🌞Suraj Biyani

SingleStore is a #cloudnative database designed for data-intensive workloads. In the modern datalake architecture, SingleStore fits squarely into the processing layer. This tutorial by databases and datalakes SME Brenna Buuck sets up a robust data stack that allows for experimentation with storing, processing, and querying data in #objectstorage. The integration of SingleStore with MinIO forms an important brick in the modern #datalake stack. Check it out: https://hubs.li/Q02zmw-z0

8

I'm super pumped about Delta Lake UniForm and Apache XTable (Incubating)! The introduction of Delta Lake UniForm and Apache XTable, which now support Delta Lake, Apache Hudi, and Apache Iceberg formats, is a significant advancement in data management. This development allows for seamless interoperability between these formats, reducing the risk of vendor lock-in and enhancing flexibility. The impact is substantial, as it simplifies data workflows, promotes open standards, and ultimately leads to more efficient and effective data management practices.

9

🗞️ Grab the latest edition of our Confluent Developer Newsletter! 🗞️ The new roundup includes helpful resources like: - Localized Hindi (हिंदी) captions for Python developers in our #ApacheKafka 101 course! - A 6-minute video on how exactly once-processing works in #ApacheFlink - Upcoming events around the world 🌎 … and much more! Read it below ⬇️

2

New article by Nischal Subedi, DevOps Engineer on PostgreSQL DB Data Replication Learn to setup Postgres DB data replication and ensure high availability of your Postgres instances.

6

Here’s our latest blog post on What’s New in PostgreSQL 17: Essential Updates for DBAs and Developers https://lnkd.in/daZch-E5

5

🛠️ Efficient Data Partitioning Explained! Follow this example to learn how to design partitions in #AzureCosmosDB for better query performance and reduced costs. 🔗 https://lnkd.in/dAKR89DX #Database #DataModeling

5

🚀 Scaling Redis? Beware of the common mistakes! In our latest blog post, we break down the most frequent pitfalls when scaling Redis, from vertical scaling missteps to replica overloads. Learn how to avoid them and discover how Dragonfly solves these challenges with a more efficient architecture. https://hubs.la/Q02Tvw_20 #Redis #RedisCluster #Valkey #DragonflyDB #Cloud #DevOps #Scaling #Caching

11

🛠️ Implement vector search in #AzureCosmosDB and revolutionize how you handle data queries. Get detailed guidance and best practices to make the most of this feature. Learn more: https://lnkd.in/ez7PxvNs

1

Apache Solr is a powerful tool for website search, but performance can be a challenge. Learn how to optimize your setup with these easy-to-follow tips! https://bit.ly/3yWNLym #Java #OpenJDK

2

In his latest blog, Ahsan Hadi talks about how the improvements to logical replication in PostgreSQL 17 are making it even more robust and reliable for enterprise use, while providing core capabilities that vendors like pgEdge can build on to deliver fully distributed PostgreSQL. Read the blog to learn more. https://hubs.la/Q02TC_gJ0 #pgedge #postgresqlprofessionals #PostgreSQL #logical replication

26

Nice Blog from Alex Merced at Dremio on how Apache XTable works in action and how to sync XTable-synced tables to Dremio's native catalog or open source Nessie 🔥 🔗 Blog Link: https://lnkd.in/deSd5qHG #dataengineering #softwareengineering #lakehouse

