Abhijeet Ramteke’s Post

This chart from dbt Labs' State of Analytics Engineering report is actually uplifting. Today, the biggest two challenges that data teams face (poor data quality and ambiguous ownership) are mostly related to people and processes rather than technical complexity or the data platform itself. This is a big shift from the struggles of the pre-Modern-Data-Stack world. As someone who started working on data platforms during the Hadoop era, it's inspiring to see the industry move past spending endless engineering resources on maintaining complex data infrastructure that had a terrible return on investment. In a way, this is a direct acknowledgment of the Modern Data Stack’s role in solving one of the Hadoop era’s biggest problems: today’s data platform (mostly) just works. Gone are the days of wasting expensive engineering time on building and maintaining the platform. The Modern Data Stack may be dead, but for all its flaws, it solved the data platform’s technical hurdles. Now it’s time to solve the business ones. #dataengineering #moderndatastack #dbt

The Data Engineering Sandwich: Top Layer: Data Ingestion – Think of this as the bread. It's where you gather all the raw data from various sources (APIs, databases, logs) and get it into your system. Middle Layer: Data Processing – The juicy filling. This is where the magic happens—cleaning, transforming, and structuring data so it's actually useful. Tools like Spark, Hadoop, and ETL pipelines work their magic here. Bottom Layer: Data Storage & Management – The base of the sandwich. All that processed data needs to be stored safely in databases, data lakes, or warehouses. Think AWS, Google BigQuery, or Snowflake. Bonus Spread: Data Monitoring & Quality – Like the condiments that hold the sandwich together, this ensures everything works as expected and stays fresh. Enjoy this sandwich, and you’re one step closer to mastering data engineering! #DataEngineering #BigData #ETL #DataPipelines #CloudComputing #DataScience #LinkedInLearning #TechSkills #DeveloperLife

#BroadcastVariable In distributed computing, especially with Apache Spark, broadcast variables are a game-changer. They allow you to efficiently share large read-only data across all nodes in a cluster, minimizing communication overhead and boosting performance. Broadcast variables ensure that your distributed applications run smoothly by reducing the need to send a copy of the data to each node. This is particularly beneficial when using large lookup tables or reference data that remains unchanged throughout the computation. By broadcasting the data once and reusing it across the cluster, you can significantly improve processing times and make your data pipeline more efficient. Broadcast variables work and their importance in Spark >>> https://lnkd.in/grNin9fE #DataEngineering #BigData #ApacheSpark #DistributedComputing #DataProcessing #ETL #DataScience #DataAnalytics #ClusterComputing #SparkOptimization #PerformanceTuning #TechTips #CloudComputing #DataManagement #Hadoop #DataEfficiency #TechTalk #DataTechnology #BigDataSolutions #DataOptimization #DataPipeline #DataArchitecture #DataStrategy #DigitalTransformation #DataInnovation #CloudEngineering #TechCareers #ITCommunity #DataCentric #DataDriven #DataPlatform #CloudData #InformationTechnology #AdvancedAnalytics #DataOps #DigitalStrategy #DataTransformation #AI #MachineLearning #DataGovernance #ITInnovation #DataStorytelling

Little research from last 10 years on ai Working very hard for turing award 2030 #turingaward2030 #Nobletransformationhub #drfaknoble #ArtificialIntelligence #deeplearning #BigData #bigdataanalytics #bigdatatechnologies #bigdatadeveloper #BigDataAnalysis #bigdatahadoop #dataengineer #dataengineering #dataengineerjobs #cloudcomputing #DataWarehouse #datawarehousing #DataLake #dataanalytics #dataanalysis #StatisticallySignificant #datasciencecourse #distribution #dataanalytics

There is something similar between the beginning of the AI data era and the Big Data era. Back when Hadoop first hit it big, proponents swore that the world was different now. Data lakes made SQL obsolete. Modeling was done and schema in write was the way. The old data world was finished! Of course we know how the story ended up. Huge investments in data lakes, most of which failed. The coming of the lake house. It turned out the new way was really an addition and extension of the tried and true. But we lost a lot in the process. Reminds me of now. So here is my prediction. In ten years people will still read reports in PDFs. There will just be some AI happening along the way. #bigdata #hadoop #reporting

