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.

Over the past few years, many organizations have experienced the benefits of migrating their SAP solutions to Google Cloud. But this migration can do more than reduce IT maintenance costs and make data more secure. By leveraging BigQuery, SAP customers can complement their SAP investments and gain fresh insights by consolidating enterprise data and easily extending it with powerful datasets and machine learning from Google. 

BigQuery is a leading cloud data warehouse, fully managed and serverless, and allows for massive scale, supporting petabyte-scale queries at super-fast speeds. It can easily combine SAP data with additional data sources, such as Google Analytics or Salesforce, and its built-in machine learning lets users operationalize machine learning models using standard SQL — all at a comparatively low cost. 

If your SAP-powered organization is looking to supercharge its analytics with the strength of BigQuery, read on for considerations and recommendations for modeling with SAP data. These guidelines are based on our real-world implementation experience with customers and can serve as a roadmap to the analytics capabilities your business needs.

Considerations for data replication 

Like most technology journeys, this one should start with a business objective. Keeping your intended business value and goals in mind is critical to making the right decisions in the early steps of the design process.

When it comes to replicating the data from an SAP system into BigQuery, there are multiple ways to do it successfully. Decide which method will work best for your organization by answering these questions:

• Does your business need real-time data? Will you need to time travel into past data?
• Which external datasets will you need to join with the replicated data?
• Are the source structures or business logic likely to change? Will you be migrating the SAP source systems any time soon? For instance, will you be moving from SAP ECC to SAP S/4HANA?

You’ll also need to determine whether replication should be done on a table-by-table basis or whether your team can source from pre-built logic. This decision, along with other considerations such as licensing, will influence which replication tool you should use.

Replicating on a table-by-table basis
Replicating tables, especially standard tables in their raw form, allows sources to be reused and ensures more stability of the source structure and functional output. For example, the SAP table for sales order headers (VBAK) is very unlikely to change its structure across different versions of SAP, and the logic that writes to it is also unlikely to change in a way that affects a replicated table. 

Something else to consider: Reconciliation between the source system and the landing table in BigQuery is linear when comparing raw tables, which helps avoid issues in consolidation exercises during critical business processes, such as period-end closing. Since replicated tables aren’t aggregated or subject to process-specific data transformation, the same replicated columns can be reused in different BigQuery views. You can, for instance, replicate the MARA table (the material master) once and use it in as many models as needed. 

Replicating pre-built logic
If you replicate pre-built models, such as those from SAP extractors or CDS views, you don’t need to build the logic in BigQuery, since you’re using existing logic. Some of these extraction objects have embedded delta mechanisms, which may complement a replication tool that can’t handle deltas. This will save initial development time, but it can also lead to challenges if you create new columns, or if customizations or upgrades change the logic behind the extraction. 

It’s also important to note that different extraction processes may transform and load the same source columns multiple times, which creates redundancy in BigQuery and can lead to higher maintenance needs and costs. However, replicating pre-built models may still be a good choice, since doing so can be especially useful for logic that tends to be immutable, such as flattening a hierarchy, or logic that is highly complex.

How you approach replication will also depend on your long-term plans and other key factors — for example, the availability (and curiosity) of your developers, and the time or effort they can put into applying their SQL knowledge to a new data warehouse. 

With either replication approach, bear in mind when designing your replication process that BigQuery is meant to be an append-always database — so post-processing of data and changes will be required in both cases. 

Processing data changes

The replication tool you choose will also determine how data changes are captured (known as CDC – change data capture). If the replication tool allows for it (for example as SAP SLT does) the same patterns described in the CDC with BigQuery documentation also apply to SAP data. 

Because some data, like transactions, are known to be less static than others (e.g., master data), you need to decide what should be scanned in real time, what will require immediate consistency, and what can be processed in batches to manage costs. This decision will be based on the reporting needs from the business.

Consider the SAP table BUT000, containing our example master data for business partners, where we have replicated changes from an SAP ERP system:

In an append-always replication in BigQuery, all updates are received as new records. For example, deleting a record in the source will be represented as a new record in BigQuery with a deletion flag. This applies to whether the records are coming from raw tables like BUT000 itself or pre-aggregated data, as from a BW extractor or a CDS view.

