Advanced Cloud Analytics Tools

𝗔𝗻𝗸𝗶𝘁𝗮: You know 𝗣𝗼𝗼𝗷𝗮, last Monday our new data pipeline was live in cloud and it failed terribly. Literally had an exhaustive week fixing the critical issues. 𝗣𝗼𝗼𝗷𝗮: Ohh, so don’t you use Cloud monitoring for data pipelines? From my experience always start by tracking these four key metrics: latency, traffic, errors, and saturation. It helps you to check your pipeline health, if it's running smoothly or if there’s a bottleneck somewhere.. 𝗔𝗻𝗸𝗶𝘁𝗮: Makes sense. What tools do you use for this? 𝗣𝗼𝗼𝗷𝗮: Depends on the cloud platform. For AWS, I use CloudWatch—it lets you set up dashboards, track metrics, and create alarms for failures or slowdowns. On Google Cloud, Cloud Monitoring (formerly Stackdriver) is awesome for custom dashboards and log-based metrics. For more advanced needs, tools like Datadog and Splunk offer real-time analytics, anomaly detection, and distributed tracing across service. 𝗔𝗻𝗸𝗶𝘁𝗮: And what about data lineage tracking? How do you track when something goes wrong, it's always a nightmare trying to figure out which downstream systems are affected. 𝗣𝗼𝗼𝗷𝗮: That's where things get interesting. You could simply implement custom logging to track data lineage and create dependency maps. If the customer data pipeline fails, you’ll immediately know that the segmentation, recommendation, and reporting pipelines might be affected. 𝗔𝗻𝗸𝗶𝘁𝗮: And what about logging and troubleshooting? 𝗣𝗼𝗼𝗷𝗮: Comprehensive logging is key. I make sure every step in the pipeline logs events with timestamps and error details. Centralized logging tools like ELK stack or cloud-native solutions help with quick debugging. Plus, maintaining data lineage helps trace issues back to their source. 𝗔𝗻𝗸𝗶𝘁𝗮: Any best practices you swear by? 𝗣𝗼𝗼𝗷𝗮: Yes, here’s what’s my mantra to ensure my weekends are free from pipeline struggles - Set clear monitoring objectives—know what you want to track. Use real-time alerts for critical failures. Regularly review and update your monitoring setup as the pipeline evolves. Automate as much as possible to catch issues early. 𝗔𝗻𝗸𝗶𝘁𝗮: Thanks, 𝗣𝗼𝗼𝗷𝗮! I’ll set up dashboards and alerts right away. Finally, we'll be proactive instead of reactive when it comes to pipeline issues! 𝗣𝗼𝗼𝗷𝗮: Exactly. No more finding out about problems from angry business users. Monitoring will catch issues before they impact anyone downstream. In data engineering, a well-monitored pipeline isn’t just about catching errors—it’s about building trust in every insight you deliver. #data #engineering #reeltorealdata #cloud #bigdata

