YOU want to break into data analytics but not sure where to start? This interactive choose-your-own-adventure episode will help you! Get ready to make real-life decisions that will shape your data career. Play now and see where your choices take you. 💌 Join 10k+ aspiring data analysts & get my tips in your inbox weekly 👉 https://www.datacareerjumpstart.com/newsletter 🆘 Feeling stuck in your data journey? Come to my next free "How to Land Your First Data Job" training 👉 https://www.datacareerjumpstart.com/training 👩‍💻 Want to land a data job in less than 90 days? 👉 https://www.datacareerjumpstart.com/daa 👔 Ace The Interview with Confidence 👉 https://www.datacareerjumpstart.com/interviewsimulator ⌚ Control this audio using these timestamps: 1:54 - 1 - Data Scientist 3:48 - 2 - Data Analyst 5:42 - 3 - Python 7:36 - 4 - SQL 9:30 - 5 - Keep Learning 11:24 - 6 - Browse Some Jobs 13:18 - 7 - Move On 15:12 - 8 - Apply 17:06 - 9 - Try to Network 🔗 CONNECT WITH AVERY 🎥 YouTube Channel: https://www.youtube.com/@averysmith 🤝 LinkedIn: https://www.linkedin.com/in/averyjsmith/ 📸 Instagram: https://instagram.com/datacareerjumpstart 🎵 TikTok: https://www.tiktok.com/@verydata 💻 Website: https://www.datacareerjumpstart.com/ Mentioned in this episode: Join the last cohort of 2025! The LAST cohort of The Data Analytics Accelerator for 2025 kicks off on Monday, December 8th and enrollment is officially open!

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Streaming data with Apache Kafka® has become the backbone of modern day applications. While streams are ideal for continuous data flow, they lack built-in querying capability. Unlike databases with indexed lookups, Kafka's append-only logs are designed for high throughput processing, not for on-demand querying. This necessitates teams to build additional infrastructure to enable query capabilities for streaming data. Traditional methods replicate this data into external stores such as relational databases like PostgreSQL for operational workloads and object storage like S3 with Flink, Spark, or Trino for analytical use cases. While useful sometimes, these methods deepen the divide between operational and analytical estates, creating silos, complex ETL pipelines, and issues with schema mismatches, freshness, and failures.\n\nIn this session, we’ll explore and see live demos of some solutions to unify the operational and analytical estates, eliminating data silos. We’ll start with stream processing using Kafka Streams, Apache Flink®, and SQL implementations, then cover integration of relational databases with real-time analytics databases such as Apache Pinot® and ClickHouse. Finally, we’ll dive into modern approaches like Apache Iceberg® with Tableflow, which simplifies data preparation by seamlessly representing Kafka topics and associated schemas as Iceberg or Delta tables in a few clicks. While there's no single right answer to this problem, as responsible system builders, we must understand our options and trade-offs to build robust architectures.

In this hands-on lab, you'll explore data with BigQuery's intuitive table explorer and data insight features, enabling you to gain valuable insights without writing SQL queries from scratch. Learn how to generate key insights from order item data, query location tables, and interact with your data seamlessly. By the end, you’ll be equipped to navigate complex datasets and uncover actionable insights quickly and efficiently.

If you register for a Learning Center lab, please ensure that you sign up for a Google Cloud Skills Boost account for both your work domain and personal email address. You will need to authenticate your account as well (be sure to check your spam folder!). This will ensure you can arrive and access your labs quickly onsite. You can follow this link to sign up!

Analyze BigQuery logs with SQL using Log Analytics. This hands-on lab covers enabling Log Analytics, querying BigQuery logs within Cloud Logging, and visualizing results for in-depth usage analysis and troubleshooting.

If you register for a Learning Center lab, please ensure that you sign up for a Google Cloud Skills Boost account for both your work domain and personal email address. You will need to authenticate your account as well (be sure to check your spam folder!). This will ensure you can arrive and access your labs quickly onsite. You can follow this link to sign up!

Time to make generative AI a reality for your application. This session is all about how to build high-performance gen AI applications fast with Cloud SQL for MySQL and PostgreSQL. Learn about Google Cloud’s innovative full-stack solutions that make gen AI app development, deployment, and operations simple and easy – even when deploying high-performance, production-grade applications. We’ll highlight best practices for getting started with Vertex AI, Cloud Run, Google Kubernetes Engine, and Cloud SQL, so that you can focus on gen AI application development from the get-go.

In this hands-on lab, you'll explore the power of BigQuery Machine Learning with remote models like Gemini Pro to analyze customer reviews. Learn to extract keywords, assess sentiment, and generate insightful reports using SQL queries. Discover how to integrate Gemini Pro Vision to summarize and extract keywords from review images. By the end, you’ll gain skills in setting up Cloud resources, creating datasets, and prompting Gemini models to drive actionable insights and automated responses to customer feedback.