Let’s take a closer look at data coming particularly from the partners “LUCIA” and “RIZ”. The operation flag tells us whether the new record in BigQuery is an insert (I), update (U) or deletion (D), while the timestamps help us identify the latest version of our business partner.

If we want to find the latest updated record for the partners LUCIA and RIZ, this is what the query would look like:

  SELECT partner,
        ARRAY_AGG(i1 ORDER BY i1.recordstamp DESC LIMIT 1) AS row
FROM SAP_ECC.but000 i1 
WHERE partner in ('LUCIA','RIZ')
    GROUP BY partner

With the following result:

After identifying stale records for “LUCIA” and “RIZ” business partners, we can proceed to deleting all stale records for “LUCIA” if we do not want to retain the history. In this example, we are using a different table to which the same replication has been done, for the purpose of comparison and to check that all stale records have been deleted for the selection made and that we only kept last updated records. For example:

  DELETE SAP_HANA.but000 i1
WHERE
i1.recordstamp < TIMESTAMP_SUB(CURRENT_TIMESTAMP(), INTERVAL 3 HOUR) AND
i1.recordstamp < (SELECT MAX(recordstamp) FROM SAP_HANA.but000 i2
WHERE
i1.partner = i2.partner
and partner="LUCIA")

You can also use the following query to retrieve stale records for “LUCIA” partner before moving forward with deletion

  SELECT partner, operation_flag, recordstamp  FROM SAP_HANA.but000 i1
WHERE
i1.recordstamp < TIMESTAMP_SUB(CURRENT_TIMESTAMP(), INTERVAL 3 HOUR) 
AND
i1.recordstamp < (SELECT MAX(recordstamp) FROM SAP_HANA.but000 i2 
WHERE 
i1.partner = i2.partner
and partner="LUCIA")

Which produces all of the records, except the latest update:

Partitioning and clustering

To limit the number of records scanned in a query, save on cost and achieve the best performance possible, you’ll need to take two important steps: determine partitions and create clusters. 

Partitioning
A partitioned table is one that’s divided into segments, called partitions, which make it easier to manage and query your data. Dividing a large table into smaller partitions improves query performance and controls costs because it reduces the number of bytes read by a query.

You can partition BigQuery tables by:

• Time-unit column: Tables are partitioned based on a “timestamp,” “date,” or “datetime” column in the table.
• Ingestion time: Tables are partitioned based on the timestamp recorded when BigQuery ingested the data.
• Integer range: Tables are partitioned based on an integer column.

Partitions are enabled when the table is created, as in the example below.  A great tip is to always include the partition filter as shown on the left-hand side of the query.

Clustering
Clustering can be created on top of partitioned tables by applying the fields that are likely to be used for filtering. When you create a clustered table in BigQuery, the table data is automatically organized based on the contents of one or more of the columns in the table’s schema. The columns you specify are then used to colocate related data.

Clustering can improve the performance of certain query types — for example, queries that use filter clauses or that aggregate data. It makes a lot of sense to use them for large tables such as ACDOCA, the table for accounting documents in SAP S/4HANA. In this case, the timestamp could be used for partitioning, and common filtering fields such as the ledger, company code, and fiscal year could be used to define the clusters.

A great feature is that BigQuery will also periodically recluster the data automatically.

Materialized views

In BigQuery, materialized views are precomputed views that periodically cache the results of a query for better performance and efficiency. BigQuery uses precomputed results from materialized views and, whenever possible, reads only the delta changes from the base table to compute up-to-date results quickly. Materialized views can be queried directly or can be used by the BigQuery optimizer to process queries to the base table.

Queries that use materialized views are generally completed faster and consume fewer resources than queries that retrieve the same data only from the base table. If workload performance is an issue, materialized views can significantly improve the performance of workloads that have common and repeated queries. While materialized views currently only support single tables, they are very useful common and frequent aggregations like stock levels or order fulfillment.

Further tips on performance optimization while creating select statements can be found in the documentation for optimizing query computation.

Deployment pipeline and security

For most of the work you’ll do in BigQuery, you’ll normally have at least two delivery pipelines running — one for the actual objects in BigQuery and the other to keep the data staging, transforming, and updated as intended within the change-data-capture flows. Note that you can use most existing tools for your Continuous Integration / Continuous Deployment (CI/CD) pipeline — one of the benefits of using an open system like BigQuery. But, if your organization is new to CI/CD pipelines, this is a great opportunity to gradually gain experience. A good place to start is to read our guide for setting up a CI/CD pipeline for your data-processing workflow.  

