A quick start guide to using HDF5 files in GLOBE ClaritasGuy Maslen
GLOBE Claritas V6.0 includes support for a new data format based on the HDF5 standard; here's how to get started with HDF5 files, and the benefits they bring
A quick start guide to using HDF5 files in GLOBE ClaritasGuy Maslen
GLOBE Claritas V6.0 includes support for a new data format based on the HDF5 standard; here's how to get started with HDF5 files, and the benefits they bring
This tutorial is designed for new HDF5 users. We will go over a brief history of HDF and HDF5 software, and will cover basic HDF5 Data Model objects and their properties; we will give an overview of the HDF5 Libraries and APIs, and discuss the HDF5 programming model. Simple C and Fortran examples, and Java tool HDFView will be used to illustrate HDF5 concepts.
The executable formats (PE, ELF, HEX, SREC AND ...)Medhat HUSSAIN
- Bare metal executables .
- SREC
- HEX
- VBF
- OS Executables
- Windows executable format
- Linux executable format POSIX
- Executable permission
- File system basics
- Conversion in-between (Windows and Linux )
- Wine project
LINKs:
https://www.heise.de/download/product...
https://sourceforge.net/projects/npp-...
https://www.winehq.org/
A powerful feature in Postgres called Foreign Data Wrappers lets end users integrate data from MongoDB, Hadoop and other solutions with their Postgres database and leverage it as single, seamless database using SQL.
Use of these features has skyrocketed since EDB released to the open source community new FDWs for MongoDB, Hadoop and MySQL that support both read and write capabilities. Now greatly enhanced, FDWs enable integrating data across disparate deployments to support new workloads, expanded development goals and harvesting greater value from data.
To view the recorded presentation please visit Enterprisedb.com > Resources > On Demand Webcasts
Contact EnterpriseDB with your questions - sales@enterprisedbc.om
All about Storage - Series 2 Defining DataDAGEOP LTD
All about Storage - Series 2 Defining Data
=> Data & Data Types
=> Text and Image Locations
=> Page Structures & Internals
by Dr. Subramani Paramasivam
Hitachi Unified Storage and Hitachi NAS Platform 4000 Series -- DatasheetHitachi Vantara
HUS and HNAS 4000 series product overview, key features and technical specifications. For more information on Hitachi Unified Storage and Hitachi NAS Platform 4000 Series please visit: http://www.hds.com/products/file-and-content/network-attached-storage/?WT.ac=us_mg_pro_hnasp
Hitachi Unified Storage and Hitachi NAS Platform, 4000 series, product overview, key features, business value description and technical specifications.
Sql Bits 2020 - Designing Performant and Scalable Data Lakes using Azure Data...Rukmani Gopalan
Cloud Storage is evolving rapidly, and our Azure Storage portfolio has added a ton of new industry leading capabilities. In this session you will learn the do's and don'ts of building data lakes on Azure Data Lake Storage. You will learn about the commonly used patterns, how to set up your accounts and pipelines to maximize performance, how to organize your data and various options to secure access to your data. We will also cover customer use cases and highlight planned enhancements and upcoming features.
This tutorial is designed for new HDF5 users. We will go over a brief history of HDF and HDF5 software, and will cover basic HDF5 Data Model objects and their properties; we will give an overview of the HDF5 Libraries and APIs, and discuss the HDF5 programming model. Simple C and Fortran examples, and Java tool HDFView will be used to illustrate HDF5 concepts.
The executable formats (PE, ELF, HEX, SREC AND ...)Medhat HUSSAIN
- Bare metal executables .
- SREC
- HEX
- VBF
- OS Executables
- Windows executable format
- Linux executable format POSIX
- Executable permission
- File system basics
- Conversion in-between (Windows and Linux )
- Wine project
LINKs:
https://www.heise.de/download/product...
https://sourceforge.net/projects/npp-...
https://www.winehq.org/
A powerful feature in Postgres called Foreign Data Wrappers lets end users integrate data from MongoDB, Hadoop and other solutions with their Postgres database and leverage it as single, seamless database using SQL.
Use of these features has skyrocketed since EDB released to the open source community new FDWs for MongoDB, Hadoop and MySQL that support both read and write capabilities. Now greatly enhanced, FDWs enable integrating data across disparate deployments to support new workloads, expanded development goals and harvesting greater value from data.
