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Deep Dives

Data Quality Design Patterns (10 minute read)

Modern data pipeline quality control leverages patterns like Write–Audit–Publish (WAP), Audit–Write–Audit–Publish (AWAP), Transform–Audit–Publish (TAP), and the Signal Table Pattern to balance data integrity, cost, and latency. WAP and AWAP use staging and multiple audits to block bad data from production, while TAP streamlines by validating in-memory to cut storage and I/O expenses, and Signal Table prioritizes speed but with less safety. Selecting the right approach ensures reliable pipelines, downstream trust, and business value.

Triton: Scaling Bulk Operations with a Feed Processing Platform (8 minute read)

Triton is a centralized feed processing platform that can handle massive bulk operations like updating millions of product listings, inventory, or catalog attributes via file uploads. It eliminates duplicated efforts across domain teams and ensures consistent reliability, scalability, and governance. Its architecture features Coordinator-Master-Worker orchestration using ZooKeeper, chunking/partitioning for workload distribution, Apache Pulsar for decoupling phases, hybrid storage, and Vert.x for non-blocking API calls, enabling high throughput.

The Real-Time Data Journey: Connecting Flink, Airflow, and StarRocks (5 minute read)

Fresha's real-time streaming architecture integrates Debezium CDC data from PostgreSQL into Kafka. StarRocks supports ingestion via three main methods: Routine Load, the Kafka Connector, and the Flink Connector. Key trade-offs involve transformation complexity, delivery semantics, schema evolution, operational considerations, balancing performance, data freshness, and integration needs.

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Opinions & Advice

Translating Data Buzzwords into Real Requirements (6 minute read)

Buzzwords like "modern data stack," "data lakehouse," or "real-time analytics" often mask vague expectations. Before picking tools and writing pipelines, you must translate these abstractions into actual requirements: who needs the data, what SLA, which data products, how lineage and governance are enforced, etc. Without going through the process of clarifying requirements and design patterns, you risk building complexity for appearances and failing to deliver actual business value.

ULID: Universally Unique Lexicographically Sortable Identifier (5 minute read)

ULID encodes a 48-bit timestamp followed by 80-bit randomness into a 26-character Base32 string, combining global uniqueness with lexicographic sortability. With the Go library oklog/ulid + a standard UUID-typed primary key in PostgreSQL, you can swap in ULIDs with no schema change, getting time-ordered, compact, human-friendlier IDs. This makes ULIDs a compelling alternative to UUIDs when you care about insert order, index locality, or query performance over time-series or high-throughput workloads.

How to Use Simple Data Contracts in Python for Data Scientists (5 minute read)

Using simple data contracts in Python helps turn fuzzy data expectations into explicit, enforceable agreements between data producers and consumers. Tools like Pandera let you define and validate table schemas before any downstream processing, catching structural and semantic errors early. This makes data pipelines more stable, auditable, and scalable without needing heavy infrastructure.

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Launches & Tools

Cloud storage has always forced a tradeoff: fast or affordable. Why not both? (Sponsor)

Choosing between performance and cost shouldn't be a decision when scaling your cloud file systems. Cloud Native Qumulo on AWS delivers both: scale from 100TB to 100EB with over 1TB/s throughput, at up to 80% less cost than alternatives. Supports NFS, SMB, S3, and FTP without refactoring. Takes 6 minutes to deploy. Learn more about CNQ on AWS

Automating Customer Support with JSM Virtual Agent (6 minute read)

Atlassian's engineering team developed the JSM Virtual Agent, an AI-powered feature in Jira Service Management (JSM), to automate customer support chats by unifying previously inconsistent channel architectures, implementing a sophisticated Retrieval-Augmented Generation system with query personalization, multi-source search, advanced ranking, and safeguards against hallucinations. This resulted in nearly half of chat queries being resolved automatically via AI, a 40% improvement in customer satisfaction scores, and support for over 20 languages.

How the 5 Major Cloud Data Warehouses Really Bill You: A Unified, Engineer-friendly Guide (20 minute read)

Compute billing models for Snowflake, Databricks SQL Serverless, ClickHouse Cloud, Google BigQuery, and Amazon Redshift Serverless depend on the usage of different units, scaling behaviors, and metering rules that make direct price comparisons misleading without understanding real query execution. By introducing the open-source Bench2Cost tool, it enables reproducible cost-per-query benchmarks, showing ClickHouse Cloud's advantages in transparency, flexibility, and value for analytical workloads.

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Miscellaneous

Securing the Model Context Protocol (MCP): Risks, Controls, and Governance (45 minute read)

MCP greatly expands an AI system's attack surface by allowing agents to call external tools and data sources, creating vectors for content-injection, poisoned tool responses, compromised MCP servers, and excessive privileges. Risks include data exfiltration, cross-system escalation, and stealthy manipulation of model outputs. Mitigation requires strict privilege boundaries, sandboxed tool execution, precise input/output validation, provenance tracking, and private, vetted MCP registries, treating MCP as critical infrastructure rather than a plugin layer.

Hybrid Intelligence: Why AI Fails Without Human Psychological Architecture (15 minute read)

AI adoption failures within enterprises are rarely caused by technical shortcomings. Instead, human psychological and organizational barriers are the primary culprits. Only 6% of organizations succeed at scaling AI, with top performers three times more likely to redesign workflows, establish human-in-the-loop controls, and foster trust and psychological safety. The proposed "Cognition × Culture × Control" framework drives adoption by emphasizing cognitively ergonomic tools, transparent and participatory cultures, and retaining employee agency.

Decoding High-bandwidth Memory: A Practical Guide to GPU Memory for Fine-tuning AI (6 minute read)

Full fine-tuning is memory-heavy and often impractical. Combine LoRA/QLoRA, quantization, and FlashAttention to fine-tune efficiently on modest GPUs (16–24 GB). For larger scale, use multi-GPU setups on Google Cloud. Experimentation is key due to framework overheads.

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