When it comes to access and security, most end-users will only have access to the final version of the BigQuery views. While row and column-level security can be applied, as in the SAP source system, separation of concerns can be taken to the next level by splitting your data across different Google Cloud projects and BigQuery datasets. While it’s easy to replicate data and structures across your datasets, it’s a good idea to define the requirements and naming conventions early in the design process so you set it up properly from the start. 

Start driving faster and more insightful analytics

The best piece of advice we can give you is this: Try it yourself. Anyone with SQL knowledge can get started using the free BigQuery tier. New customers get $300 in free credits to spend on Google Cloud during the first 90 days. All customers get 10 GB storage and up to 1 TB queries/month, completely free of charge. In addition to discovering the massive processing capabilities, embedded machine learning, multiple integration tools, and cost benefits, you’ll soon discover how BigQuery can simplify your analytics tasks. 

If you need additional assistance, our Google Cloud Professional Services Organization (PSO) and Customer Engineers will be happy to help show you the best path forward for your organization. For anything else, contact us at cloud.google.com/contact.

In-memory data stores are a fundamental infrastructure for building scalable, high-performance applications. Whether it is building a highly responsive ecommerce website, creating multiplayer games with thousands of users, or doing real-time analysis on data pipelines with millions of events, an in-memory store helps provide low latency and scale for millions of transactions. Redis is a popular in-memory data store for use cases like session stores, gaming leaderboards, stream analytics, API rate limiting, threat detection, and more. Another in-memory data store, open source Memcached, continues to be a very popular choice as a caching layer for databases and is used for its speed and simplicity.

We’re announcing Memorystore for Memcached in beta, a fully managed, highly scalable service that’s compatible with the open source Memcached protocol. We launched Memorystore for Redis in 2018 to let you use the power of open source Redis easily without the burden of management. This announcement brings even more flexibility and choice for your caching layer. 

Highlights of Memorystore for Memcached

Memcached offers a simple but powerful in-memory key value store and is popular as a front-end cache for databases. Using Memcached as a front-end store not only provides an in-memory caching layer for faster query processing, but it can also help save costs by reducing the load on your back-end databases.

Using Memorystore for Memcached provides several important benefits:

• Memorystore for Memcached is fully open source protocol compatible. If you are migrating applications using self-deployed Memcached or other cloud providers, you can simply migrate your application with zero code changes.
• Memorystore for Memcached is fully managed. All the common tasks that you spend time on, like deployment, scaling, managing node configuration on the client, setting up monitoring, and patching, are all taken care of. You can focus on building your applications.
• Right-sizing a cache is a common challenge with distributed caches. The scaling feature of Memorystore for Memcached, along with detailed open source Memcached monitoring metrics, allows you to scale your instance up and down easily to optimize for your cache-hit ratio and price. With Memorystore for Memcached, you can scale your cluster up to 5 TB per instance.
• Auto-discovery protocol lets clients adapt to changes programmatically, making it easy to deal with changes to the number of nodes during scaling. This drastically reduces manageability overhead and code complexity.
• You can monitor your Memorystore for Memcached instances with built-in dashboards in the Cloud Console and rich metrics in Cloud Monitoring.

Memorystore for Memcached can be accessed from applications running on Compute Engine, Google Kubernetes Engine (GKE), App Engine Flex, App Engine Standard, and Cloud Functions.

The beta launch is available in major regions across the U.S., Asia, and Europe, and will be available globally soon.

Getting started with Memorystore for Memcached

To get started with Memorystore for Memcached, check out the quick start guide. Sign up for a $300 credit to try Memorystore and the rest of Google Cloud. You can start with the smallest instance and when you’re ready, you can easily scale up to serve performance-intensive applications. Enjoy your exploration of Google Cloud and Memorystore for Memcached.

Picking the right database for your application is not easy. The choice depends heavily on your use case—transactional processing, analytical processing, in-memory database, and so on—but it also depends on other factors. This post covers the different database options available within Google Cloud across relational (SQL) and non-relational (NoSQL) databases and explains which use cases are best suited for each database option. 

