Isaac Newton said, “If I have seen farther than others it is because I have stood on the shoulders of giants.” He meant that in order to explain the Law of Gravity, he used the work of major thinkers who came before him in order to make intellectual progress (as-in giving credit). In the 1640’s (yes, the time of the English Civil War and the start of the mini Ice Age), we can say the Age of Reason began and the word “data” was also (re)born in a way.

Why? Hard to assert a specific reason, it could be apropos of all those events of that decade, and somewhere amidst all that, the scientists and Churchmen (rather Churchman, Henry Hammond who really coined the term data) started to pen down their credit in books and there was a continued proliferation of data to reason their finding (and most times to reason why their work was better than that of the others). And thus comes to us the word datum from the Latin verb dare (it means “to give”, not the English dare).

Dare to Recap History?

Data is history captured through language (it has become the future as well, but that is for another day). Now we all like history (well, most of us). But it is highly likely the context gets lost in the complexity and style of definition. One way to mitigate that risk is to have a clear set of definitions (language), sustained hold of events (history), a clean process of capture (extract) and a scalable process for translation and aggregation (transform). If we want our data to be successful and rise to the occasion, then we need to keep these ways to mitigate the risk of complexity in mind.

And this is exactly what we discussed in the Part 1 of this blog series, Data Modeling Basics—the various business attributes, technical aspects, design questions, and considerations for designing your database model.

In this blog…

We will look into the different databases and storage options in Google Cloud, a brief note on each one of them, when to choose one over the other, interesting alternatives, exceptions and if you make it to the end of the blog, a fun challenge to make sure we put this little tech nugget to an ACID test (see what I did there?). If you are a cloud enthusiast, a database practitioner, a data geek, or a general wonderer of life with computing, you may find this engaging…

Google Cloud Storage Options

We at Google Cloud, have realized how hard it is to go through these laundry list assessment aspects and have made it simpler for you with a Decision Tree. (Of course, It ain’t Christmas if not for the tree):

If only the world was always “Structured”

In a structured world, you will know all the attributes on a first-name basis (I mean to say that you will have a well defined fixed set of attributes that can be modeled in a table of rows and columns), and the applications are transactional or analytical in orientation. Transactional Structured Data operate one row at a time generally and they need to adhere to ACID compliance. (Ah. Now you connect the dots, if not already.) ACID properties are Atomicity, Consistency, Isolation, and Durability. Cloud SQL and Cloud Spanner are our Google Cloud choices for Transactional Structured Data use cases.

Let’s look at the below aspects for each type and structure of data:

• Why that option? (highlights and key features)
• When to choose?
• When not to choose?
• Security aspects

Cloud SQL

• Fully Managed, cloud-native RDBMS (Relational DataBase Management System) that offers both MySQL, PostgreSQL, SQL Server engines
• Cloud SQL is accessible from apps running on App Engine, GKE, or Compute Engine

Note: A managed database is one that does not require as much administration and operational support (creating databases, performing backups, updating the operating system of database instances) as an unmanaged database.

When to use Cloud SQL?

• Typical online transaction processing (OLTP) workloads
• Lift and shift of on-premise SQL databases (or from anywhere else) to cloud
• Regional applications that do not need to store > 30 TB of data in a single instance

When not to use Cloud SQL?

Cloud SQL is not an appropriate storage system for online analytical processing (OLAP) workloads or data that requires dynamic schemas on a per-object basis.

Security

Data stored is encrypted both in transit and at rest. Have built-in support for access control, using network firewalls to manage database access.

Cloud Spanner

• 
  Relational, horizontally scalable, global database with strong consistency
• Supports schemas, ACID transactions, and SQL queries (ANSI 2011)
• Scales horizontally in regions, but can also scale across regions for workloads that have more stringent availability requirements

When to use Cloud Spanner?

• For large amounts of data and when you require high transactional consistency
• When you require sharding for higher throughput, access and low latency

When not to use Cloud Spanner?

Cloud Spanner is not an appropriate storage system for online analytical processing (OLAP) workloads

Security

Security features in Spanner include data-layer encryption, audit logging, and Identity and Access Management (IAM) integration.

Analytical Structure is when we want the data to tell us an aggregated or enhanced story, for which we use limited columns and multiple rows and hence mostly use a Column-Oriented storage mechanism. Column-oriented storage is if we want to store the data in the tables by columns instead of by rows, and this column-oriented storage is done to efficiently access only a subset of columns for querying. BigQuery is the data warehouse option for analytics needs.