To view the recorded presentation please visit Enterprisedb.com > Resources > On Demand Webcasts
Contact EnterpriseDB with your questions - sales@enterprisedbc.om
All about Storage - Series 2 Defining DataDAGEOP LTD
All about Storage - Series 2 Defining Data
=> Data & Data Types
=> Text and Image Locations
=> Page Structures & Internals
by Dr. Subramani Paramasivam
Hitachi Unified Storage and Hitachi NAS Platform 4000 Series -- DatasheetHitachi Vantara
HUS and HNAS 4000 series product overview, key features and technical specifications. For more information on Hitachi Unified Storage and Hitachi NAS Platform 4000 Series please visit: http://www.hds.com/products/file-and-content/network-attached-storage/?WT.ac=us_mg_pro_hnasp
Hitachi Unified Storage and Hitachi NAS Platform, 4000 series, product overview, key features, business value description and technical specifications.
Sql Bits 2020 - Designing Performant and Scalable Data Lakes using Azure Data...Rukmani Gopalan
Cloud Storage is evolving rapidly, and our Azure Storage portfolio has added a ton of new industry leading capabilities. In this session you will learn the do's and don'ts of building data lakes on Azure Data Lake Storage. You will learn about the commonly used patterns, how to set up your accounts and pipelines to maximize performance, how to organize your data and various options to secure access to your data. We will also cover customer use cases and highlight planned enhancements and upcoming features.
An overview of Hadoop Storage Format and different codecs available. It explains which are available and how they are different and which to use where.
Serverlesss Big Data Analytics with Amazon Athena and QuicksightAmazon Web Services
Check out how you can easily query raw data in various formats in Amazon S3, transform it into a canonical form, analyze it, and build dashboards to get more insights from your data.
Django Files — A Short Talk (slides only)James Aylett
My Django Under the Hood 2015 talk, which includes a whistlestop tour of Django File with HTTP, Form & ORM integration, Storage backends, contrib.staticfiles, Form.Media and asset pipelines, including some thoughts on how to integrate arbitrary asset pipelines with Django.
Canllawiau CBHC ar gyfer Archifau Archaeolegol Digidol – Ymagwedd Gynaliadwy ...RCAHMW
Cofnod Henebion Cenedlaethol (CHC) CBHC yw archif cyhoeddus Cymru o gofnodion yn ymwneud â’r amgylchedd hanesyddol, a dyma’r cartref cenedlaethol ar gyfer archifau archaeolegol digidol. Yn unol â hyn, mae wrthi’n datblygu ei gyfleusterau a gweithdrefnau archifo digidol i gydymffurfio â safonau rhyngwladol, sef model cyfeiriol y System Gwybodaeth Archifol Agored (OAIS) – OAIS (ISO 14721). I sicrhau cydymffurfiad effeithiol a dichonadwy, mae’n bwriadu mabwysiadu pecyn archifau digidol sy’n bodloni safonau’r diwydiant, wedi’i gynhyrchu gan Preservica, fel rhan o’i blatfform data presennol. Bydd hyn yn sicrhau cydymffurfiad â llifoedd gwaith OAIS, bod cynnwys digidol yn cael ei ddiogelu, a bod y cyhoedd yn gallu cyrchu cofnodion digidol.
Er mwyn sicrhau bod derbynion digidol yn cael eu derbyn a’u hymgorffori yn y system hon mor effeithlon â phosibl, ac mewn modd cynaliadwy sy’n cymryd lefelau staffio i ystyriaeth, mae CBHC wedi creu canllawiau ar gyfer archifau digidol. Mae’r rhain yn nodi sut y dylai cynhyrchwyr data yn y sector sy’n bwriadu rhoi cofnodion ar adnau yn CHC drefnu, disgrifio a fformatio archifau archaeolegol digidol. Bwriedir i’r canllawiau gael eu defnyddio o ddechrau prosiect, ac fe’u cynhwysir fel atodiad i Safonau Cenedlaethol Cymru ar gyfer Casglu ac Adneuo Archifau Archaeolegol a gyhoeddir maes o law. Cânt eu lledaenu hefyd drwy’r drefn caniatâd cynllunio.
Bydd y sgwrs yn amlinellu gofynion y model cyfeiriol OAIS ac yn dangos sut y mae CBHC yn gweithredu i gydymffurfio ag ef. Bydd yn egluro’r gofynion cyffredinol yn y canllawiau yn y cyd-destun hwn, gan roi pwyslais ar yr angen am ddata wedi’u strwythuro’n dda a metadata disgrifiadol digonol i ganiatáu ar gyfer cadwraeth ddigidol ac, yn bwysicaf oll, gallu defnyddwyr data i gyrchu a defnyddio’r archif.
