Nnaemeka Obodoekwe

Just in case you want to explain the difference between ETL (extract-transform-load) and ELT (extract-load-transform) approaches in data engineering to your 5 y.o. child, I have something for you. It’s actually easy: either you get something, store it, and make sense out of it later; or you get something, make some sense out of it, and store only the results. What is better? Depends on the use case and a few parameters like size of your storage space and complexity of the processing needed. UPD: for more information about ETL and ELT please check the episode of my video-blog, which is dedicated to this topic: https://lnkd.in/eP52bjFc #data #leadership #etl #elt #apples

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𝗕𝗶𝗴 𝗗𝗮𝘁𝗮 𝗣𝗶𝗽𝗲𝗹𝗶𝗻𝗲 𝗖𝗵𝗲𝗮𝘁𝘀𝗵𝗲𝗲𝘁: 𝗔𝗪𝗦, 𝗔𝘇𝘂𝗿𝗲, 𝗮𝗻𝗱 𝗚𝗖𝗣: In today’s data-driven world, cloud-native big data pipelines are essential for extracting insights and maintaining a competitive edge. Here’s a concise breakdown of key components across AWS, Azure, and GCP: 𝟭. 𝗗𝗮𝘁𝗮 𝗜𝗻𝗴𝗲𝘀𝘁𝗶𝗼𝗻: AWS: Kinesis (real-time), AWS Data Pipeline (managed workflows) Azure: Event Hubs (real-time streaming), Data Factory (ETL) GCP: Pub/Sub (real-time), Dataflow (batch & stream processing) 𝟮. 𝗗𝗮𝘁𝗮 𝗟𝗮𝗸𝗲: AWS: S3 with Lake Formation for secure data lakes Azure: Azure Data Lake Storage (ADLS), integrates with HDInsight & Synapse GCP: Google Cloud Storage (GCS) with BigLake for unified data management 𝟯. 𝗖𝗼𝗺𝗽𝘂𝘁𝗲 & 𝗣𝗿𝗼𝗰𝗲𝘀𝘀𝗶𝗻𝗴 AWS: EMR (managed Hadoop/Spark), Glue (serverless data integration) Azure: Databricks (Spark-based analytics), HDInsight (Hadoop) GCP: Dataproc (managed Spark/Hadoop), Dataflow (Apache Beam-based processing) 𝟰. 𝗗𝗮𝘁𝗮 𝗪𝗮𝗿𝗲𝗵𝗼𝘂𝘀𝗶𝗻𝗴 AWS: Redshift – scalable, high-performance data warehousing Azure: Synapse Analytics – combines SQL Data Warehouse & big data processing GCP: BigQuery – serverless, highly scalable, cost-effective analytics 𝟱. 𝗕𝘂𝘀𝗶𝗻𝗲𝘀𝘀 𝗜𝗻𝘁𝗲𝗹𝗹𝗶𝗴𝗲𝗻𝗰𝗲 & 𝗩𝗶𝘀𝘂𝗮𝗹𝗶𝘇𝗮𝘁𝗶𝗼𝗻 AWS: QuickSight – scalable BI & reporting Azure: Power BI – deeply integrated with Microsoft ecosystem GCP: Looker – flexible data visualization & analytics Each cloud provider has unique strengths. Selecting the right combination of ingestion, storage, compute, and analytics tools is key to building scalable, cost-effective big data pipelines. Whether handling real-time streaming or deep data warehousing or batch processing, choosing wisely can optimize both efficiency and costs. Image Credits : ByteByteGo Alex Xu 🔈 For Regular Job & Data related updates, check out my Data Community to learn, share and grow together!! https://lnkd.in/g-ZtB4Yf Please Like, repost ✅, if you find them useful. #DataPipeline #data #ETL #dataengineering #datawarehouse

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Apache Parquet Vs New File Formats. There’s no denying the impact Apache Parquet has had on the analytics ecosystem. - Columnar layout - Strong compression & encoding - The de facto format in data lakes and warehouses for over a decade But there are also a lot of new things happening with our analytical workloads. ❌ Workloads are no longer just batch analytics - they involve AI pipelines, low-latency use cases, and hardware-accelerated compute ❌ Hardware is not only CPU but also GPU, from x86 to ARM/RISC-V, with wide SIMD instruction sets becoming the norm ❌ And performance bottlenecks are seen in decompression speed, memory pressure and lack of vectorized execution paths As a result, we’re seeing a wave of innovation around next-gen file formats. Efforts like: BtrBlocks – introduces cascaded compression with lightweight codecs Nimble (Meta) – optimized for fast scan & inference LanceDB – tailored for vector search and ML use cases And the past week I read about “Fastlanes” Here are some highlights from their VLDB paper ✅ Expression Encoding: Flexible chains of lightweight codecs (FFOR, DELTA, DICT, FSST) that outperform heavyweight compression like Zstd ✅ Multi-Column Compression: Exploits inter-column correlations (e.g. equality, one-to-one mappings) to go beyond traditional columnar compression ✅ Segmented Layout: Decompresses in small vectors (1024 values) rather than rowgroups, reducing memory pressure and improving cache efficiency ✅ Compressed Execution Support: Returns compressed vectors to engines like DuckDB/Velox for SIMD/GPU-friendly query execution Here are some numbers I picked up on Performance: - 800× faster random access than Parquet+Zstd - 41% better compression than Parquet+Snappy - 2% better compression than Parquet+Zstd (without using heavyweight codecs) - Decoding accelerates with AVX512 (up to 40% faster) I think we are going to see more of fit-for-purpose formats in the future as we tackle specific use cases around AI workloads and to target performance. Paper link in comments. #dataengineering #softwareengineering

