In the last blog post, we discussed why legacy data warehouses are not cutting it any more and why organizations are moving their data warehouses to cloud.

At GCP, we often hear that customers feel that migration is an uphill battle because the migration strategy was not deliberately considered. 

Migrating to a modern data warehouse from a legacy environment can require a massive up-front investment in time and resources. There’s a lot to think about before and during the process, so your organization has to take a strategic approach to streamline the process.

At Google Cloud, we work with enterprises shifting data to our BigQuery data warehouse, and we’ve helped companies of all kinds successfully migrate to cloud. Here are some of the questions we frequently hear around migrating a data warehouse to the cloud:

• How do we minimize any migration risks or security challenges?
• How much will it cost?
• How do we migrate our data to the target data warehouse?
• How quickly will we see equal or better performance?

These are big, important questions to ask—and have answered—when you’re starting your migration. Let’s take them in order.

How do we minimize any migration risks or security challenges?
It’s easy to consider an on-premises data warehouse secure because, well, it’s on-site and you can manage its data protection. But if scaling up an on-prem data warehouse is difficult, so is securing it as your business scales. 

We’ve built in multiple features to secure BigQuery. For enterprise users, Cloud Identity and Access Management (Cloud IAM) is key to setting appropriate role-based user access to data.

You can also take advantage of SQL’s security views within BigQuery. And all BigQuery data is encrypted at rest and in transit.

You can add the protection of customer-managed encryption keys to establish even stronger security measures. Using virtual private cloud (VPC) security controls can secure your migration path, since it helps reduce data exfiltration risks. 

How much will it cost?
The cost of a cloud data warehouse has a different structure from what you’re likely used to with a legacy data warehouse. An on-prem system like Teradata may depend on your IT team paying every three years for the hardware, then paying for licenses for users who need to access the system. Capacity increases come at an additional cost outside of that hardware budget.

With cloud, you’ve got a lot more options for cost and scale. Instead of a fixed set of costs, you’re now working on a price-utility gradient, where if you want to get more out of your data warehouse, you can spend more to do so immediately, or vice versa. While cloud data warehouses help reduce or eliminate capital and fixed costs, they are not all the same.

You’ll find varying levels of simplicity and cost savings across vendors, so it’s important to check out the operational costs of each data warehouse in relation to its performance. 

With a cloud data warehouse like BigQuery, TCO becomes an important metric for customers when they’ve migrated to BigQuery (check out ESG’s report on that), and Google Cloud’s flexibility makes it easy to optimize costs.

How do we migrate all of our data to the target data warehouse?
This question encompasses both migrating your extract, transform, load (ETL) jobs and SAS/BI application workloads to the target data warehouse, as well as migrating all your queries, stored procedures, and other extract, load, transform (ELT) jobs.

Actually getting all of a company’s data into the cloud can seem daunting at the outset of the migration journey. We know that most businesses have a lot of siloed data. That might be multiple data lakes set up over the years for various teams, or systems acquired through acquisition that handle just one or two crucial applications. You may be moving data from an on-prem or cloud data warehouse to BigQuery and type systems or representations don’t match up.

One big step you can take to prepare for a successful migration is to do some workload and use case discovery.

That might involve auditing which use cases exist today and whether those use cases are part of a bigger workload, as well as identifying which datasets, tables, and schemas underpin each use case.

Use cases will vary by industry and by job role. So, for example, a retail pricing analyst may want to analyze past product price changes to calculate future pricing. Use cases may include the need to ingest data from a transactional database, transforming data into a single time series per product, storing the results in a data warehouse table, and more. 

After the preparation and discovery phase, you should assess the current state of your legacy environment to plan for your migration. This includes cataloging and prioritizing your use cases, auditing data to decide what will be moved and what won’t, and evaluating data formats across your organization to decide what you’ll need to convert or rewrite.

Once that’s decided, choose your ingest and pipeline methods. All of these tasks take both technology and people management, and require some organizational consensus on what success will look like once the migration is complete. 

How quickly will we see equal or better performance?
Managing a legacy data warehouse isn’t usually synonymous with speed. Performance often comes at the cost of capacity, so users can’t do the analysis they need till other queries have finished running.