Relational databases 

In relational databases information is stored in tables, rows and columns, which typically works best for structured data. As a result they are used for applications in which the structure of the data does not change often. SQL (Structured Query Language) is used when interacting with most relational databases. They offer ACID consistency mode for the data, which means:

• Atomic: All operations in a transaction succeed or the operation is rolled back.
• Consistent: On the completion of a transaction, the database is structurally sound.
• Isolated: Transactions do not contend with one another. Contentious access to data is moderated by the database so that transactions appear to run sequentially.
• Durable: The results of applying a transaction are permanent, even in the presence of failures.

Because of these properties, relational databases are used in applications that require high accuracy and for transactional queries such as financial and retail transactions. For example: In banking when a customer makes a funds transfer request, you want to make sure the transaction is possible and it actually happens on the most up-to-date account balance, in this case an error or resubmit request is likely fine.

There are three relational database options in Google Cloud: Cloud SQL, Cloud Spanner, and Bare Metal Solution.

Cloud SQL: Provides managed MySQL, PostgreSQL and SQL Server databases on Google Cloud. It reduces maintenance cost and automates database provisioning, storage capacity management, back ups, and out-of-the-box high availability and disaster recovery/failover. For these reasons it is best for general-purpose web frameworks, CRM, ERP, SaaS and e-commerce applications.

Cloud Spanner: Cloud Spanner is an enterprise-grade, globally-distributed, and strongly-consistent database that offers up to 99.999% availability, built specifically to combine the benefits of relational database structure with non-relational horizontal scale. It is a unique database that combines ACID transactions, SQL queries, and relational structure with the scalability that you typically associate with non-relational or NoSQL databases. As a result, Spanner is best used for applications such as gaming, payment solutions, global financial ledgers, retail banking and inventory management that require ability to scale limitlessly with strong-consistency and high-availability. 

Bare Metal Solution: Provides hardware to run specialized workloads with low latency on Google Cloud. This is specifically useful if there is an Oracle database that you want to lift and shift into Google Cloud. This enables data center retirements and paves a path to modernize legacy applications. 

Non-relational databases

Non-relational databases (or NoSQL databases) store compex, unstructured data in a non-tabular form such as documents. Non-relational databases are often used when large quantities of complex and diverse data need to be organized. Unlike relational databases, they perform faster because a query doesn’t have to access several tables to deliver an answer, making them ideal for storing data that may change frequently or for applications that handle many different kinds of data. 

For example, an apparel store might have a database in which shirts have their own document containing all of their information, including size, brand, and color with room for adding more parameters later such as sleeve size, collars, and so on.

Qualities that make NoSQL databases fast:

• Eventual consistency: stores usually exhibit consistency at some later point (e.g., lazily at read time)
• Horizontal scaling, usually using hashed distributions
• Typically, they are optimized for a specific workload pattern (i.e., key-value, graph, wide-column)
• Typically, they don’t support cross shard transactions or flexible isolation modes.

Because of these properties, non-relational databases are used in applications that require large scale, reliability, availability, and frequent data changes.They can easily scale horizontally by adding more servers, unlike some relational databases, which scale vertically by increasing the machine size as the data grows. Although, some relations databases such as Cloud Spanner support scale-out and strict consistency.

Non-relational databases can store a variety of unstructured data such as documents, key-value, graphs, wide columns, and more. Here are your non-relational database options in Google Cloud: 

• Document databases: Store information as documents (in formats such as JSON and XML). For example: Firestore
• Key-value stores: Group associated data in collections with records that are identified with unique keys for easy retrieval. Key-value stores have just enough structure to mirror the value of relational databases while still preserving the benefits of NoSQL. For example: Datastore, Bigtable, Memorystore
• In-memory database: Purpose-built database that relies primarily on memory for data storage. These are designed to attain minimal response time by eliminating the need to access disks. They are ideal for applications that require microsecond response times and can have large spikes in traffic. For example: Memorystore
• Wide-column databases: Use the tabular format but allow a wide variance in how data is named and formatted in each row, even in the same table. They have some basic structure while preserving a lot of flexibility. For example: Bigtable
• Graph databases: Use graph structures to define the relationships between stored data points; useful for identifying patterns in unstructured and semi-structured information. For example: JanusGraph

