It’s an astonishing difference, allowing us to run 300 queries per day,” says Sam.

Previously, Blue Apron spent up to a week out of every month optimizing its data warehouse to attempt to improve query performance. With Google BigQuery, all maintenance is handled by Google, reclaiming 25% of up to two engineers’ time. Even when multiple people are using Looker concurrently, query performance never degrades and storage never runs out.

“Because Google BigQuery is architected as a giant, shared cluster, growth is smooth,” says Lloyd Tabb, Founder and CTO of Looker. “Like a race car going from 0 to 120 mph, there are no shift points, just smooth acceleration. To us, it looks like the future.”

An empowering, integrated toolset

Looker takes advantage of aggressive caching and support for date-based table partitioning in Google BigQuery to increase performance, simplify the load process, and improve data manageability. By partitioning data by time, Blue Apron can also take advantage of better long-term storage pricing without sacrificing query performance. When using Google BigQuery with Looker, analysts can easily see how much data is going to be scanned before each query is run.

Blue Apron is also using Looker for Google BigQuery Data Transfer Service to provide actionable analytics for all of the company’s Google marketing data from Google AdWords and DoubleClick by Google in one place to understand campaign performance across channels, saving its data operations team months of work. Using Looker Blocks, marketers can quickly make sense of the data with reports and dashboards, and set alerts when campaign performance hits certain thresholds.

Looker Blocks for Google AdWords and DoubleClick by Google provide all the analysis you’d get straight from the Google console, plus additional value-add analysis that’s impossible to replicate without SQL. Complex metrics such as ROI on ad spend, flexible multi-touch attribution, and predictive lifetime value empower marketers with a better understanding of their customers and where to spend their next dollar.

In addition to these turnkey dashboards and pieces of analysis, marketers can customize views to meet their unique needs and workflows. These capabilities help the Blue Apron marketing team make decisions regarding the allocation of spend to maximize customer acquisition and retention.

“Everyone at Blue Apron is excited about using Google BigQuery with Looker,” says Sam. “Business users and marketers are more empowered to look for answers, instead of waiting for analytics teams. Because users know they can get results rapidly, our business processes are evolving and improving.”

For data cleansing and transformation, Blue Apron uses Google Cloud Dataproc to run fully managed Apache Spark clusters on Google Cloud Platform. It’s also leveraging Google BigQuery integration with Google Workspace to bring data into Google Sheets for further distribution and analysis.

“Transferring data between Google tools is fast because it all happens on the Google network,” says Sam. “We can pull data from Google BigQuery, run transformations with Spark, and then write it back to Google BigQuery. That’s very helpful in providing our business users and data analysts with the richest, most current data.”

A perfect match for better insights

As Blue Apron seeks to expand its reach and deepen its engagement with customers, it is making Google BigQuery and Looker available to more users, providing a high-quality interactive analytics experience. “Our ability to pull a lot of data in and compute fast results affects everyone in our company,” says Sam. “Using Google BigQuery and Looker to iterate quickly and build new models to make our operations more efficient will directly impact our customers.”

For Looker, Google BigQuery represents the next step in data warehouse evolution. “Google BigQuery is a perfect match for Looker, combining easy setup with near infinite scale-out and elasticity,” says Lloyd. “People can make smarter decisions faster that directly benefit their business and customers.”

Editor’s note: In this blog post we learn how TapClicks migrated to Google Cloud to offer their marketing customers a unified platform for data management, operations, insights, and analysis.

TapClicks is a smart marketing cloud, powered by data, that unifies our customer’s marketing. By choosing to migrate our core applications last year to Google Cloud, we cut costs, solved data-sharing concerns for our customers, and opened our stack up to a new ecosystem of possibilities. 

The core problem that we’re solving for our customers is how to manage their marketing infrastructures data and operations. Life isn’t easy for marketers now. There are 7,000 different vendors servicing this space today – creating much complexity between digital agencies, media, and brands. Marketers face challenges in navigating all of these systems, logging in and out, understanding pacing goals, and managing the flow of marketing data so they can analyze and report internally as well as to their clients at scale.

