Reliable Data Engineering

Reliable Data Engineering
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Discover how to build, deploy, and maintain data pipelines that scale reliably in production. This podcast features practical lessons on Databricks migrations, dbt best practices, SQL optimization, cost reduction on cloud platforms, and data quality frameworks—all designed to help data engineers avoid costly mistakes.

Выпуски

  1. 11 ЯНВ.

    Data Engineering Is Not One Job Anymore

    Ten years ago, being a generalist worked. In 2026, companies don't hire "tool collectors." They hire specialists who solve specific problems exceptionally well. Here's which path pay and which leaves you unemployable.

    4 мин.

  2. 8 ЯНВ.

    dbt Is Not an ETL Tool — And That’s Okay

    dbt is often treated like a full ETL platform — and that expectation causes more problems than it solves. In this episode, we break down what dbt was actually designed for, where it shines, where it doesn’t, and why accepting its limits leads to cleaner pipelines and better data architecture.

    5 мин.

  3. 8 ЯНВ.

    Comparing Databricks, Snowflake, and BigQuery: Same Query, Real Costs

    A side-by-side analysis of query performance, pricing, and operational differences across three leading data platforms

    6 мин.

  4. 8 ЯНВ.

    I Replaced My Databricks Pipeline with AI Agents — Here’s What Actually Happened

    A story about how AI agents what do for us!

    4 мин.

  5. 3 ЯНВ.

    How AI Will Transform Data Engineering in 2026

    A realistic look at what’s coming and the skills that will keep you ahead of the curve

    12 мин.

  6. 3 ЯНВ.

    Welcome to Reliable Data Engineering - Here's What You'll Learn

    Welcome to Reliable Data Engineering—the podcast for data engineers who build production systems and want to stay ahead of the curve. I'm your host, a data engineer with 6+ years building enterprise-scale data infrastructure. I've migrated 500+ dbt models to Databricks, optimized SQL workloads across multiple platforms, and managed complex data governance at scale. Now I'm sharing the real lessons learned—no hype, no theory, just practical insights from someone still building these systems every day. What This Podcast Is About: Real-world data engineering challenges (migrations, cost optimization, reliability, data quality) Modern data stacks and tools (Databricks, dbt, Apache Spark, cloud platforms) Career guidance for a rapidly evolving field War stories from production incidents and lessons learned How AI is transforming data engineering and what you need to know Who This Is For:Mid-level and senior data engineers managing production systems. If you're navigating legacy-to-modern migrations, optimizing cloud costs, building data quality frameworks, or wondering how AI fits into your career—this show is for you. What You Won't Find Here:No shallow takes on AI replacing engineers. No theoretical tutorials disconnected from production reality. No influencers selling courses. Just honest analysis from someone in the trenches. New episodes every week exploring the real challenges, evolving landscape, and practical strategies that help data engineers thrive. Let's build something reliable.

    1 мин.

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Discover how to build, deploy, and maintain data pipelines that scale reliably in production. This podcast features practical lessons on Databricks migrations, dbt best practices, SQL optimization, cost reduction on cloud platforms, and data quality frameworks—all designed to help data engineers avoid costly mistakes.

Информация

  • Автор
    Reliable Data Engineering
  • Годы выхода
    2 тыс.
  • Выпуски
    6
  • Ограничения
    Без ненормативной лексики
  • Авторские права
    © Reliable Data Engineering
  • Сайт подкаста
    Reliable Data Engineering