Reporting and other analytics functions may take hours or days, which is especially true for running large reports with a lot of data, like an end-of-quarter sales calculation. As the amount of data and number of users rapidly grows, performance begins to melt down and organizations often face disruptive outages.

However, with a modern cloud data warehouse like BigQuery, compute and storage are decoupled, so you can scale immediately without facing capital infrastructure constraints. 

BigQuery helps you modernize because it uses a familiar SQL interface, so users can run queries in seconds and share insights right away. Home Depot is an example of a customer that migrated their warehouse and reduced eight-hour workloads to five minutes. 

Moving to cloud may seem daunting, especially when you’re migrating an entrenched legacy system. But it brings the benefits of adopting technology that lets the business grow, rather than simply adopting a tool. It’s likely you’ve already seen that the business demand exists. Now it’s time to stop standing in the way of that demand and instead make way for growth.

Editor’s note: Streak makes a CRM add-on for Gmail, and recently adopted Cloud Spanner to take advantage of its scalability and SQL capabilities to implement a graph data model. Read on to learn about their decision, what they love about the system, and the ways in which it still needs work.]  

Streak is a customer relationship management (CRM) tool built directly into Gmail. It is used for sales, marketing, hiring, and just about anything else you can think of.

We built it because out of the box, email is actually a really crummy team sharing system. By adding a layer of organization on top of email, Streak lets you add email threads directly into its spreadsheet view, making it useful as a workflow tool with capabilities including task creation, email template management, and easy data entry.

Streak has been integrated with G Suite (originally Google Apps For Your Domain) since its inception so when choosing a cloud, it made sense to colocate our server stack with Google Cloud.

Likewise, Streak was on Google App Engine from the start, and we slowly added other GCP services as the offerings improved or as our use cases became more complex.

In addition to App Engine, we use Google Kubernetes Engine to run a bunch of our compute workload, including both application servers and our offline processes like indexers and task queue consumers.

We use Cloud Dataflow for both streaming event processing and logs ETL, and BigQuery for all of our analytics queries. We use Cloud Pub/Sub for interacting with the Gmail watch API, as well as Stackdriver (logging, tracing, monitoring, errors) and OpenCensus to dig into any operational issues as they arise.

Then, on the database front, we recently started using Cloud Spanner, Google Cloud’s scalable relational database service. Before that, we stored most of our business data in Cloud Datastore, Google Cloud’s NoSQL document database.

Partially, that was historical, since Cloud Datastore was GCP’s only managed database when we wrote the Streak backend. And we’ve been very happy with how easy Cloud Datastore is to maintain. Between Google App Engine and Cloud Datastore, we’ve never had to have an explicit infrastructure on-call rotation.

But as more users rely on Streak to collaborate with larger and larger teams, we were feeling the pain of not having a fully relational database. We found ourselves having to manually join data in our application, which increased application latency and increased the time developers spent coding workarounds and debugging that complexity.

We found we needed two things out of our database: a scalable relational store and a graph store that could power next-gen Streak features. At the same time, we wanted a single database that could handle both use cases and wouldn’t increase our operational burden. This meant finding a managed service to give us more query flexibility, so we decided to give Cloud Spanner a try.

Of course, we didn’t want to migrate our existing stack to a new data platform without first testing it out (never a smart strategy). But since most of our existing data model required transactional updates with other entities, pulling out a single entity to test was challenging. We did have a feature in our pipeline that necessitated a graph data store and that was removed from our other data: our email metadata indexing system.

How your client software handles email metadata indexing can make or break the useability of a system.  Think about how many times somebody forgets to reply-all or that you receive a forwarded thread with thirty emails in reverse-chronological order. Within our own inboxes, we rely on Gmail’s UI to nicely organize email threads, but that organization breaks down when working with a team or across organizational boundaries.

We decided to fix that in the Streak product by organizing metadata (i.e., headers but not message content) from users’ email by using Cloud Spanner as a graph database. Using a graph database lets us answer questions like “What are all the emails on this thread in the inboxes of everybody on my team?” and “Who on my team has previously talked with the organization that this prospect works at?”