BigQuery

• 
  BigQuery is a fully managed Data Warehouse for analytics with built-in data transfer service
• Peta-byte scale, low-cost warehouse that supports loading data through the web interface, command line tools, and REST API calls
• Incorporates features for machine learning, business intelligence, and geospatial analysis that are provided through BigQuery ML, BI Engine, and GIS.

Note: A data warehouse stores large quantities of data for query and analysis instead of transactional processing.

When to use BigQuery?

For use cases that cover process analytics and optimization, big data (Petabyte scale) processing and analytics, data warehouse modernization, machine learning-based behavioral analytics, and predictions

When not to use BigQuery?

BigQuery is not a Transactional database and is oriented on running analytical queries, not for simple CRUD operations and queries.

Security

BigQuery provides encryption at rest and in transit. Cloud Data Loss Prevention (Cloud DLP) can be used to scan the BigQuery tables and to protect sensitive data and meet compliance requirements. BigQuery supports access control of datasets and tables using Identity and Access Management (IAM).

And then we have the Semi-structured and the Unstructured world of data that we will address in the below sections.

Cloud Firestore (Cloud Datastore)

Firestore is the next major version of Datastore and a re-branding of the product. Taking the best of Datastore and the Firebase Realtime Database, Firestore is a NoSQL document database built for automatic scaling, high performance, and ease of application development.

• A fully managed, serverless NoSQL Google Cloud database designed for the development of serverless apps that stores JSON data
• Can be used to store, sync, and query data for web, mobile, and IoT applications
• Automatically handles sharding and replication making it highly available, durable, and scalable
• Provides ACID transactions, SQL-like queries, indexes, and more
• If a client does not have network connectivity, the Firestore API lets your app persist data to a local disk and synchronizes itself with the current server state once connectivity is reestablished

When to use?

For use cases of app development, live synchronization, offline support, multi-user collaborative applications, leader board, etc.

When not to use?

Not a relational database so not meant for relational structured data use cases.

Security

Firestore Security Rules support serverless authentication and authorization for the mobile and web client libraries. Identity and Access Management (IAM) manages database access.

Cloud Bigtable

• Bigtable is a wide-column, fully managed, high-performance NoSQL database service designed for terabyte- to petabyte-scale workloads
• Bigtable is battle tested on Google internal Bigtable database infrastructure that powers Google Search, Google Analytics, Google Maps, and Gmail
• Provides consistent, low-latency, and high-throughput storage for large-scale NoSQL data

When to use?

• For large amounts of single key data and is preferable for low-latency, high throughput workloads
• For real-time app serving workloads and large-scale analytical workloads

When not to use?

While Bigtable is considered an OLTP system, it doesn’t support multi-row transactions, SQL queries or joins. For those use cases, consider either Cloud SQL or Datastore.

Security

• All the data at rest in Cloud Bigtable is encrypted using Google’s default encryption, by default.
• Instead of Google managing the encryption keys that protect your data, your Bigtable instance can also be protected using a key that you manage (customer-managed encryption keys (CMEK)) in Cloud Key Management Service (Cloud KMS).

Cloud Storage

• Google Cloud Storage is an object storage system that is durable and highly available, persists unstructured data like images, videos, data files, videos, backup, and other data
• It is unstructured and so the files in the cloud storage are atomic that you read the entire file but you cannot access specific blocks in the files
• Cloud Storage is available in multiple classes, depending on the availability and performance required for apps and services
• Standard – Offers the highest levels of availability and is appropriate for storing data that requires low-latency access
  Nearline – Low-cost, highly durable, fast-access storage service for storing data that you access less than once per month
  Coldline – Very-low-cost, highly durable, fast-access storage service for storing data that you intend to access less than once per quarter
  Archive – Lowest-cost, highly durable, fast-access storage service for storing data that you intend to access less than once per year
  

Security

Files in Cloud Storage are organized by project into individual buckets. These buckets can support either custom access control lists (ACLs) or centralized identity and access management (IAM) controls.

Firebase Realtime Database

• Firebase is a realtime, NoSQL, Google Cloud database that is a part of the Firebase platform that allows you to store and sync data in real-time and includes caching capabilities for offline use
• Data is stored as JSON and synchronized in real-time to every connected client and remains available when app goes offline

When to use?

For mobile and web app development, development of apps that work across devices

When not to use?

Not in relational dataset use cases. The Realtime Database is a NoSQL database and as such has different optimizations and functionality compared to a relational database. The Realtime Database API is designed to only allow operations that can be executed quickly.