Embarking on a transformative journey in the realm of data engineering! With a wealth of experience, this expert thrives in the Analysis, Design, Development, and Testing phases, sculpting intricate ETL data flows. Their expertise extends to crafting sophisticated SQL queries, stored procedures, and triggers, ensuring optimal database performance. They have honed their skills in cutting-edge technologies such as Hadoop, Spark, and AWS, translating complex business needs into seamless data solutions. From building databases to optimizing query performance, they are dedicated to unlocking the true potential of data. A visionary in the world of Data Engineering, they revel in the challenge of turning raw data into actionable insights. Let's innovate, transform, and revolutionize the data landscape together! #DataEngineering #TechInnovation #DataTransformation #AnalyticsMaestro 🚀💻

📂 Common Data File Formats in Data Engineering and Their Uses 📂 In the world of data engineering, choosing the right file format can make a big difference in how efficiently data is processed, stored, and analyzed. Here’s a quick rundown of some of the most commonly used file formats and where they shine: CSV 📄 Simple and human-readable, but lacks efficiency for big data. Best for small datasets and quick data sharing. JSON 📝 Widely used for semi-structured data, particularly in web APIs and log files. Offers flexibility but can become bulky with large datasets. Parquet 🧱 Columnar storage format designed for fast query performance on analytical workloads. Ideal for big data projects using Spark, Hadoop, or AWS. Avro 🚀 Schema-based row-oriented format that’s great for serialization and deserialization. Often used in Kafka for streaming data pipelines. ORC 📊 Similar to Parquet, but designed specifically for Hadoop and offers better compression and performance for read-heavy operations. Each format has its strengths and weaknesses, and the choice depends on the specific use case—whether it's stream processing, batch analytics, or data lake storage. What’s your go-to file format for your data pipelines? 🔧 #DataEngineering #FileFormats #BigData #ETL #CSV #Parquet #Avro #ORC #DeltaLake #ApacheIceberg #CloudComputing

🚀 𝐓𝐡𝐞 𝐅𝐮𝐭𝐮𝐫𝐞 𝐨𝐟 𝐃𝐚𝐭𝐚 𝐖𝐢𝐭𝐡 𝐁𝐢𝐠 𝐃𝐚𝐭𝐚 𝐓𝐞𝐜𝐡𝐧𝐨𝐥𝐨𝐠𝐢𝐞𝐬 🚀 In today’s world, handling vast amounts of data efficiently is crucial. Traditional systems struggle with the scale and complexity, which is where 𝐁𝐢𝐠 𝐃𝐚𝐭𝐚 technologies take over. 🔑 𝐊𝐞𝐲 𝐂𝐨𝐧𝐜𝐞𝐩𝐭𝐬: - 𝐕𝐨𝐥𝐮𝐦𝐞, 𝐕𝐚𝐫𝐢𝐞𝐭𝐲, 𝐕𝐞𝐥𝐨𝐜𝐢𝐭𝐲, 𝐕𝐞𝐫𝐚𝐜𝐢𝐭𝐲, 𝐚𝐧𝐝 𝐕𝐚𝐥𝐮𝐞: These 5V's capture the fundamental challenges of managing Big Data. - 𝐃𝐢𝐬𝐭𝐫𝐢𝐛𝐮𝐭𝐞𝐝 𝐒𝐲𝐬𝐭𝐞𝐦𝐬 offer scalability, effectively replacing the limitations of monolithic systems. - 𝐀𝐩𝐚𝐜𝐡𝐞 𝐇𝐚𝐝𝐨𝐨𝐩 was the foundation of Big Data processing with 𝐇𝐃𝐅𝐒, 𝐌𝐚𝐩𝐑𝐞𝐝𝐮𝐜𝐞, and 𝐘𝐀𝐑𝐍. However, 𝐀𝐩𝐚𝐜𝐡𝐞 𝐒𝐩𝐚𝐫𝐤 has emerged as a faster, in-memory alternative for data processing, allowing businesses to process large datasets at lightning speed. 🔧 𝐊𝐞𝐲 𝐏𝐫𝐨𝐜𝐞𝐬𝐬𝐞𝐬: 1. 𝐈𝐧𝐠𝐞𝐬𝐭𝐢𝐧𝐠 𝐝𝐚𝐭𝐚 from multiple sources into 𝐃𝐚𝐭𝐚 𝐋𝐚𝐤𝐞𝐬 via tools like 𝐒𝐪𝐨𝐨𝐩 or 𝐀𝐳𝐮𝐫𝐞 𝐃𝐚𝐭𝐚 𝐅𝐚𝐜𝐭𝐨𝐫𝐲. 2. 𝐓𝐫𝐚𝐧𝐬𝐟𝐨𝐫𝐦𝐢𝐧𝐠 𝐝𝐚𝐭𝐚 using platforms such as 𝐃𝐚𝐭𝐚𝐛𝐫𝐢𝐜𝐤𝐬 and 𝐀𝐳𝐮𝐫𝐞 𝐒𝐲𝐧𝐚𝐩𝐬𝐞 𝐀𝐧𝐚𝐥𝐲𝐭𝐢𝐜𝐬. 3. 𝐒𝐞𝐫𝐯𝐢𝐧𝐠 𝐭𝐡𝐞 𝐝𝐚𝐭𝐚 through 𝐀𝐳𝐮𝐫𝐞 𝐒𝐐𝐋, 𝐇𝐢𝐯𝐞, or 𝐂𝐨𝐬𝐦𝐨𝐬 𝐃𝐁 for easy access by analytics and visualization tools like 𝐏𝐨𝐰𝐞𝐫 𝐁𝐈 or 𝐓𝐚𝐛𝐥𝐞𝐚𝐮. 💡 𝐁𝐢𝐠 𝐃𝐚𝐭𝐚 𝐓𝐞𝐜𝐡𝐧𝐨𝐥𝐨𝐠𝐢𝐞𝐬 enable businesses to harness massive datasets, offering real-time insights and driving more informed decision-making. Above are a few key insights gained through guidance from Sumit Mittal, focusing on modern Big Data technologies and their applications in data engineering. #BigData #ApacheSpark #Hadoop #AzureDataFactory #DataTransformation #CloudComputing #DataLakes #ETL #NoSQL #AI #DataLakes #DataWarehouse #DataPipelines #DataEngineering #ApacheSpark #Hadoop