There are three non-relational databases in Google Cloud:

• Firestore: Is a serverless document database which scales on demand and acts as a backend-as-a-service. It is DBaaS that increases the speed of building applications. It is perfect for all general purpose uses cases such as ecommerce, gaming, IoT and real time dashboards. With Firestore users can interact with and collaborate on live and offline data making it great for real-time application and mobile apps.  
• Cloud Bigtable: Cloud Bigtable is a sparsely populated table that can scale to billions of rows and thousands of columns, enabling you to store terabytes or even petabytes of data. It is ideal for storing very large amounts of single-keyed data with very low latency. It supports high read and write throughput at sub-millisecond latency, and it is an ideal data source for MapReduce operations. It also supports the open-source HBase API standard to easily integrate with the Apache ecosystem including HBase, Beam, Hadoop and Spark along with Google Cloud ecosystem.
• Memorystore: Memorystore is a fully managed in-memory data store service for Redis and Memcached at Google Cloud. It is best for in-memory and transient data stores and automates the complex tasks of provisioning, replication, failover, and patching so you can spend more time coding. Because it offers extremely low latency and high performance, Memorystore is great for web and mobile, gaming, leaderboard, social, chat, and news feed applications.

Conclusion

Choosing a relational or a non-relational database largely depends on the use case. Broadly, if your application requires ACID transactions and your data structure is not going to change much, select a relational database. 

In Google Cloud use Cloud SQL for any general-purpose SQL database and Cloud Spanner for large-scale globally scalable, strongly consistent use cases. In general, if your data structure may change later and if scale and availability is a bigger requirement than consistency then a non-relational database is a preferable choice.  Google Cloud offers Firestore, Memorystore, and Cloud Bigtable to support a variety of use cases across the document, key-value, and wide column database spectrum.

For more comparison resources on each database check out the overview. For more hands-on experience with Bigtable, check out our on-demand training here and learn about migrating databases to managed services check out this whitepaper.  

https://youtube.com/watch?v=2TZXSnCTd7E%3Fenablejsapi%3D1%26

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

October 23rd (this past Saturday!) was my 4th Googlevarsery and we are wrapping an incredible Google Next 2021!

When I started in 2017, we had a dream of making BigQuery Intelligent Data Warehouse that would power every organization’s data driven digital transformation. 

This year at Next, It was amazing to see Google Cloud’s CEO, Thomas Kurian, kick off his keynote with CTO of WalMart, Suresh Kumar , talking about how his organization is giving its data the “BigQuery treatment”.

AS  I recap Next 2021 and  reflect on our amazing journey over the past 4 years, I’m so proud of the opportunity I’ve had to work with some of the world’s most innovative companies from Twitter to Walmart to Home Depot, Snap, Paypal and many others.   

So much of what we announced at Next is the result of years of hard work, persistence and commitment to delivering the best analytics experience for customers. 

I believe that one of the reasons why customers choose Google for data is because we have shown a strong alignment between our strategy and theirs and because we’ve been relentlessly delivering innovation at the speed they require. 

Unified Smart Analytics Platform 

Over the past 4 years our focus has been to build industries leading unified smart analytics platforms. BigQuery is at the heart of this vision and seamlessly integrates with all our other services. Customers can use BigQuery to query data in BigQuery Storage, Google Cloud Storage, AWS S3, Azure Blobstore, various databases like BigTable, Spanner, Cloud SQL etc. They can also use  any engine like Spark, Dataflow, Vertex AI with BigQuery. BigQuery automatically syncs all its metadata with Data Catalog and users can then run a Data Loss Prevention service to identify sensitive data and tag it. These tags can then be used to create access policies. 

In addition to Google services, all our partner products also integrate with BigQuery seamlessly. Some of the key partners highlighted at Next 21 included Data Ingestion (Fivetran, Informatica & Confluent), Data preparation (Trifacta, DBT),  Data Governance (Colibra), Data Science (Databricks, Dataiku) and BI (Tableau, PowerBI, Qlik etc).

Planet Scale analytics with BigQuery

BigQuery is an amazing platform and over the past 11 years we have continued to innovate in various aspects. Scalability has always been a huge differentiator for BigQuery. BigQuery has many customers with more than 100 petabytes of data and our largest customer is now approaching  an exabyte of data. Our large customers have run queries over trillions of rows. 