Let’s do this! I speak to so many leaders and get so many insights into how the space is evolving! “Data 3.0 in the Lakehouse era,” using this map as a guide. Data 3.0 is composable. Open formats anchor the system, metadata is the control plane, orchestration glues it together, and AI use cases shape choices. Ingestion & Transformation - Pipelines are now products, not scripts. Fivetran, Airbyte, Census, dbt, Meltano and others standardize ingestion. Orchestration tools like Prefect, Flyte, Dagster and Airflow keep things moving, while Kafka, Redpanda and Flink show that streaming is no longer a sidecar but central to both analytics and AI. Storage & Formats - Object storage has become the system of record. Open file and table formats—Parquet, Iceberg, Delta, Hudi—are the backbone. Warehouses (Snowflake, Firebolt) and lakehouses (Databricks, Dremio) co-exist, while vector databases sit alongside because RAG and agents demand fast recall. Metadata as Control - This is where teams succeed or fail. Unity Catalog, Glue, Polaris and Gravtino act as metastores. Catalogs like Atlan, Collibra, Alation and DataHub organize context. Observability tools—Telmai, Anomalo, Monte Carlo, Acceldata—make trust scalable. Without this layer, you might have a modern-looking stack that still behaves like 2015. Compute & Query Engines - The right workload drives the choice: Spark and Trino for broad analytics, ClickHouse for throughput, DuckDB/MotherDuck for frictionless exploration, and Druid/Imply for real-time. ML workloads lean on Ray, Dask and Anyscale. Cost tools like Sundeck and Bluesky matter because economics matter more than logos. Producers vs Consumers - The left half builds, the right half uses. Treat datasets, features and vector indexes as products with owners and SLOs. That mindset shift matters more than picking any single vendor. Trends I see • Batch and streaming are converging around open table formats. • Catalogs are evolving into enforcement layers for privacy and quality. • Orchestration is getting simpler while CI/CD for data is getting more rigorous. • AI sits on the same foundation as BI and data science—not a separate stack. This is my opinion of how the space is shaping up. Use this to reflect on your own stack, simplify, standardize, and avoid accidental complexity!!!! ---- ✅ I post real stories and lessons from data and AI. Follow me and join the newsletter at www.theravitshow.com

In-Place Analytics and S3 Tables – With or Without Amazon Web Services (AWS) Proprietary Tools 🙌 What if you could perform analytics on your data without ever moving it?⏩ - With advancements like S3 Tables and in-place analytics, that’s exactly what we can do. This is transforming the way we think about data lakes and lakehouses. In-Place Analytics Without Data Movement: - The core idea of in-place analytics is that data stays where it resides—typically in storage like Amazon S3—and query engines process it directly without moving it to a warehouse. This is made possible by open tools like: • #Trino: Distributed querying for large-scale datasets. • #DuckDB: Lightweight, in-process SQL for local or small-scale queries. • #ApacheDrill: Ad-hoc querying on semi-structured data. These engines enable true in-place analytics on S3, without needing to load the data into an intermediate system like a data warehouse. No data movement, no duplication. Enter S3 Tables: A New Era for Lakehouses✨ With S3 Tables and table formats like Apache Iceberg, advanced features like ACID transactions, schema evolution, and time travel are now built directly into the storage layer. Does this mean you still need external query engines? AWS Proprietary Tools: #Glue and #Athena If you’re within the AWS ecosystem, you now have an alternative to external engines: 1. Athena: A serverless SQL query engine optimized for querying S3 Tables. It handles advanced Iceberg features like schema evolution and partition pruning. 2. Glue: Provides metadata management and seamless integration with Iceberg tables. With these AWS-native tools, you can achieve in-place analytics without needing external engines like Trino, DuckDB, or Drill. Key Takeaways 1. In-Place Analytics Without Proprietary Tools: • You can still use open query engines (e.g., Trino, DuckDB, Drill) to achieve in-place analytics on S3. • These tools are open, flexible, and vendor-neutral. 2. AWS Tools Simplify the Process: • For users fully within the AWS ecosystem, Glue and Athena provide managed, serverless solutions, eliminating the need for configuring and maintaining external engines. 3. True Lakehouse Architecture: • With S3 Tables and Iceberg, S3 evolves into a direct lakehouse, merging storage and advanced table functionality. What Does This Mean for You? • If you value vendor neutrality: Continue using external query engines like Trino or DuckDB for in-place analytics across storage systems. • If you prefer AWS-native simplicity: Leverage S3 Tables with Athena and Glue for seamless integration and managed analytics. In either case, data stays where it resides, and you avoid costly data movement and duplication. What’s Your Take? Are you exploring in-place analytics with S3 Tables or still relying on external query engines? Let’s discuss ! 🚀 #DataLakehouse #InPlaceAnalytics #AWS #S3Tables #ApacheIceberg #DataEngineering #Trino #DuckDB #CloudData #ModernDataStack #OpenSource