In our model, the nodes of the graph are either an email message, a person (email address) or a company (a domain). Then we have four different types of “edges”— properties by which nodes in a graph connect to one another:

1. Message to message (thread): messages that are on the same thread have an edge between them. The reason we do this is because we want to show users a list of threads to answer their questions, not messages, so we need to be able to get the spanning set of messages.
2. Message to message (same RFC id): A core value proposition of Streak is being able to see the “unified” version of a thread that shows each person on a team’s version of the email thread. To make sure we are getting each user’s version of a thread when we issue a query, there needs to be an edge between a message in the queryer’s inbox and the same message in their team’s inbox. In case you’re curious, Streak uses the RFC message id to determine that two messages across inboxes are actually the same.
3. Email address to message: a message has an edge to an email address if it was either the from, to, cc, or bcc on the message. This edge is crucial for queries that start with: “Show me all threads between this person and our team.”

Domain to message: a message has an edge to a domain if the domain is present in any of the from, to, cc, or bcc addresses on the message. This edge is similarly used for queries that start with “Show me all threads between this company and our team.”

Using Cloud Spanner’s distributed SQL capabilities and scalability to build a graph database also let us answer the important follow-up question: “Which threads have I been granted permission to view?” And while a lot of these questions could be answered per-user by a traditional relational database, scale limitations have to be taken into consideration, especially as we plan for 10x or more data volume growth as both our user base grows and as their inboxes accumulate more emails. A graph database model is simply a better fit for Streak’s collaboration model with many-to-many mappings between users and teams, and will allow us to query the data in any number of configurations, without worrying about scale limitations or having to manually shard a relational database. Cloud Spanner gives us queryability and scalability.

Taking the Cloud Spanner plunge

With so many advantages to it, we went ahead and began building out our metadata system with Cloud Spanner as a back-end.

Adopting Cloud Spanner has been great. Here are some of the high points:

1. The fast distributed queries and transactions are absolutely real. We have global indexes across our entire dataset and we haven’t had to spend very much time at all thinking about co-locating data. In particular, we only use interleaved tables for values that would be repeated fields in Cloud Datastore, and that hasn’t been a problem for us yet.
2. We haven’t had any reliability problems whatsoever, despite averaging 20K writes/sec in steady state.
3. Once we optimized our queries on realistic data, Cloud Spanner scaled up in a surprisingly predictable way. You need to run queries after you’ve populated data, do the explain to figure out how the query planner is executing the query, and add indexes/modify queries to make sure they’re performant.
4. Compared to the hoops some traditional relational databases make you jump through, Cloud Spanner’s online schema changes and index builds are magical. There is no downtime for these operations.

Overall, the experience has been encouraging, and we’re planning to move 20 TB of existing data in Cloud Datastore to Cloud Spanner as well. We built out an ORM library for Java on top of Cloud Spanner called Ratchet and are testing a framework for dual-writing entities to both Cloud Datastore and Cloud Spanner to support the rest of the migration. We now store about 40 TB of email metadata in Cloud Spanner, which makes us a large user of Cloud Spanner.

In short, if you’re starting to outgrow your NoSQL database, and want to move to a managed SQL database, give Cloud Spanner a try. You definitely want to model out your costs and try out a proof of concept, both to see how it works on your workload and to get familiar with the quirks of the system. But you don’t need to spend much time worrying about the reliability of the product: it’s there.

May is Asian American Pacific Islander Heritage Month — a time for us to come together to celebrate and remember the important people and history of Asian and Pacific Island heritage. This feature highlights founder Georgene Huang and how her startup Fairygodboss uses easy to use Google Cloud tools to grow and innovate. 

Coming from a male-dominated industry like Wall Street, I never thought much about my gender. I was so focused on work that I didn’t feel like I could focus on my identity as a woman of color. That all changed when I found myself thrust into the job market while two months pregnant.

Traditionally, job-seeking sites are built by – and geared towards – men and their professional needs. There was no recruiting site that broke down what candidates who identify as women might expect from a company, let alone women of color like myself. I founded Fairygodboss to create a space where women can crowdsource information about opportunities and employers in order to make informed career decisions. 

Fairygodboss 

Fair•y•god•boss (noun): A person who elevates women at work.

Fairygodboss is the largest career community for women. In researching potential employers, I had many questions surrounding company culture, maternity leave, and women in leadership positions. While sites like Glassdoor provide high-level details for job seekers, I wanted more concrete examples and data than general salary information and anecdotes about work-life balance. 

Fairygodboss began as a job review site geared towards women, but has expanded significantly since our initial launch. In addition to providing free resources like career connections, job listings, virtual recruiting events, community advice, and real research on how companies treat women, we also proactively help companies improve gender diversity. Fairygodboss partners with hundreds of top employers to create a 360-degree recruiting and employer branding program designed to drive female applicants to job postings by showcasing a company’s track record of women in leadership within a company, and their diversity investments. 

Google Cloud + Fairygodboss: Data defined 

At our core, Fairygodboss is a data-driven company, and we rely on Google Cloud to help us process, store, and analyze user-driven events occurring on the site. As a highly scalable, serverless, and cost-effective data warehousing solution, BigQuery collect millions of events each day, helping us identify where traffic is coming from (and where it goes next), and it produces actionable insights to provide better recommendations, information, and opportunities for women visiting our platform. 

After seamlessly connecting our BigQuery data to Data Studio, we have been able to visualize and make sense of all the data we receive on our platform. Unlike some other data visualization tools, Data Studio allows us to analyze data in real time. With tools like BigQuery, we are now able to identify trends and gain a deep understanding for how our users interact with our site. 

Google for Startups Accelerator: Women Founders 

In order to provide more accurate job recommendations for our users, the Fairygodboss team wanted to build a machine learning prototype to classify our users and jobs into categories to facilitate more effective matching. So we applied for the Google for Startups Accelerator: Women Founders, a three-month digital accelerator program for high-potential Seed to Series A tech startups based in the U.S. and Canada. Along with tailored mentorship and product support from Googlers and industry experts, the Google for Startups Accelerator provided $100K in Google Cloud credits to scale our businesses. The mentorship we received from the Google Cloud technical experts as part of the Accelerator – special shout out to Peter Novig! – empowered us to integrate Cloud AutoML into our systems and ultimately curate more accurate job suggestions for our users.  

Not only did the Google for Startups Accelerator program help to achieve our business goals for the year, it was also extremely beneficial to be connected with a cohort of other women founders of color. While we are all building different kinds of businesses across different industries, the guidance around fundraising, scaling teams, and coping with the struggles of being a founder rang true for all of us. 

Advancing the community

It can be daunting to launch your own business – and even more so as an AAPI woman. Throughout my career, I am certain I have experienced unconscious bias around my race. Specifically, there have likely been “model minority” stereotypes about my demeanor and math abilities, assumptions that I may be mild-mannered or agreeable. While frustrating, these unfair tropes actually inspired me to see myself beyond others’ perceptions. Why be either analytical or creative, meek or brash, inspiring or agreeable? It’s the ors of life that prevent us from seeing ourselves as truly multi-dimensional individuals. I aspired to be more instead of or, and want my company to be as well. 

As an inclusion-focused business, it is extremely important that Fairygodboss mindfully engage with all underrepresented groups. Diversity can be sliced in many different ways, and these intersections lead to great opportunities for change. We have a team of volunteer ‘culture wizards’ at work that provide educational resources and videos for those wanting to learn more about different cultures and identities from members of under-represented groups. We also practice what we preach at Fairygodboss by prioritizing diverse slates and diversity in our own hiring process. We aim for the talent we hire to be representative of the diverse communities we support. It is imperative to take an intentional approach to hiring diverse talent, as it falls on all employers to promote equity and inclusion—there are always ways in which we can all improve. Fairygodboss looks forward to evolving with Google Cloud in 2021 and beyond as we work together to foster inclusive teams around the world. 

If you want to learn more about how Google Cloud can help your startup, visit our Startup Program application page here and sign up for our monthly startup newsletter to get a peek at our community activities, digital events, special offers, and more.

Today, more and more applications are being deployed in containers on Kubernetes—so much so that we’ve heard Kubernetes called the Linux of the cloud.

Despite all that growth on the application layer, the data layer hasn’t gotten as much traction with containerization. That’s not surprising, since containerized workloads inherently have to be resilient to restarts, scale-out, virtualization, and other constraints. So handling things like state (the database), availability to other layers of the application, and redundancy for a database can have very specific requirements. That makes it challenging to run a database in a distributed environment. 

However, the data layer is getting more attention, since many developers want to treat data infrastructure the same as application stacks.