🚀 𝐃𝐚𝐭𝐚 𝐄𝐧𝐠𝐢𝐧𝐞𝐞𝐫𝐬: 𝐋𝐞𝐭'𝐬 𝐃𝐢𝐯𝐞 𝐃𝐞𝐞𝐩𝐞𝐫 𝐢𝐧𝐭𝐨 𝐂𝐨𝐦𝐩𝐫𝐞𝐬𝐬𝐢𝐨𝐧 𝐓𝐞𝐜𝐡𝐧𝐢𝐪𝐮𝐞𝐬! We all know how essential compression is for optimizing storage and boosting performance. But beyond the basics, there are some lesser-known details that can supercharge your data pipelines. 📦 𝐆𝐳𝐢𝐩, 𝐒𝐧𝐚𝐩𝐩𝐲, 𝐏𝐚𝐫𝐪𝐮𝐞𝐭, 𝐎𝐑𝐂 —these are household names in data compression, but did you know? 🔍 𝐁𝐫𝐨𝐭𝐥𝐢: Developed by Google, Brotli is a 𝐡𝐢𝐝𝐝𝐞𝐧 𝐠𝐞𝐦 that offers 𝐮𝐩 𝐭𝐨 𝟑𝟎% 𝐛𝐞𝐭𝐭𝐞𝐫 𝐜𝐨𝐦𝐩𝐫𝐞𝐬𝐬𝐢𝐨𝐧 𝐫𝐚𝐭𝐢𝐨𝐬 compared to Gzip. Originally designed for web content, it’s now finding its way into big data because of its 𝐜𝐨𝐦𝐛𝐢𝐧𝐚𝐭𝐢𝐨𝐧 𝐨𝐟 𝐬𝐩𝐞𝐞𝐝 𝐚𝐧𝐝 𝐞𝐟𝐟𝐢𝐜𝐢𝐞𝐧𝐜𝐲. Brotli is particularly great for text-heavy data like JSON, making it a fantastic choice for web logs or semi-structured data storage. 🧠 𝐃𝐢𝐜𝐭𝐢𝐨𝐧𝐚𝐫𝐲 𝐄𝐧𝐜𝐨𝐝𝐢𝐧𝐠: A feature of columnar formats like Parquet and ORC, dictionary encoding works by creating a reference table of unique values and replacing 𝐫𝐞𝐟𝐞𝐫𝐞𝐧𝐜𝐞 𝐭𝐚𝐛𝐥𝐞 with shorter codes. This drastically reduces file sizes for categorical data (e.g., repeated strings or enum values). This is especially useful in datasets with high cardinality, where the same values appear many times over. ⚡ 𝐃𝐞𝐥𝐭𝐚 𝐄𝐧𝐜𝐨𝐝𝐢𝐧𝐠: Perfect for 𝐭𝐢𝐦𝐞-𝐬𝐞𝐫𝐢𝐞𝐬 𝐝𝐚𝐭𝐚 or datasets with numerical sequences. Instead of storing each individual value, delta encoding stores the 𝐝𝐢𝐟𝐟𝐞𝐫𝐞𝐧𝐜𝐞 𝐛𝐞𝐭𝐰𝐞𝐞𝐧 𝐜𝐨𝐧𝐬𝐞𝐜𝐮𝐭𝐢𝐯𝐞 𝐯𝐚𝐥𝐮𝐞𝐬. For example, in stock market data or sensor logs, where changes between readings are more important than the actual value, delta encoding slashes the storage needed while maintaining accuracy. 🗂️ 𝐙𝐬𝐭𝐚𝐧𝐝𝐚𝐫𝐝 (𝐙𝐬𝐭𝐝): One of the most versatile compression algorithms, Zstd offers a unique 𝐛𝐚𝐥𝐚𝐧𝐜𝐞 𝐛𝐞𝐭𝐰𝐞𝐞𝐧 𝐜𝐨𝐦𝐩𝐫𝐞𝐬𝐬𝐢𝐨𝐧 𝐬𝐩𝐞𝐞𝐝 𝐚𝐧𝐝 𝐫𝐚𝐭𝐢𝐨. It’s highly configurable, allowing you to 𝐟𝐢𝐧𝐞-𝐭𝐮𝐧𝐞 settings for either faster compression or higher density. Zstd is a great fit for high-velocity data pipelines where both 𝐩𝐞𝐫𝐟𝐨𝐫𝐦𝐚𝐧𝐜𝐞 𝐚𝐧𝐝 𝐬𝐢𝐳𝐞 𝐦𝐚𝐭𝐭𝐞𝐫, like in ETL jobs or batch processing systems. 🤔 What’s your go-to compression trick for balancing speed, cost, and performance? Any hidden gems you've come across? Let’s elevate the conversation and learn from each other! #DataEngineering #CompressionTechniques #BigData #DataOptimization #CloudData #ETL #DataPipelines #DataStorage #DataArchitecture #Hadoop #Spark