Security

The Realtime Database provides a flexible, expression-based rules language, called Firebase Realtime Database Security Rules, to define how your data should be structured and when data can be read from or written to. When integrated with Firebase Authentication, developers can define who has access to what data, and how they can access it.

That’s a rather packed read. But I hope you find this useful to understand comprehensively the basics of data, storage options and databases in Google Cloud Platform.

Next Steps, before I go…

In the blog part 1 of the series, I ended with an action item – “How would you model a NoSQL solution for an application that needs to query the lineage between individual entities that are represented in pairs?”.

Well, my answer is Firestore. As part of this episode, why don’t you take some time to go over the options and key aspects that attribute to this.

As the Olympics kicked off in Tokyo at the end of July, we found ourselves reflecting on the beauty of diverse countries and cultures coming together to celebrate greatness and sportsmanship. For this month’s blog, we’d like to highlight some key data and analytics performances that should help inspire you to reach new heights in your data journey.

Let’s review the highlights!

BigQuery ML Anomaly Detection: A perfect 10 for augmented analytics

Identifying anomalous behavior at scale is a critical component of any analytics strategy. Whether you want to work with a single frame of data or a time series progression, BigQuery ML allows you to bring the power of machine learning to your data warehouse. 

In this blog released at the beginning of last month, our team walked through both non-time series and time-series approaches to anomaly detection in BigQuery ML:

• Non-time series anomaly detection
  • Autoencoder model (now in Public Preview)
  • K-means model (already in GA)
• Time-series anomaly detection
  • ARIMA_PLUS time series model (already in GA)

These approaches make it easy for your team to quickly experiment with data stored in BigQuery to identify what works best for your particular anomaly detection needs. Once a model has been identified as the right fit, you can easily port that model into the Vertex AI platform for real-time analysis or schedule it in BigQuery for continued batch processing.

App Analytics: Winning the team event

Google provides a broad ecosystem of technologies and services aimed at solving modern day challenges. Some of the best solutions come when those technologies are combined with our data analytics offerings to surface additional insights and provide new opportunities. 

Firebase has deep adoption in the app development community and provides the technology backbone for many organization’s app strategy. This month we launched a design pattern that shows Firebase customers how to use Crashlytics data, CRM, issue tracking, and support data in BigQuery and Looker to identify opportunities to improve app quality and enhance customer experiences.

Crux on BigQuery: Taking gold in the all-around data competition

Crux Informatics provides data services to many large companies to help their customers make smarter business decisions. While they were already operating on a modern stack and not on the hunt for a modern data warehouse, BigQuery became an enticing option due to performance and a more optimal pricing model. Crux also found advantages with lower-cost ingestion and processing engines like Dataflow that allow for streaming analytics.… when it came to building a centralized large-scale data cloud, we needed to invest in a solution that would not only suit our current data storage needs but also enable us to tackle what’s coming, supporting a massive ecosystem of data delivery and operations for thousands of companies.Mark Etherington
Chief Technology Office, Crux Informatics

Technology is a team sport, and Crux found our support team responsive and ready to help. This decision to more deeply adopt Google Cloud’s data analytics offerings provides Crux with the flexibility to manage a constantly evolving data ecosystem and stay competitive.

You can read more about Crux’s decision to adopt BigQuery in this blog.

Google Trends: A classic emerges a champion

Following up on the launch of our Google Trends dataset in June, we delivered some examples of how to use that data to augment your decision making. 

As a quick recap of that dataset, Google Cloud, and in particular BigQuery, provide access to the top 25 trending terms by Nielsen’s Designated Market Area® (DMA) with a weekly granularity. These trending terms are based on search patterns and have historically only been available on the Google Trends website.https://www.youtube.com/embed/9FJAXMF0ASc?enablejsapi=1&

The Google Trends design pattern addresses some common business needs, such as identifying what’s trending geographically near your stores and how to match trending terms to products to identify potential campaigns. 

Dataflow GPU: More power than ever for those streaming sprints

Dataflow is our fully-managed data processing platform that supports both batch and streaming workloads. The ability of Dataflow to scale and easily manage unbounded data has made it the streaming solution of choice for large workloads with high-speed needs in Google Cloud. 

But what if we could take that speed and provide even more processing power for advanced use cases? Our team, in partnership with NVIDIA, did just that by adding GPU support to Dataflow. This allows our customers to easily accelerate compute-intensive processing like image analysis and predictive forecasting with amazing increases in efficiency and speed. 

Take a look at the times below:

Data Fusion: A play-by-play for data integration’s winning performance

Data Fusion provides Google Cloud customers with a single place to perform all kinds of data integration activities. Whether it’s ETL, ELT, or simply integrating with a cloud application, Data Fusion provides a clean UI and streamlined experience with deep integrations to other Google Cloud data systems. Check out our team’s review of this tool and the capabilities it can bring to your organization.