Levelwise PageRank with Loop-Based Dead End Handling Strategy : SHORT REPORT ...Subhajit Sahu
Abstract — Levelwise PageRank is an alternative method of PageRank computation which decomposes the input graph into a directed acyclic block-graph of strongly connected components, and processes them in topological order, one level at a time. This enables calculation for ranks in a distributed fashion without per-iteration communication, unlike the standard method where all vertices are processed in each iteration. It however comes with a precondition of the absence of dead ends in the input graph. Here, the native non-distributed performance of Levelwise PageRank was compared against Monolithic PageRank on a CPU as well as a GPU. To ensure a fair comparison, Monolithic PageRank was also performed on a graph where vertices were split by components. Results indicate that Levelwise PageRank is about as fast as Monolithic PageRank on the CPU, but quite a bit slower on the GPU. Slowdown on the GPU is likely caused by a large submission of small workloads, and expected to be non-issue when the computation is performed on massive graphs.
Adjusting primitives for graph : SHORT REPORT / NOTESSubhajit Sahu
Graph algorithms, like PageRank Compressed Sparse Row (CSR) is an adjacency-list based graph representation that is
Multiply with different modes (map)
1. Performance of sequential execution based vs OpenMP based vector multiply.
2. Comparing various launch configs for CUDA based vector multiply.
Sum with different storage types (reduce)
1. Performance of vector element sum using float vs bfloat16 as the storage type.
Sum with different modes (reduce)
1. Performance of sequential execution based vs OpenMP based vector element sum.
2. Performance of memcpy vs in-place based CUDA based vector element sum.
3. Comparing various launch configs for CUDA based vector element sum (memcpy).
4. Comparing various launch configs for CUDA based vector element sum (in-place).
Sum with in-place strategies of CUDA mode (reduce)
1. Comparing various launch configs for CUDA based vector element sum (in-place).
Analysis insight about a Flyball dog competition team's performanceroli9797
Insight of my analysis about a Flyball dog competition team's last year performance. Find more: https://github.com/rolandnagy-ds/flyball_race_analysis/tree/main
Unleashing the Power of Data_ Choosing a Trusted Analytics Platform.pdfEnterprise Wired
In this guide, we'll explore the key considerations and features to look for when choosing a Trusted analytics platform that meets your organization's needs and delivers actionable intelligence you can trust.
Learn SQL from basic queries to Advance queriesmanishkhaire30
Dive into the world of data analysis with our comprehensive guide on mastering SQL! This presentation offers a practical approach to learning SQL, focusing on real-world applications and hands-on practice. Whether you're a beginner or looking to sharpen your skills, this guide provides the tools you need to extract, analyze, and interpret data effectively.
Key Highlights:
Foundations of SQL: Understand the basics of SQL, including data retrieval, filtering, and aggregation.
Advanced Queries: Learn to craft complex queries to uncover deep insights from your data.
Data Trends and Patterns: Discover how to identify and interpret trends and patterns in your datasets.
Practical Examples: Follow step-by-step examples to apply SQL techniques in real-world scenarios.
Actionable Insights: Gain the skills to derive actionable insights that drive informed decision-making.
Join us on this journey to enhance your data analysis capabilities and unlock the full potential of SQL. Perfect for data enthusiasts, analysts, and anyone eager to harness the power of data!
#DataAnalysis #SQL #LearningSQL #DataInsights #DataScience #Analytics
Enhanced Enterprise Intelligence with your personal AI Data Copilot.pdfGetInData
Recently we have observed the rise of open-source Large Language Models (LLMs) that are community-driven or developed by the AI market leaders, such as Meta (Llama3), Databricks (DBRX) and Snowflake (Arctic). On the other hand, there is a growth in interest in specialized, carefully fine-tuned yet relatively small models that can efficiently assist programmers in day-to-day tasks. Finally, Retrieval-Augmented Generation (RAG) architectures have gained a lot of traction as the preferred approach for LLMs context and prompt augmentation for building conversational SQL data copilots, code copilots and chatbots.
In this presentation, we will show how we built upon these three concepts a robust Data Copilot that can help to democratize access to company data assets and boost performance of everyone working with data platforms.
Why do we need yet another (open-source ) Copilot?
How can we build one?
Architecture and evaluation
2. Evaluation Criteria
- The processing tools
- i.e Cloudera do not support ORC
- Whether data has a changing nature or not
- Splitability
- XML is not splittable
- Compression
- Speed up I/O operation
- Save Storage
- Increase processing time : DECOMPRESSION!
- The data size
- Processing and query performance
3. