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Last week, the Cloud community came together for the Latest Google Cloud Data Analytics Innovations Event. With exclusive demos of tools like BigQuery, Dataproc, Dataplex, and Dataflow, practical customer use cases, and live Q&A, attendees learned how to accelerate the time it takes to drive value from data.
In this post, we’ll share the session recordings, written questions and answers, as well as supporting documentation and resources so you can refer to them at any time.
Still have questions? Please use #AskGoogleDataCloud on Twitter or simply add a comment below and a Googler or someone from the Community will be happy to help!
Reimagine how far data can take you when simplicity, openness, and intelligence are at the core of every stage in the data lifecycle. In this session, learn how our top data and analytics experts use the newest product innovations and solutions to transform data chaos into data-driven value. Google Cloud leaders Gerrit Kazmaier, VP & GM of Database & Analytics, and Bruno Aziza, Head of Data & Analytics, are joined byMathias Nitzsche, the VP Engineering of Global Data at Delivery Hero, and two Google Cloud Data Analysis Innovator Champions, Lynn Langit (@Former Community Member) and Matthias Baetens (@baetensmatthias).
Plus, check out Lynn’s story in the Google Cloud Data Heroes Series here to see how she’s equipping bioinformatic researchers with genomic-scale data pipelines on Google Cloud.
Converting business logic, like SQL, scripts, and other code artifacts to work with BigQuery during a modern data warehouse migration is challenging – but no longer needs to be. In this session, learn about streamlined data warehouse migration strategy with a live demo of Google Cloud’s recently launched BigQuery Migration Service that automatically analyzes, converts, and optimizes legacy code.
Streaming and batch data pipelines, whether big or small in scale, do have their limits. Vertical Auto Scaling and Right Fitting can help stabilize sizing and resources for your pipeline. In this session, learn how to leverage these two new cost-efficient capabilities in Dataflow Prime – the next generation serverless platform.
This quarter’s new Google Cloud Data Lake capabilities are strengthening dynamic products like Dataplex and Dataproc. Find out how they work for consistent data quality, control, and governance. Also learn about Dataplex General Availability, Google Cloud Unified Data Fabric, and Dataproc’s Serverless Spark offering.
Only 1 in 4 companies fully realize the value of their multiple channels of data, yet when properly centralized, these channels can unlock key insights, inform product roadmaps, prioritize urgent fixes, and facilitate better customer relations. In this session, learn how Google Cloud Design Patterns, and Looker Business Intelligence, centralizes common data sources to BigQuery – turning your data warehouse into an actionable customer insights tool.
We wrapped up the event with Sudhir Hasbe, Senior Director of Product Management, as he responded to live questions on the future of data, Google Cloud products, and Machine Learning. In case you missed it, here are the top questions asked with Sudhir’s answers and supporting resources.
There are three primary trends we’re seeing today, which are all related.
First, there is simply more data. The amount of data in every organization is growing, and now organizations need to figure out how to collect, store, manage, and gather valuable insights from all this data.
Second, there are more users. Data isn’t just important for leaders anymore. Everyone in the organization wants the right data to make the right decisions on a daily basis. This is true internally amongst employees, and also externally with customers and end users.
For example, with an ecommerce site, shoppers want to know how popular a certain item is or how many items are still available. This type of data-driven information helps them make an informed decision.
Thirdly, there are more data use cases. As the number of users are growing, the different kinds of situations, requirements, and skill sets are also growing. Technology has evolved to meet these different use cases and will continue to do so - from SQL to NoSQL, Apache Hadoop and Spark, to Tensorflow and Machine Learning. With data more accessible than ever, it’s even more important that you identify and govern what data is available where, to ensure security, privacy, and compliance.
To answer this question, I would first ask why you want to move some workloads to a different cloud platform?
Google Cloud has some of the most differentiated analytics and AI capabilities, including BigQuery ML and Vertex AI. So if you want more value from your data, it makes sense to look at Google Cloud for those types of scenarios. We see many of our customers who operate with multiple cloud providers, but we’re their primary data analytics provider.
I live, breathe, and believe in GCP so there are many reasons why I would recommend it over other providers, but one reason that also often stands out to me is our migration tools and capabilities. If you are migrating workloads, Google Cloud offers various solutions depending on your use case, including database migration, application migration, and more. Additional information about Google Cloud migration solutions can be seen here.
Machine Learning (ML) is becoming increasingly common amongst organizations.
There are various use cases where ML can be beneficial - not just for data scientists with advanced ML coding capabilities - but also for users solving problems in their day-to-day lives. For example, a common logistics problem is understanding how long it will take for a package to reach the customer. To solve this, you can use a large-scale regression model on a large amount of data to provide an estimated time of delivery.
At Google Cloud, we’re continually investing in our solutions, including BigQuery ML, Vertex AI, and AutoML, to simplify ML processes so more users (not just the limited few) can have access and drive value with ML.
Dataplex is the solution that we’re focusing on from a data governance perspective. It enables standardization and unification of metadata, security policies, governance, and data classification for consistency across distributed data. Overall, Dataplex key features help with:
Data quality: Automate data quality across distributed data and enable out-of-box access to data you can trust.
Data management and discovery: Automate data discovery, classification, schema detection, metadata harvesting and registration of structured, semi-structured, and unstructured data across data silos with built-in data intelligence. Readily access this data from a variety of analytics and data science tools.
Centralized security and governance: Central policy management, monitoring, and auditing for data authorization and classification. Distributed data ownership with global monitoring and governance across data and related artifacts including machine learning models.
Looker is one of Google Cloud’s differentiated business intelligence solutions that helps organizations explore, analyze, and share real-time business analytics easily.
Looker’s interactive and dynamic dashboards and components enable real-time data experiences governed by one trusted and unified data model. Users can choose from a library of visualizations or build their own to tell a unique story. They also have the flexibility to set up filters, set alerts to flag changes and irregularities, drill-in and ask more questions of the data, and integrate actionable insights.
Also, with Google Data Studio, you can quickly and easily create customizable dashboards and reports, and share with individuals, teams, and more.
So Looker provides more of a centralized business intelligence solution and Data Studio offers more of a self-service solution, and we’re working on bringing these two solutions together more in the future.
Thanks for tuning in! We’re looking forward to more data community engagements every quarter, so please keep an eye out for more events like these and let us know what you’d want to learn about in a future event (#AskGoogleDataCloud).
COMING UP: Register for our annual Data Cloud Summit happening April 6th!
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