𝗕𝗲𝗰𝗼𝗺𝗲 𝗔𝗺𝗮𝘇𝗶𝗻𝗴 𝗗𝗮𝘁𝗮 𝗘𝗻𝗴𝗶𝗻𝗲𝗲𝗿 𝗶𝗻 𝟮𝟬𝟮𝟰 🔥🔥🔥 Proudly Announcing another 6 months Indepth Data Engineering Program, which starts from Scratch, covering the advance concepts 𝗪𝗵𝗮𝘁 𝘄𝗲 𝗼𝗳𝗳𝗲𝗿? ✅Scala ✅Neo4j ✅Spark. ✅AWS ✅Kafka. ✅DSA ✅ElasticSearch ✅Airflow ✅ 4 Real time Project(No Covid Analysis,Uber Analysis,Titanic ) 𝗦𝗰𝗵𝗲𝗱𝘂𝗹𝗲: Date: 16th-Mar 2024 (Sat) 7:00 PM to 8:30 PM IST 17th-Mar 2024 (Sun) 7:00 PM to 8:30 PM IST 18th-Mar Onwards 7:30 AM to 9:00 AM IST 𝗗𝗲𝗺𝗼? Attend 4 weeks of Session(Scala, Spark) Free, to understand if the course is for you. 𝗛𝗼𝘄 𝘁𝗼 𝗘𝗻𝗿𝗼𝗹𝗹? Register in the link shared in the First Comment to be a part of our Program. Even if you just want to get started with the fundamental, You are free to join us Live for 4 weeks and take it forward on your own. You have nothing to lose. 𝗙𝗲𝗲: INR 35400 payable in 5 EMI (7400 + 7000+7000+7000+7000) Note: You will pay the EMI manually and its not a auto-debit. You can stop payment anytime if the course is not as per your expectation. #dataengineering #bigdata #onlinelearningcenter #hadoop #data #datascience