But scale for us is not just about storing or processing a lot of data. Scale is also how we can reach every organization in the world. This is the reason we launched BigQuery Sandbox which enables organizations to get started with BigQuery without a credit card. This has enabled us to reach tens of thousands of customers. Additionally to make it easy to get started with BigQuery we have built integrations with various Google tools like Firebase, Google Ads, Google Analytics 360, etc. 

Finally, to simplify adoption we now provide options for customers to choose whether they would like to pay per query, buy flat rate subscriptions or buy per second capacity. With our autoscaling capabilities we can provide customers best value by mixing flat rate subscription discounts with auto scaling with flex slots.

Intelligent Data Warehouse to empower every data analyst to become a data scientist

BigQuery ML is one of the  biggest innovations that we have brought to market over the past few years. Our vision is to make every data analyst a data scientist by democratizing Machine learning. 80% of time is spent in moving, prepping and transforming data for the ML platform. This also causes a huge data governance problem as now every data scientist has a copy of your most valuable data.  Our approach was very simple.  We asked:”what if we could bring ML to data rather than taking data to an ML engine?” 

That is how BigQuery ML was born. Simply write 2 lines of SQL code and create ML models. 

Over the past 4 years we have launched many models like regression, matrix factorization, anomaly detection, time series, XGboost, DNN etc. These  models are used by customers to solve complex  business problems simply from segmentation, recommendations, time series forecasting, package delivery estimation etc. The service is very popular: 80%+ of our top customers are using BigQueryML today.  When you consider that the average adoption rate of ML/AI is in the low 30%, 80% is a pretty good result!

We announced tighter integration of BQML with Vertex AI. Model explainability will provide the ability to explain the results of predictive ML classification and regression models by understanding how each feature contributes to the predicted result. Also users will be able to manage, compare and deploy BigQuery ML models in Vertex; leverage Vertex Pipelines to train and predict BigQuery ML models.

Real-time streaming analytics with BigQuery 

Customer expectations are changing and everyone wants everything in an instant: according to Gartner, by the end of 2024, 75% of enterprises will shift from piloting to operationalizing AI, driving a 5X increase in streaming data and analytics infrastructures.

The BigQuery’s storage engine is optimized for real-time streaming. BigQuery supports streaming ingestion of 10s of millions of events in real-time and there is no impact on query performance. Additionally customers  can use materialized views and BI Engine (which is now GA) on top of streaming data. We guarantee always fast, always fresh data. Our system automatically updates MVs and BI Engine. 

Many customers also use our PubSub service to collect real-time events and process these through Dataflow prior to ingesting into BigQuery. This is a streaming ETL pattern which is very popular. Last year,we announced PubSub Lite to  provide customers with a 90% lower price point and aTCO that is lower than any DIY Kafka deployment. 

We also announced Dataflow Prime, it is our next generation platform for Dataflow. Big Data processing platforms have only focused on horizontal scaling to optimize workloads. But we have seen new patterns and use cases like streaming AI where you may have a few steps in pipelines that perform data prep and then customers  have to run a GPU based model. Customers  want to use different sizes and shapes of machines to run these pipelines in the most optimum manner. This is exactly what Dataflow Prime does. It delivers vertical auto scaling with the right fitting for your pipelines. We believe this should lower costs for pipelines significantly.

With Datastream as our change data capture service (built on Alooma technology), we have solved the last key problem space for customers. We can automatically detect changes in your operational databases like MySQL, Postgres, Oracle etc and sync them in BigQuery.

Most importantly, all these products work seamlessly with each other through a set of templates. Our goal is to make this even more seamless over next year. 

Open Data Analytics with BigQuery

Google has always been a big believer in Open Source initiatives. Our customers love using various open source offerings like Spark, Flink, Presto, Airflow etc. With Dataproc & Composer our customers have been able to run various of these open source frameworks on GCP and leverage our scale, speed and security. Dataproc is a great service and delivers massive savings to customers moving from on-prem Hadoop environments. But customers want to focus on jobs and not clusters. 

That’s why we launched Dataproc Serverless Spark (GA) offering at Next 2021. This new service adheres to one of our key design principles we started with: make data simple.  

