Holger Mueller’s Post

Hear from @DHenschen giviving an in-depth report LIVE from @Teradata Possible LA & unpacks the implications of the big announcements - BYO-LLM & GPU acceleration options, giving customers flexibility 4 #genAI #innovation https://buff.ly/3BDAA71 #Possible2024 #TeradataPossible

Hear from Doug Henschen as he gives an in-depth report LIVE from @Teradata Possible LA and unpacks the implications of the big announcements - BYO-LLM and GPU acceleration options, giving customers flexibility for #genAI #innovation https://bit.ly/4eFhdc6  #Possible2024 #TeradataPossible

Historians, data lakes and traditional databases are too slow. Quasar DB changes the game for those who need a new paradigm in time series databases. What we do that others don't is : ▫️ High-speed data ingestion: Capture data with virtually no latency, ensuring real-time insights. ▫️ Advanced compression algorithms: Store massive time series data sets with minimal storage footprint. ▫️ Sub-second complex queries: Access and analyze historical data instantly for informed decision-making. ▫️ Built-in aggregation functions: Simplify feature extraction and reduce CPU workload. https://lnkd.in/eJS5wTdU #RealTimeData #StreamingDataAnalytics #PredictiveAnalytics #MachineLearning #TimeSeriesForRealTime

HELLO CONNECTIONS #100daysofrtl 👨💻Day-37:Universal Shift Register. 🚀Universal Shift Register: A register that can store the data and /shifts the data towards the right and left along with the parallel load capability is known as a universal shift register. It can be used to perform input/output operations in both serial and parallel modes. Unidirectional shift registers and bidirectional shift registers are combined together to get the design of the universal shift register. 🚀Operation: Universal shift registers are capable of performing 3 operations as listed below. Parallel load operation – stores the data in parallel as well as the data in parallel. Shift left operation – stores the data and transfers the data shifting towards left in the serial path. Shift right operation – stores the data and transfers the data by shifting towards right in the serial path. ⚙️Applications: 1. Datastorage. 2.Serial-to-Parallel Conversion. 3.Parallel-to-serial conversion. 4. Serial data Processing. 5.Sequence Generation. check my repository for more details GITHUB link:- https://lnkd.in/gX_NZN82 #verilog #rtldesign #xilinx #github #vlsi #hdl #digitalelectronics #learning #vivado #100daysofRTL

HPE GreenLake for File Storage can address the biggest challenges many enterprises face today in its IT infrastructure to support AI workloads. The video shows how a Large Language Model (LLM) with Retrieval-Augmented Generation (RAG) works and a demo of a private instance of a chatbot using LLM+RAG with its inferencing workload supported by a HPE GreenLake for File Storage via RDMA and GPUDirect. #HPEGreenLake #storage #HPE

Maximizing Efficiency in Data Compression with Zstandard for Delta Tables In the world of data analytics, the choice of compression algorithm can significantly impact performance. For Delta tables in Databricks, Zstandard (zstd) stands out as the superior choice. Here's why: 1️⃣High Compression Ratio: Zstd compresses data more efficiently, resulting in smaller storage footprints. 2️⃣Speedy Reads: Zstd's fast decompression enhances read operations, crucial for analytics. 3️⃣Scalability: Handles large datasets with ease, ensuring your data pipelines remain efficient. 4️⃣Modern Hardware Optimization: Designed to leverage the latest in CPU and GPU advancements. To switch to Zstd compression in Databricks, simply set your configuration as follows: spark.conf.set("spark.sql.parquet.compression.codec", "zstd") So zstd can unlock faster, more efficient data processing! #DataAnalytics #BigData #Databricks #DeltaTables #Zstandard #Compression #DataEngineering #PerformanceOptimization 🌟

"Crunching Numbers with Swagger: Unveiling the CPU Core Dance in Databricks!" 💡 💡 #Databricks #DataProcessing #CPUCoreMagic #BigData #Analytics #DataScience #TechTalk #Optimization #DataWorkflow #MachineLearning #ComputePower #EfficiencyBoost #DataEngineering #ApacheSpark

#Compute involves three key elements: ✅ Accurate data representation ✅ Effective algorithms ✅ The underlying mathematics that supports it What possibilities emerge when you excel in all three? Watch now to hear #Hazelcast Chief Data Scientist Stephen Weston explore the capabilities of #DistributedCompute and #EdgeCompute.

No other filesystem on the planet can promote data into the very servers using it, and offer full POSIX access directly to it from within the (unmodified) Linux kernel. Hyperscale NAS Tier 0 and Data Orchestration are game changers!

🚀 Why is Snappy compression the go-to choice for big data files? When compressing large files, there are two key factors to consider: speed and compression ratio. 1️⃣ Speed: The speed of compression refers to how quickly a compression algorithm can compress a file. More complex algorithms require more CPU cycles, resulting in slower compression speeds. 2️⃣ Compression Ratio: Compression ratio is the amount by which a file is reduced in size after compression. Higher compression ratios generally result in smaller file sizes but can come at the cost of slower compression speeds. Snappy compression strikes a balance between speed and compression ratio, offering moderate compression with high speed. This makes it a preferred choice for processing big data files efficiently. 🎯 Keep in mind, if your goal is long-term archival, a compression method with a higher compression ratio may be more suitable, even if it sacrifices some speed. Follow me Sampath Vijayan for more insights and tips. Tagging Sumit Mittal sir for better reach. #spark #databricks #pyspark #dataengineer #trendytech #ctgdatabit