When you’re serving customers online, speed is essential for a good experience. As the amount of data in a database grows, queries that used to be fast can slow down.

For example, if a query has to scan every row because a table is missing an index, response times that were acceptable with a thousand rows can turn into multiple seconds of waiting once you have a million rows.

If this query is executed every time a user loads your web page, their browsing experience will slow to a crawl, causing user frustration. Slow queries can also impact automated jobs, causing them to time out before completion.

If there are too many of these slow queries executing at once, the database can even run out of connections, causing all new queries, slow or fast, to fail. 

The popular open-source databases MySQL and Google Cloud Platform‘s fully managed version, Cloud SQL for MySQL, include a feature to log slow queries, letting you find the cause, then optimize for better performance.

However, developers and database administrators typically only access this slow query log reactively, after users have seen the effects and escalated the performance degradation.

With Stackdriver Logging and Monitoring, you can stay ahead of the curve for database performance with automatic alerts when query latency goes over the threshold, and a monitoring dashboard that lets you quickly pinpoint the specific queries causing the slowdown.

To get started, import MySQL’s slow query log into Stackdriver Logging.

Once the logs are in Stackdriver, it’s straightforward to set up logs-based metrics that can both count the number of slow queries over time, which is useful for setting up appropriate alerts, and also provide breakdowns by slow SQL statement, allowing speedy troubleshooting.

What’s more, this approach works equally well for managed databases in Cloud SQL for MySQL and for self-managed MySQL databases hosted on Compute Engine. 

For a step-by-step tutorial to set up slow query monitoring, check out Monitoring slow queries in MySQL with Stackdriver. For more ideas about what else you can accomplish with Stackdriver Logging, check out Design patterns for exporting Stackdriver Logging.

Unlocking the full potential of data requires breaking down the silo between open-source data formats and data warehouses. At the same time, it is critical to enable data governance team to apply policies regardless of where the data happens, whether – on file or columnar storage.

Today, data governance teams have to become subject matter experts on each storage system the corporate data happens to reside on. Since February 2022, Dataplex has offered a unified place to apply policies, which are propagated across both lake storage and data warehouses in GCP. Rather than specifying policies in multiple places, bearing the cognitive load of translating policies from “what you want the storage system to do” to “how your data should behave” Dataplex offers a single point for unambiguous policy management. Now, we are making it easier for you to use BigLake.

Earlier this year, we launched BigLake into general availability, BigLake unifies data fabric between Data Lakes and Data Warehouses by extending BigQuery storage to open file formats. Today, we announce BigLake Integration with Dataplex (available in preview). This integration eliminates the configuration steps for the admin taking advantage of BigLake and managing policies across GCS and BigQuery from a unified console.

Previously, you could point Dataplex at a Google Cloud Storage (GCS) bucket, and Dataplex will discover and extract all metadata from the data lake and register this metadata in BigQuery (and Dataproc Metastore, Data Catalog) for analysis and search. With the BigLake integration capability, we are building on this capability by allowing an “upgrade” of a bucket asset, and instead of just creating external tables in BigQuery for analysis – Dataplex will create policy-capable BigLake tables!

The immediate implication is that admins can now assign column, row, and table policies to the BigLake tables auto-created by Dataplex, as with BigLake – the infrastructure (GCS) layer is separate from the analysis layer (BigQuery). Dataplex will handle the creation of a BigQuery connection and a BigQuery publishing dataset and ensure the BigQuery service account has the correct permissions on the bucket.

But wait – there’s more.

With this release of Dataplex, we are also introducing advanced logging called governance logs. Governance logs allow tracking the exact state of policy propagation to tables and columns – adding an additional level of detail going beyond the high-level “status” for the bucket and into fine-grained status and logs for tables, columns.

What’s next?

• We have updated our documentation for managing buckets and have additional detail regarding policy propagation and the upgrade process.
• Stay tuned for an exciting roadmap ahead, with more automation around policy management.

For more information, please visit:

• Google Cloud Dataplex

Editor’s note: In honor of Mother’s Day, we look at how 1-800-FLOWERS.COM, Inc. migrated to Google Cloud as part of its digital transformation to quickly deploy seamless and convenient customer experiences across multiple brands on Mother’s Day and every day. 