Operators want to use the same tools for databases and applications, and get the same benefits as the application layer in the data layer: rapid spin-up and repeatability across environments. In this blog, we’ll explore when and what types of databases can be effectively run on Kubernetes.

Before we dive into the considerations for running a database on Kubernetes, let’s briefly review our options for running databases on Google Cloud Platform (GCP) and what they’re best used for.

• Fully managed databases. This includes Cloud Spanner, Cloud Bigtable and Cloud SQL, among others. This is the low-ops choice, since Google Cloud handles many of the maintenance tasks, like backups, patching and scaling. As a developer or operator, you don’t need to mess with them. You just create a database, build your app, and let Google Cloud scale it for you. This also means you might not have access to the exact version of a database, extension, or the exact flavor of database that you want.
• Do-it-yourself on a VM. This might best be described as the full-ops option, where you take full responsibility for building your database, scaling it, managing reliability, setting up backups, and more. All of that can be a lot of work, but you have all the features and database flavors at your disposal.
• Run it on Kubernetes. Running a database on Kubernetes is closer to the full-ops option, but you do get some benefits in terms of the automation Kubernetes provides to keep the database application running. That said, it is important to remember that pods (the database application containers) are transient, so the likelihood of database application restarts or failovers is higher. Also, some of the more database-specific administrative tasks—backups, scaling, tuning, etc.—are different due to the added abstractions that come with containerization.

Tips for running your database on Kubernetes

When choosing to go down the Kubernetes route, think about what database you will be running, and how well it will work given the trade-offs previously discussed.

Since pods are mortal, the likelihood of failover events is higher than a traditionally hosted or fully managed database. It will be easier to run a database on Kubernetes if it includes concepts like sharding, failover elections and replication built into its DNA (for example, ElasticSearch, Cassandra, or MongoDB). Some open source projects provide custom resources and operators to help with managing the database.

Next, consider the function that database is performing in the context of your application and business. Databases that are storing more transient and caching layers are better fits for Kubernetes. Data layers of that type typically have more resilience built into the applications, making for a better overall experience.  

Finally, be sure you understand the replication modes available in the database. Asynchronous modes of replication leave room for data loss, because transactions might be committed to the primary database but not to the secondary database(s). So, be sure to understand whether you might incur data loss, and how much of that is acceptable in the context of your application.

After evaluating all of those considerations, you’ll end up with a decision tree looking something like this:

How to deploy a database on Kubernetes

Now, let’s dive into more details on how to deploy a database on Kubernetes using StatefulSets.

With a StatefulSet, your data can be stored on persistent volumes, decoupling the database application from the persistent storage, so when a pod (such as the database application) is recreated, all the data is still there.

Additionally, when a pod is recreated in a StatefulSet, it keeps the same name, so you have a consistent endpoint to connect to. Persistent data and consistent naming are two of the largest benefits of StatefulSets. You can check out the Kubernetes documentation for more details.

If you need to run a database that doesn’t perfectly fit the model of a Kubernetes-friendly database (such as MySQL or PostgreSQL), consider using Kubernetes Operators or projects that wrap those database with additional features. Operators will help you spin up those databases and perform database maintenance tasks like backups and replication. For MySQL in particular, take a look at the Oracle MySQL Operator and Crunchy Data for PostgreSQL. 

Operators use custom resources and controllers to expose application-specific operations through the Kubernetes API. For example, to perform a backup using Crunchy Data, simply execute pgo backup [cluster_name]. To add a Postgres replica, use pgo scale cluster [cluster_name].

There are some other projects out there that you might explore, such as Patroni for PostgreSQL. These projects use Operators, but go one step further. They’ve built many tools around their respective databases to aid their operation inside of Kubernetes. They may include additional features like sharding, leader election, and failover functionality needed to successfully deploy MySQL or PostgreSQL in Kubernetes.

While running a database in Kubernetes is gaining traction, it is still far from an exact science. There is a lot of work being done in this area, so keep an eye out as technologies and tools evolve toward making running databases in Kubernetes much more the norm. 

When you’re ready to get started, check out GCP Marketplace for easy-to-deploy SaaS, VM, and containerized database solutions and operators that can be deployed to GCP or Kubernetes clusters anywhere.