Just like with BigQuery, you can simply RUN QUERY. With Spark on Google Cloud, you simply RUN JOB.  ZDNet did a great piece on this.  I invite you to check it out!

Many of our customers are moving to Kubernetes and wanted to use that as the platform for Spark. Our upcoming Spark on GKE offering will give the ability to deploy spark workloads on existing Kubernetes clusters.  

But for me the most exciting capability we have is, the ability to run Spark directly on BigQuery Storage. BigQuery storage is highly optimized analytical storage. By running Spark directly on it, we again bring compute to data and avoid moving data to compute. 

BigSearch to power Log Analytics

We are bringing the power of Search to BigQuery. Customers already ingest massive amounts of log data into BigQuery and perform analytics on it. Our customers have been asking us for better support for native JSON and Search. At Next 21 we announced the upcoming availability of both these capabilities.

Fast cross column search will provide efficient indexing of structured, semi-structured and unstructured data. User friendly SQL functions let customers rapidly find data points without having to scan all the text in your table or even know which column the data resides in. 

This will be tightly integrated with native JSON, allowing customers to get BigQuery performance and storage optimizations on JSON as well as search on unstructured or constantly changing  data structures. 

Multi & Cross Cloud Analytics

Research on multi cloud adoption is unequivocal — 92% of businesses in 2021 report having a multi cloud strategy. We have always believed in providing customers choice to our customers and meeting them where they are. It was clear that all our customers wanted us to take our gems like BigQuery to other clouds as their data was distributed on different clouds. 

Additionally it was clear that customers wanted cross cloud analytics not multi-cloud solutions that can just run in different clouds. In short, see all their data with a single pane of glass, perform analysis on top of any data without worrying about where it is located, avoid egress costs and finally perform cross cloud analysis across datasets on different clouds.

With BigQuery Omni, we deliver on this vision, with a new way of analyzing data stored in multiple public clouds.  Unlike competitors, BigQuery Omni does not create silos across different clouds. BigQUery provides a single control plane that shows an analyst all data they have access to across all clouds. Analyst just writes the query and we send it to the right cloud across AWS, Azure or GCP to execute it locally. Hence no egress costs are incurred. 

We announced BQ Omni GA for both AWS and Azure at Google Next 21 and I’m really proud of the team for delivering on this vision.  Check out Vidya’s session and learn from Johnson and Johnson how they innovate in a multi-cloud world.

Geospatial Analytics with BigQuery and Earth Engine

We have partnered with our Google Geospatial team to deliver GIS functionality inside BigQuery over the years. At Next we announced that customers will be able to integrate Earth Engine with BigQuery, Google Cloud’s ML technologies, and Google Maps Platform. 

Think about all the scenarios and use-cases your team’s going to be able to enable sustainable sourcing, saving energy or understanding business risks.

We’re integrating the best of Google and Google Cloud together to – again – make it easier to work with data to create a sustainable future for our planet.  

BigQuery as a Data Exchange & Sharing Platform

BigQuery was built to be a sharing platform. Today we have 3000+ organizations sharing more than 250 petabytes of data across organizations. Google also brings more than 150 public datasets to be used across various use cases. In addition to this, we are also bringing some of the most unique datasets like Google Trends to BigQuery. This will enable organizations to understand in real-time trends and apply to their business problems.

I am super excited about the Analytics Hub Preview announcement. Analytics Hub will provide the ability for organizations to build private and public analytics exchanges. This will include data, insights, ML Models and visualizations. This is built on top of the industry leading security capabilities of BigQuery.

Breaking Data Silos

Data is distributed across various systems in the organization and making it easy to break the data silo and make all this data accessible to all is critical. I’m also particularly excited about the Migration Factory we’re building with Informatica and the work we are doing for data movement, intelligent data wrangling with players like Trifacta and FiveTran, with whom we share over 1,000 customers (and growing!).  Additionally we continue to deliver native Google service to help our customers. 

We acquired Cask in 2018 and launched our self service Data Integration service in Data Fusion. Now Fusion allows customers to create complex pipelines with just simple drag and drop. This year we focused on unlocking SAP data for our customers. We have launched various SAP connectors and accelerators to achieve this.

At GCP Next we also announced our BigQuery Migration service in preview. Many of our customers are migrating their legacy data warehouses and data lakes to BigQuery. BigQuery Migration Service provides end-to-end tools to simplify migrations for these customers. 

