Apache Doris’ Post

Apache Hudi is transforming distributed data management! With its scalable, real-time solutions, businesses can elevate their data game and achieve their management goals. Apache Hudi ensures data quality, drives analytics excellence, and transforms data workflows. Follow posts from Shiyan Xu on Apache Hudi to gain deeper insights. Connect with me to learn how Apache Hudi can help you manage your data. #ApacheHudi #DataManagement #BigData 🚀

ScyllaDB, along with #ApacheCassandra, HBase, RocksDB, and #CockroachDB, use the log-structured merge-tree data structure. Learn more about the LSM Tree in this free #ScyllaDB University lesson: https://ow.ly/Ky7y50PBVcE #TechTips #NoSQL #TechTraining #database

💡 Tuesday tech tip: ScyllaDB, along with #ApacheCassandra, HBase, RocksDB, and #CockroachDB, use the log-structured merge-tree data structure. Learn more about the LSM Tree in this free #ScyllaDB University lesson: https://ow.ly/Ky7y50PBVcE #TechTips #NoSQL #TechTraining #database

🔗 New read on Medium! 📘 I've just published an article exploring the integration of Apache Spark with DuckDB to optimize data analytics operations. This setup proves to be a game-changer for handling vast datasets with incredible efficiency. 🚀 Highlights include: - Step-by-step guide on combining Apache Spark with DuckDB - Insights into the performance benefits of this integration Ideal for data professionals looking to enhance their toolkit. Check it out and let me know your thoughts or experiences with these technologies! 👉 Read the full article here: Enhancing Data Analytics: Connecting Apache Spark and DuckDB for Optimal Performance - (https://lnkd.in/d3cuhWpx) #DataEngineering #BigData #TechInnovation #ApacheSpark #DuckDB

Let's make the lives of data engineers easier! At Knowi , we've got the strategy: 1️⃣ Forget about ETL/ELT. You don't need it with Knowi. There are no complex workflows or convoluted steps just to pull your data in. — 2️⃣ Your data should stay where it is, and you only bring in what's necessary. Let's keep everything efficient and minimize the headache of data migration. — 3️⃣ Use the native query language of your database. Be it Mongo, Elastic, SQL, or our in-house SQL-like syntax. We're all about flexibility. — 4️⃣ Forget maintaining connectors for SQL, API, or NoSQL data. We handle that for you so your engineers can focus on what matters. — 5️⃣ No more silos or disjointed data. We're here to help you pull from and join all your data sources, including both NoSQL and SQL. — At Knowi , we're making data engineering smoother, faster, and more efficient – giving engineers the tools to excel without the added complications. Discover a new way to handle your data today. Book a demo: https://lnkd.in/gtCgZYEA #EngineeringSimplified #ETL #DataQuery #BusinessIntelligence

#Technology #DataAnalytics #DataDriven Building a Batch Data Pipeline with Athena and MySQL: An End-To-End Tutorial for Beginners Continue reading on Towards Data Science » #MachineLearning #ArtificialIntelligence #DataScience

Building a Batch Data Pipeline with Athena and MySQL: An End-To-End Tutorial for Beginners Continue reading on Towards Data Science » #MachineLearning #ArtificialIntelligence #DataScience

💡 Tuesday tech tip: #ScyllaDB provides the functionality to automatically delete expired data according to Time to Live (TTL). The TTL can be set when defining a Table or using the INSERT and UPDATE queries, as seen in this free ScyllaDB University lesson: http://ow.ly/Kmbb50MWTHH #TechTip #NoSQL #NoSQLdatabase

🔍 Delta Lake vs. Apache Hudi: The Next Step to a Data Lakehouse 🏔️ My comparison results - Read/Write Performance Achieving a seamless and cost-effective data architecture is paramount in our data-driven world. That's why our data team is building a data lakehouse as the future of data management. This is so that we can 🔸 Save cost 🔸 Scale 🔸 Avoid vendor lock If you haven't heard about data lakehouse technologies and open table formats, here's a quick run-through: 🏠 What's a Data Lakehouse? A data lakehouse combines the strengths of data lakes with the functionality of traditional data warehouses. It offers the best of both worlds—scalability and cost-effectiveness, without compromising on key features like ACID transactions, Schema Enforcement, and BI Support. 🚀🏢 📂 Open Table Formats Explained Open formats like Delta Lake, Apache Hudi, and Apache Iceberg are paving the way for organized and efficient data lakes. Delta Lake, for instance, utilizes log files and data files for effective data management, while maintaining historical changes through changelogs. 📊📁 🔍 The Experiment and Results We ran a comprehensive test on 23.4GB of Parquet data using AWS Glue with 3 G1X workers (3 DPU). Our experiment covered bulk writes/inserts, upserts, and read operations across different stages. 📈 The Verdict When it comes to Read/Write Performance, Delta Lake outshines Hudi ✅🚀 Regardless of whether you opt for the MoR or CoW version, Delta Lake consistently demonstrates superior performance, especially as data scales. 📊 🧩 Discovering Hudi's Cool Secrets Although Delta Lake takes the crown in this performance battle, it's worth noting that Hudi boasts unique benefits beneath the surface. For one, it assigns a unique identifier for every record in your data, while delta doesn't. Let me know if you would like to know more! Nicholas Leong #deltalake #dataengineering #datascience #dataanalytics #apachespark

✴ Z-ordering optimization in Delta Lake involves organizing data files within a table based on a Z-ordering or Morton ordering scheme. This technique arranges data in a linear sequence, optimizing data retrieval and processing for spatial or range-based queries. Here's how Z-ordering works in Delta Lake optimization: 1️⃣ Spatial Indexing: Delta Lake leverages Z-ordering to organize data files in a multidimensional space, such as geographical coordinates or timestamp ranges. By interleaving the bits of each dimension's value, Z-ordering creates a linear sequence that preserves locality, ensuring nearby data points are stored close to each other in the sequence. 2️⃣ Data Skew Reduction: Z-ordering helps reduce data skew by evenly distributing data across partitions or files based on their Z-ordering values. This minimizes hot spots and imbalance in data distribution, leading to more balanced query execution and improved parallelism. 3️⃣ Range-Based Query Optimization: Z-ordering optimizes range-based queries by clustering related data points together in the linear sequence. This reduces the amount of data that needs to be scanned during query execution, resulting in faster query performance and reduced I/O overhead. 4️⃣ Predicate Pushdown: Delta Lake's Z-ordering optimization allows for efficient predicate pushdown, where query predicates are pushed down to the data files based on their Z-ordering values. This enables the query engine to skip irrelevant data files during query execution, further improving query performance. 5️⃣ Delta Lake Integration: Z-ordering optimization is seamlessly integrated into Delta Lake, allowing users to enable it for specific tables or partitions using configuration options or DDL commands. Delta Lake automatically manages Z-ordering metadata and file organization, simplifying optimization and maintenance tasks for data engineers and administrators. Overall, Z-ordering optimization in Delta Lake provides an efficient and scalable approach to organizing and accessing multidimensional data, leading to improved query performance, reduced data skew, and better resource utilization in data lake environments. #sql #mysql #dataanalytics #database #datascience #dataanalysis #dataengineering #dataanalyst #bigdata #cloud #bigdataengineer #database #datawarehouse #datalake #pyspark #python