Hi all,

I am tuning in to StarRocks Summit 2025 on September 10. It is a one-day, free, virtual event focused on how teams run Apache Iceberg at scale, not just in pilots.

Customer spotlights I am watching

• TRM Labs - Their journey from BigQuery and Postgres to an Iceberg-centered lakehouse that serves customer-facing analytics straight from Iceberg tables with StarRocks on top.
• Fresha - Iceberg as the durable source of truth with a Kubernetes-native lakehouse for predictable, low-latency insights.
• Demandbase - Iceberg as the foundation for customer-facing MarTech analytics so dashboards stay fresh without brittle ETL.

You will also see sessions on governance, AI workloads, and best practices for running Iceberg in production.

Why I care

On The Ravit Show I hear the same theme again and again. Value shows up when teams move past experiments and standardize on an open table format with a fast serving layer. Iceberg with StarRocks is a pattern I now see in the wild.

One day. Free. Virtual. I will be there. Check the agenda and grab your pass:

Data Engineering is the backbone of modern data and AI. Here are 20 foundational terms every professional should know Part 1:

1️⃣ Data Pipeline: Automates data flow from sources to destinations like warehouses

2️⃣ ETL: Extract, clean, and load data for analysis

3️⃣ Data Lake: Stores raw, unstructured data at scale

4️⃣ Data Warehouse: Optimized for structured data and BI

5️⃣ Data Governance: Ensures data accuracy, security, and compliance

6️⃣ Data Quality: Accuracy, consistency, and reliability of data

7️⃣ Data Cleansing: Fixes errors for trustworthy datasets

8️⃣ Data Modeling: Organizes data into structured formats

9️⃣ Data Integration: Combines data from multiple sources

🔟 Data Orchestration: Automates workflows across pipelines

1️⃣1️⃣ Data Transformation: Prepares data for analysis or integration

1️⃣2️⃣ Real-Time Processing: Analyzes data as it’s generated

1️⃣3️⃣ Batch Processing: Processes data in scheduled chunks

1️⃣4️⃣ Cloud Data Platform: Scalable data storage and analytics in the cloud

1️⃣5️⃣ Data Sharding: Splits databases for better performance

1️⃣6️⃣ Data Partitioning: Divides datasets for parallel processing

1️⃣7️⃣ Data Source: Origin of raw data (APIs, files, etc.)

1️⃣8️⃣ Data Schema: Blueprint for database structure

1️⃣9️⃣ DWA: Automates warehouse creation and management

2️⃣0️⃣ Metadata: Context about data (e.g., types, relationships)

Building your first AI agent can feel overwhelming. There are so many tools, frameworks, and steps that it is easy to lose track of where to begin.

To simplify the process, I created a 20-step roadmap that takes you from the idea stage all the way to launch and ongoing maintenance.

Here’s how the journey looks:
1. Define the purpose of your agent with clear success metrics and use cases
2. Select the right development framework like LangChain, AutoGen, or CrewAI
3. Choose a language model such as GPT-4, Claude, or LLaMA 2 based on cost, performance, and needs
4. Outline the core capabilities and limits of your agent
5. Plan tool integrations, APIs, and databases your agent will need
6. Design the agent architecture including input handling, processing, and error management
7. Implement memory management for both short-term and long-term interactions
8. Create prompt templates that are structured and reusable
9. Add context injection for more accurate and personalized responses
10. Enable tool calling for real-world task completion
11. Equip your agent with multi-step reasoning and planning abilities
12. Apply safety filters to prevent harmful or biased outputs
13. Set up monitoring for accuracy, latency, and user feedback
14. Optimize for speed using caching, async calls, and model efficiency
15. Enable continuous learning through retraining, A/B testing, and feedback loops
16. Add multimodal capabilities like text, image, speech, and video
17. Personalize the experience based on user history and preferences
18. Plan deployment strategy across web, mobile, APIs, or on-device
19. Launch with a controlled rollout and proper support in place
20. Maintain and upgrade regularly to stay secure and relevant

Every step builds on the previous one. Together, they form a structured path for turning ideas into real, functional AI agents.

If you are experimenting with agentic AI or planning to build your first agent, this roadmap can be your starting point. Save it for future reference.

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Best,

Ravit Jain

Founder & Host of The Ravit Show