And today, to make migrations to BigQuery easier for even more customers, I am super excited to announce the acquisition of CompilerWorks. CompilerWorks’ Transpiler is designed from the ground up to facilitate SQL migration in the real world and will help our customers accelerate their migrations. It supports migrations from over 10 legacy enterprises data warehouses and we will be making it available as part of our BigQuery Migration service in the coming months.

Data Democratization with BigQuery

Over the past 4 years we have focused a lot on making it very  easy to derive actionable insights from data in BigQuery. Our priority has been to provide a strong ecosystem of partners that can provide you with great tools to achieve this but also deliver native Google capabilities. 

With our BI engine GA announcement which we introduced in 2019, previewed earlier this year and showcased with tools like Microsoft PowerBI and Tableau, is now available for all to play with.

BigQuery + Data Studio are like peanut butter and Jelly. They just work well together. We launched BI Engine first with Data Studio and scaled it to all the users. More than 40% of our BigQuery customers use Data Studio. Once we knew BI Engine works extremely well we now have made it an integral part of BigQuery API and launched it for all our internal and partner BI tools. 

We announced GA for BI Engine at Next 2021 but we were already GA with Data Studio for the past 2 years. We recently moved the Data Studio team back into Google Cloud making the partnership even stronger. If you have not used Data Studio, I encourage you to take a look and get started for free today here!! 

Connected Sheets for BigQuery is one of my favorite combinations. You can give every business user in your organization the ability to analyze billions of records using standard Google Sheets experience. I personally use it everyday to analyze all our product data. 

We acquired Looker in Feb 2020 with a vision of providing a semantic modeling layer to our customers with a governed BI solution. Looker is tightly integrated with BigQuery including BigQuery ML. Our latest partnership with Tableau where Tableau customers will soon be able to leverage Looker’s semantic model, enabling new levels of data governance while democratizing access to data. 

Finally, I have a dream that one day we will bring Google Assistant to your enterprise data. This is the vision of Data QnA. We are in early innings on this and we will continue to work hard to make this vision a reality. 

Intelligent Data Fabric to unify the platform

Another important trend that shaped our market is the Data Mesh.  Earlier this year, Starburst invited me to talk about this very topic. We have been working for years on this concept, and although we would love for all data to be neatly organized in one place, we know that our customers’ reality is that it is not (If you want to know more about this, read about my debate on this topic with Fivetran’s George Fraser, a16z’s Martin Casado and Databricks’ Ali Ghodsi).

Everything I’ve learned from customers over my years in this field is that they don’t just need a data catalog or a set of data quality and governance tools, they need an intelligent data fabric.  That is why we created Dataplex, whose general availability we announced at Next.

Dataplex enables customers to centrally manage, monitor, and govern data across data lakes, data warehouses, and data marts, while also ensuring data is securely accessible to a variety of analytics and data science tools.  It lets customers organize and manage data in a way that makes sense for their business, without data movement or duplication. It provides logical constructs – lakes, data zones, and assets – which enable customers to abstract away the underlying storage systems to build a foundation for setting policies around data access, security, lifecycle management, and so on.  Check out Prajakta Damle’s session and learn from Deutsche Bank how they are thinking about a unified data mesh across distributed data.

Closing Thoughts

Analysts have recognized our momentum and, as I look back at this year, I couldn’t thank our customers and partners enough for the support they provided my team and I across our large Data Analytics portfolio: in March, Google BigQuery was named a Leader in The Forrester Wave™: Cloud Data Warehouse, Q1 2021.  And in June, Dataflow was named a Leader in The Forrester Wave™: Streaming Analytics, Q2 2021 report.

If you want to get a taste for why customers choose us over other hyperscalers or cloud data warehousing, I suggest you watch the Data Journey series we’ve just launched, which documents the stories of organizations modernizing to the cloud with us.

The Google Cloud Data Analytics portfolio has become a leading force in the industry and I couldn’t be more excited to have been part of it.  I do miss you, my customers and partners, and I’m frankly bummed that we didn’t get to meet in person like we’ve done so many times before (see a photo of my last in-person talk before the pandemic), but this Google Next was extra special, so let’s dive into the product innovation and their themes.

I hope that I will get to see you in person next time we run Google Next!