If you register for a Learning Center lab, please ensure that you sign up for a Google Cloud Skills Boost account for both your work domain and personal email address. You will need to authenticate your account as well (be sure to check your spam folder!). This will ensure you can arrive and access your labs quickly onsite. You can follow this link to sign up!

Build resilient, scalable applications that thrive in the face of increasing demands. Cloud SQL offers new features designed to optimize performance, availability, and cost efficiency for MySQL and PostgreSQL databases, managed replica pools, and connection pooling. Learn how to make downtime a thing of the past, implement advanced disaster recovery strategies, and maximize your application’s performance. Join our demo-packed session for a deep dive into these new Cloud SQL capabilities and best practices.

Learn how Database Migration Service can help you modernize your SQL Server databases to unleash the power of cloud databases and open source PostgreSQL! Convert your SQL Server schema and T-SQL code to PostgreSQL dialect with a click of a button in the DMS Conversion Workspace. Some objects could not be fully converted? Gemini can suggest a fix. Not yet familiar with PostgreSQL features? Ask Gemini to teach you how to convert SQL Server features to PostgreSQL equivalent ones. While Gemini is there - ask it to optimize the converted code or add some comments to explain the business logic. Once your database is fully converted and optimized you can migrate the data with minimal downtime using the change data capture powered migration job and complete your migration journey.

AI is revolutionizing observability. Learn about Cloud SQL AI-powered Database Insights and how it can help you optimize your queries and boost database performance. We’ll dive deep into the new Insights capabilities for MySQL, PostgreSQL, and SQL Server, including the Gemini-powered chat agent. Learn how to troubleshoot those tricky database performance issues and get practical tips to improve the performance of your applications.

SQL Server workloads powering mission-critical applications demand high availability and disaster recovery configurations from the get-go. Users can choose to leverage the business continuity and disaster recovery (BCDR) capabilities in Cloud SQL for SQL Server. In this session, we’ll cover the basics of BCDR with Cloud SQL for SQL Server. We’ll highlight the architectural patterns that are best suited for different types of workloads while leveraging the latest enterprise-ready Cloud SQL innovations.

Bigtable has been a core piece of application infrastructure for Google and companies such as Snap, Spotify, and many other massive platforms for over 20 years. In this session, we’ll discuss the fundamental changes to Bigtable processing capabilities made available via SQL that will let you bring more data transformations directly into Bigtable – enabling extract, load, and transform (ELT) capabilities taking advantage of Bigtable’s flexible schema to achieve increased data freshness – and that will reduce the time and costs of running other data processing services to prepare data for your real-time application.

Newt Global's DMAP revolutionizes Oracle/MS-SQL to PostgreSQL migrations. This automated solution streamlines the entire process, from initial planning to final production deployment. Key advantages are 1. Container-driven parallelization: Dramatically reduces migration timelines by harnessing powerful computing resources. 2. Unmatched speed: For medium complexity databases, DMAP achieves in 12 weeks what other tools take 12 months due to its advanced automation capabilities, including streamlined application and complex code translation.

This Session is hosted by a Google Cloud Next Sponsor.
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This session demonstrates how BigQuery ML connects all your data to cutting-edge AI using familiar SQL. Learn practical steps to build, train, and deploy machine learning (ML) models for predictive analytics directly in BigQuery while minimizing complexity and data movement. Discover ways to perform tasks such as sentiment analysis, audio transcription, and document classification with the latest models from Gemini, Claude, Llama, and others directly in BigQuery without the need for advanced Python or specialized ML skills.

PostgreSQL is one of the most popular open source databases for application development.  Furthermore, native support for vector search makes PostgreSQL an excellent choice for generative AI app development too. Learn why Google Cloud is the best place for your PostgreSQL workloads across Cloud SQL, AlloyDB and Spanner database offerings. Also hear from Salesloft about their journey to Google Cloud and how they chose which database service to select for their workloads.

This hands-on lab guides you through importing real-world data from CSV files into a Cloud SQL database. Using a flight dataset from the US Bureau of Transport Statistics, you'll gain hands-on experience with data ingestion and basic analysis. You'll learn to create a Cloud SQL instance and database, effectively import your data, and build a foundational data model using SQL queries.

If you register for a Learning Center lab, please ensure that you sign up for a Google Cloud Skills Boost account for both your work domain and personal email address. You will need to authenticate your account as well (be sure to check your spam folder!). This will ensure you can arrive and access your labs quickly onsite. You can follow this link to sign up!