We unify omnichannel campaign data (250 API connectors and 6000 Smart Connectors ™ ) from a plethora of marketing sources on an automated data warehousing solution, creating simplicity for organizations. Over 4,000 agencies, media companies, and brands use our Marketing Operations and Data Management Platform, which imports data at scale and creates an automatic data warehouse on Google Cloud. Teams can also leverage TapClicks, like our world class Facebook connector, to import data directly into Google Data Studios.  Beyond importing and storing, we also provide data exporting to other Google solutions like Google Data Studio and Google Sheets.  We also create interactive dashboards that let stakeholders and clients analyze their data, as well as automated, multi-channel reports that go out to clients at specified times. So channel comparisons, optimizations, attribution, and calculations are easily performed.  Some of our customers are able to generate hundreds of thousands of individual reports and dashboards for their clients.

Although we may be best known for our reporting and analytics, we also empower teams managing the marketing operations workflow from customers and internal stakeholders, especially at scale. Our user-friendly, configurable system helps manage their orders and campaigns. Through automation of this process, we deliver tremendous amounts of efficiency, time saving, cost savings, and reduction of errors. The combination of these solutions makes up our unified platform, with additional capabilities like marketing intelligence that offers competitive and brand-level analysis. This is a disruptive solution in use by all leading media companies, agencies and many brands.

Partnering for possibilities

We faced a few challenges with our original tech stack, which included a mix of the leader in web services revenue, leaders in high performance data warehousing, as well as vendors on bare metal servers. 

• One challenge was around costs, which were growing. 
• Second, many of our customers work with multiple brands, and are very hesitant to share their data with the leader in web services, who’s often viewed as their competitor. 
• Third, these vendors are more focused on their own revenue rather than a true long term partnership that would enable their customers to enjoy similar success as they have experienced.

When looking at other cloud providers, Google Cloud emerged for us as the front runner. They were competitive on costs, and their native Kubernetes support was superior— a big selling point for our DevOps team. There’s also a movement in the marketing and advertising industry away from AWS toward Google Cloud because of the data-sharing concern. Finally, most of our customers are already using Google Cloud tools, so there’s brand recognition and familiarity there, and easier integrations with their own systems.

Migrating to Google Cloud

Our migration, which took about five months, involved moving a significant chunk of our infrastructure, including our core applications, using Google Kubernetes Engine (GKE). In our legacy architecture, each of our clients was assigned to one of our virtual machines (VMs), and there was a lot of unused capacity because we had to provision for the max usage. We appreciated GKE’s cloud native capabilities, especially autoscaling, a huge benefit for our web application. We have varying usage patterns during the day, and though our application is mostly used during business hours, there are also days in the month of higher usage, and autoscaling saves us time and costs. GKE also makes deployments much easier, and we anticipate a lot of benefits there for our developer environments. We’ve moved some of our microservices into GKE and plan to move more in the future. All in all, we were able to migrate our core products and the bulk of our AWS spend successfully to Google Cloud. 

We also moved from our other vendors Relational Database Service (RDS) to running MySQL on our own VMs on Google Cloud, which gives us more flexibility in terms of settings and fine tuning. We’re still trying to find the best mix as we’re modernizing our infrastructure, and we took this opportunity to migrate from MySQL 5.7 to 8.0.  

Our next stage is exploring more of the capabilities and services of Google Cloud, including BigQuery, which we’re considering for our own data warehouse. The fact that we could also run Snowflake on Google Cloud, if needed, was another selling point for our migration. 

We’re especially interested in BigQuery ML’s machine learning and natural language processing capabilities, which enabled better predictive insights. Our customers want insights from their campaigns— which are working, which are paying off, where should they invest next? Using our platform, they’re looking not only to generate reporting, but also identify opportunities to improve campaign performance. We plan to use AI and ML to improve those capabilities, so that our customers can seamlessly unlock insight and intelligence from their marketing data and campaigns.

Double-clicking on Google Cloud

For us, being able to deeply leverage and partner with Google Cloud to deliver those solutions on a single stack is critical, and we think our customers will love it. We see TapClicks and Google Cloud partnering at a level beyond what you typically see in a cloud provider relationship. Already, fifty percent of our company is working with various Google Cloud solutions, and we envision TapClicks and Google Cloud as extensions of each other, providing a single, powerful platform solution. 