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Data pipelines have evolved and you need to keep up! Here's what you need to know: 🔹 ETL (Extract, Transform, Load): Preprocesses data before loading to conserve storage and compute (common in the Hadoop era). 🔹 ELT (Extract, Load, Transform): Loads raw data directly into scalable cloud storage, transforming later for agility and flexibility. 🔹 Streaming: Processes data continuously, ideal for real-time applications like fraud detection or live dashboards. 🔹 Zero-ETL: Tightly integrates operational and analytical databases, eliminating explicit pipeline steps for faster, simpler data syncing. 🔹 Data Sharing: Grants direct query access without moving data, perfect for secure external collaboration or cross-departmental data sharing. Each has unique strengths and successful data teams often blend these patterns depending on their needs. SQLMesh is always the right answer 😉. Am I a corporate shill? Maybe. But I AM DEFINITELY an open source shill 👍 Ready to learn? Follow - Zach Wilson and his DataExpert.io Bootcamps Joe Reis and his Practical Data Discord Carly Taylor, MSc and her @carlytaylor Substack Madison Schott and her Learn Analytics Engineering Substack Tobias (Toby) Mao at Tobiko (creator of SQLMesh and SQLGlot) Lester Martin 🥑 at Starburst + all things Trino Software Foundation Who else? #dataengineering #sql #datatransformation

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Indexes are one of the most powerful levers you can pull for PostgreSQL performance. When designed with the right strategy, they can transform query speed and unlock efficiency at scale. But balance matters. - Well-placed indexes make filtering and joins seamless. - Thoughtfully aligned multi-column indexes keep queries sharp. - Regular reviews ensure indexing evolves with your application. On the other hand, adding indexes without a strategy can create extra overhead and slow down writes. The real performance boost comes not from more indexes, but from the right ones, tuned to your access patterns. A few checkpoints that consistently bring clarity: - pg_stat_user_indexes to validate index usage - pg_index to keep index health in check - EXPLAIN ANALYZE to confirm efficient access paths Getting indexing right is one of the most effective ways to troubleshoot performance and set your PostgreSQL environment up for long-term success. I wrote more about this in a blog that breaks down practical steps: https://lnkd.in/dr6HecjV

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❄️#How Snowflake transformed one of my most complex ETL projects ----------------------------------------------------------------------------------- . . . . . . Every growing business eventually hits the same wall: data pipelines that can’t keep up with scale. One of my recent clients faced this challenge. Their ETL pipelines were slow, expensive, and rigid. Queries often failed under heavy loads, and adding new data sources required weeks of rework. Leadership wanted faster insights at lower cost—but the legacy stack simply couldn’t deliver. We rebuilt their architecture with Snowflake at the center: 🔹 AWS S3 for raw data ingestion and secure storage. 🔹 Databricks for heavy transformations, leveraging Spark to process billions of records. 🔹 Snowflake as the cloud data warehouse, powering clean, analytics-ready datasets. What made Snowflake a game-changer? Separation of compute & storage → ETL pipelines ran independently of analyst queries. Virtual warehouses → Scaled compute up or down in minutes. Time Travel & Zero-Copy Cloning → Allowed safe testing, debugging, and rollback with no extra cost. Seamless integration → Fit perfectly with AWS + Databricks for a modern data ecosystem. ✅ Pipelines that once took 5+ hours finished in under 40 minutes. ✅ Query performance improved by 10x. ✅ Infrastructure costs dropped significantly thanks to autoscaling. 💡 Takeaway: Snowflake isn’t just a data warehouse—it’s a business enabler. When paired with the right orchestration and transformation tools, it unlocks speed, scalability, and cost-efficiency. 👉 If you’re looking to modernize your ETL pipelines or migrate to a Snowflake-based data platform, let’s connect. Always happy to share insights—or help design a solution tailored to your needs. #DataEngineering #Snowflake #AWS #Databricks #ETL #BigData #CloudComputing #FreelanceDataEngineer #DataPipeline

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⚡ Spark – Creating Custom UDFs and Using Pandas UDFs for Scalability 🐼🚀 Need to run custom Python logic on your Spark DataFrame? Spark lets you define UDFs (User Defined Functions)—and for speed, you can use Pandas UDFs to process data in vectorized batches. ⚡ 💡 How? See code snippet below 🔗 Why Pandas UDFs? - Process data in vectorized batches, avoiding Python overhead - Works great for numeric-heavy transformations - Keeps your logic scalable while still writing in Python 💡 Pro Tip: Use Pandas UDFs over standard UDFs whenever possible—they can give a massive performance booston large datasets. 🔹 Follow me for daily DataFrame manipulation tips and other great data engineering content!

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