20

𝐓𝐞𝐚𝐫𝐢𝐧𝐠 𝐃𝐨𝐰𝐧 𝐭𝐡𝐞 𝐌𝐨𝐧𝐨𝐥𝐢𝐭𝐡 | 𝐓𝐡𝐞 𝐑𝐢𝐬𝐞 𝐨𝐟 𝐌𝐢𝐜𝐫𝐨𝐬𝐞𝐫𝐯𝐢𝐜𝐞𝐬 & 𝐌𝐨𝐝𝐮𝐥𝐚𝐫 𝐀𝐫𝐜𝐡𝐢𝐭𝐞𝐜𝐭𝐮𝐫𝐞 𝐢𝐧 𝐃𝐚𝐭𝐚 𝐄𝐧𝐠𝐢𝐧𝐞𝐞𝐫𝐢𝐧𝐠 Modern Data 101 is here with its latest newsletter. In this edition, the author, Hugo Lu, discussed common anti-patterns in modern data stacks and provided strategies for effectively implementing microservices architecture to optimise cost and performance. The blog delves into the shift from the monolithic to microservices architecture in data engineering, highlighting the drawbacks of monolith systems, such as inflexibility, difficulty in scaling, and high maintenance costs. 𝐊𝐞𝐲 𝐓𝐚𝐤𝐞𝐚𝐰𝐚𝐲𝐬: 💠 𝐌𝐨𝐧𝐨𝐥𝐢𝐭𝐡𝐢𝐜 𝐯𝐬. 𝐌𝐢𝐜𝐫𝐨𝐬𝐞𝐫𝐯𝐢𝐜𝐞𝐬 Monolithic applications are single, self-contained units in which all components are interdependent, leading to challenges in scaling and updating. While the microservices architecture consists of independent, self-sustained units, each handling a specific function and easily interacting with other components. 💠 𝐃𝐫𝐚𝐰𝐛𝐚𝐜𝐤𝐬 𝐨𝐟 𝐌𝐨𝐧𝐨𝐥𝐢𝐭𝐡 Monolithic systems are inflexible and costly to maintain, with any minor update requiring a complete redevelopment of the entire application. Scaling a monolith system is cumbersome, often leading to performance bottlenecks and resource inefficiencies. 💠 𝐁𝐞𝐧𝐞𝐟𝐢𝐭𝐬 𝐨𝐟 𝐌𝐢𝐜𝐫𝐨𝐬𝐞𝐫𝐯𝐢𝐜𝐞𝐬 Microservices architecture enables independent deployment, testing, and scaling of individual units without impacting the entire system. 💠 𝐂𝐨𝐦𝐦𝐨𝐧 𝐚𝐧𝐭𝐢-𝐩𝐚𝐭𝐭𝐞𝐫𝐧𝐬 A prevalent anti-pattern in modern data stacks is the improper use of orchestration tools like Airflow, leading to unnecessary complexity and costs. Recognizing and avoiding these anti-patterns can lead to more efficient and cost-effective data management practices. 💠 𝐂𝐨𝐬𝐭 𝐎𝐩𝐭𝐢𝐦𝐢𝐳𝐚𝐭𝐢𝐨𝐧 Effective implementation of microservices architecture can significantly reduce the total cost of ownership by leveraging cloud resources and distributed computing. Prioritizing cost-effective processing and minimizing redundant infrastructure investments can also lead to substantial savings. 💠 𝐑𝐞𝐚𝐥-𝐖𝐨𝐫𝐥𝐝 𝐄𝐱𝐚𝐦𝐩𝐥𝐞𝐬 Historical monolithic data architectures, such as those built with Airflow, Talend, and Informatica, highlight the challenges of scaling and flexibility. Today, cloud-based solutions and modern data platforms offer more efficient alternatives, though many organizations remain entrenched in older systems. 𝐑𝐞𝐚𝐝 𝐭𝐡𝐞 𝐞𝐧𝐭𝐢𝐫𝐞 𝐛𝐥𝐨𝐠 𝐡𝐞𝐫𝐞: https://lnkd.in/ds29AcBQ 📢 Do share what you feel about this take! 🔔 Follow 𝐌𝐨𝐝𝐞𝐫𝐧 𝐃𝐚𝐭𝐚 101 and stay updated with our weekly highlights from the modern data space.

18

3 commentaren

Benchmarking can be a contentious and complicated topic in the database world. If you're looking for insight and clarity around benchmarking, you'll want to check out this informative and insightful talk from EDB Software Development Engineer Mark Wong. The recording from PostgreSQL Conference Europe highlights how TPC benchmarks are often misused or misrepresented by database vendors. In his talk, "Introduction to Fair-Use TPC Benchmarking Kits", Mark clarified the strict requirements for conducting these benchmarks properly, such as offering a solution with guaranteed 5-year support, running on commercially available systems, and adhering closely to TPC specifications. This talk is essential for anyone in the Postgres community who wants to understand how to properly use benchmarking tools to evaluate and improve their systems. Catch the full talk here: https://lnkd.in/gimKPMkC #EDB #Postgres #PGConfEU #Benchmarking #DatabaseLeadership #PostgreSQL #JustSolveItWithPostgres #EDBPostgresAI #OpenSourceInnovation #DevOps #opensource #Postgres #EDBPostgresAI #data #AI #database #DBA #opensource #PGConfEU #PGConfAthens #TPC

8

In enterprise artificial intelligence, there are two main types of models: discriminative and generative. Discriminative models are used to classify or predict data, while generative models are used to create new data. #DataStorage #MachineLearning #DataScience by Keith Pijanowski thanks to MinIO

5