Google Cloud understands the partnership concept, and their team was able to shine a light on their services and what they could bring to the table. Compared to our previous experiences, dealing with the Google Cloud team has been a true pleasure. Now that we’ve migrated, we’re ready to take our next steps into the services available to us in the Google Cloud ecosystem, and the problems we’ll continue to solve for our customers. Learn more about TapClicks and BigQuery ML.

Despite a growing number of diversity, equity, and inclusion (DEI) initiatives, Black and Latinx people remain highly underrepresented in tech. Although comprising 12.6% and 18% of the U.S. labor force respectively, Black professionals hold only 5% of tech positions, while Latinx professionals fill just 6% of tech roles.

Long-standing biases in hiring practices and non-inclusive work environments are the primary contributors to this lack of diversity. Even when underrepresented professionals are successfully recruited, invisible barriers to promotion and layoffs that disproportionally impact Black and Latinx employees make it extremely challenging for tech firms to retain top talent.

We founded Divercity to help employers build diverse workforces that are more inclusive and sustainable. With our comprehensive recruiting and retention platform, tech companies can accurately measure employee diversity and gender parity, seamlessly connect with underrepresented talent, and significantly reduce turnover.

As Divercity continues to grow, we’ll introduce new services and solutions that empower the tech world to build inclusive companies while improving compliance with state and federal equal opportunity laws. We also hope to expand the reach of Divercity to support DEI initiatives in non-tech industries and bolster recruiting underrepresented professionals in other countries as well.

Scaling Divercity with the help of the Google for Startups Black Founders Fund

Shortly after founding Divercity, we participated in the 2021 Techstars Workforce Development Accelerator. The incredible support and guidance we received during and after the program highlighted the importance of long-term collaboration with reliable technology partners who actively champion diversity and inclusion.

That’s why we became part of the Google for Startups Cloud Program. After completing the program, we used Google Cloud credits and Google for Startups Black Founders Fund funding to cost-effectively trial, deploy, and scale key Google Cloud solutions. In just months, we rolled out new inclusion tracking and recruiting tools on the highly secure-by-design infrastructure of Google Cloud to expedite the sourcing and hiring of underrepresented talent in the tech industry.

With the support and mentorship of the Google for Startups Cloud Program and the Black Founders Fund, Divercity is well on its way to becoming one of the industry’s most trusted sites for DEI measurement, recruitment, and retention.

Delivering predictive diversity analyses with a 99% accuracy rate

We rely on the expansive Google Cloud ecosystem to power all the services offered on the Divercity platform. Specifically, we leverage Colab to write and execute the sophisticated TensorFlow machine learning (ML) models that deliver our predictive diversity analyses with a 99% accuracy rate.

We also use BigQuery to democratize insights and run analytics at scale with 27% lower three-year TCO than cloud data warehouse alternatives. BigQuery seamlessly integrates with Looker and Data Studio to display company diversity and recruitment data on interactive dashboards—and automatically populate reports with detailed demographic information.

We also accelerate the development, launch, and management of new Divercity tools with Firebase, while taking advantage of key features such as A/B testing and messaging campaigns to boost user engagement.

In the future, we plan to explore how Google Cloud AI and machine learning products such as Vertex AI and AutoML can further refine our diversity score analyses and applicant recruiting pipeline. We’ll also continue leveraging the many resources provided by the Google Black Founders Fund, including opportunities for technical project partnerships, early access to new Google Cloud products and tools, and collaboration with dedicated Google experts.

The Google for Startups Cloud Program and Google Black Founders Fund have been invaluable to our success. Since completing the Black Founders Accelerator program, we’ve been named a top 10 HR tech product by HR Tech Outlook, significantly increased our subscriber base, and received positive feedback from investors. We can’t wait to see what we accomplish next as we empower tech companies to build more diverse, inclusive, and sustainable workforces.

If you want to learn more about how Google Cloud can help your startup, visit our page here to get more information about our program, and sign up for our communications to get a look at our community activities, digital events, special offers, and more.