As a leading provider of gifts designed to help customers express, connect, and celebrate,  1-800-FLOWERS.COM, Inc. has embraced cloud technologies to grow and transform its business through constant innovation. As part of our digital transformation, we recently completed the migration of our ecommerce platform and other services to Google Cloud. We’ve transitioned from a monolithic to a microservices platform, moved many workloads from our on-premises data centers to our Google Cloud environment, and scaled both horizontally and vertically. 

Since our migration, we’ve developed efficient processes to launch new brands, improved the customer experience across all brands, and seen significantly increased site traffic. 

Nurturing a more delightful customer journey

Customer delight is at the core of everything we do. Whether it be with a flower bouquet, a sweet treat, or a personalized keepsake, our mission is to deliver smiles. With the rise of the COVID-19 pandemic, we’ve all been challenged to find unique and safe ways to continue honoring the special connections in our lives and celebrating occasions with loved ones. Our customers have adapted by doing things such as sending gifts to isolated loved ones, sharing the same meal together virtually, or using video to engage with others through group activities like flower arranging and building charcuterie boards. 

The customer experience is a top priority for us, and we constantly look for innovative ways to enhance the customer journey across our ecommerce platform of more than a dozen brands. As a result, we’ve continued to see a rise in demand as customers enjoy the ease and convenience of our site and discover our full family of brands. 

Migrating to a cloud-first mindset

As we’ve continued to innovate and iterate on the customer experience, we knew we wanted to evolve our platform. We wanted to shift to a microservices platform, which would allow our team the opportunity to release updates to our site more often and set up the right continuous integration/continuous deployment (CI/CD) practices. 

Working with Google Cloud, we were able to move our ecommerce platform to the cloud and standardize our site and brand deployment by building one release that could then be repeated across all of our brands. We built everything in a modular fashion, including microservices and      code libraries, so that sites could be easily constructed and replicated for each brand. And because of this, we were able to launch Shari’s Berries extremely quickly after we acquired the brand in 2019.   

Moving our platform to a completely homegrown solution of microservices was a daunting task. But our team handled it beautifully through load-testing, stress-testing, and building new monitoring tools. And with Google Cloud supporting us all along the way, managing the migration process was simple and easy from start to finish. 

Arranging a better bouquet of services

Currently, we’ve migrated every customer-facing touchpoint for all of our brands to Google Cloud—whether it’s on the web or mobile, our AI bots, or our chat interfaces. 

• We run on Google Kubernetes Engine and Istio.
• We have nearly 200 microservices built to help power our entire ecommerce stack across several cloud services running on Google Cloud. 
• We’re utilizing BigQuery for our offline intelligence.

Results are coming up roses

Our new stack on Google Cloud has benefits for both us and our customers. We moved from a session-based to a token-based system, which provides enhanced security as well as a consistent, convenient experience across all our brands. Using service workers and a single-page app, we are able to download all the relevant site content to the browser in under two seconds to create an instant-click experience for each and every customer. We also use Google Analytics to measure our user interactions and provide personalized results to each customer. Our hope is that with this new system, we can learn from customer behavior to offer gift givers a more personalized shopping experience during each visit. 

The benefits of our new tech stack have not only helped us enhance the solutions we offer to customers today, they’ve also enabled us to offer new ones at lightning speed. With our legacy system, we used to release new code once a week or once a month. Now, even during our peak periods, we’re able to release 10 to 15 times a day and can deploy and pivot quickly to create new microservices and microsites on the fly—often without having to touch any code. 

Efficiencies abound     

The benefits of moving our platform to Google Cloud have extended to our internal teams as well. Before the migration, we had only two environments for developing and testing, which made it time-consuming to test updates before they went into production. Now with Google Cloud, we have several different journey teams—which are made up of developers, product owners, and technical owners—all working in several different environments, solving problems, and creating new solutions together. 

Everyone is now empowered to be self-sufficient, developing and releasing microservices on their own when they’re ready. This has given our developers more time to take part in continued development and learning opportunities. For example, we offer lunch-and-learn sessions as well as other resources for everyone to take advantage of so they can continue to learn and refine their skills.

Planting the seeds for future growth

As we look to the future and think about how we help our customers express, connect, and celebrate, we’ll continue to collaborate across teams to deliver solutions that spread smiles. Specifically, we’re exploring additional use of AI to help us better serve our customers across all our brands. 

We’ve enjoyed the ongoing support we’ve received from the Google Cloud team as they help us build new solutions and design a road map for the future. Their support has helped the 1-800-FLOWERS.COM, Inc. team to realize the power of the cloud and bring the very best experience to our customers.

Learn more about 1-800-FLOWERS.COM, Inc., or check out our recent blog about cloud migration for the real world.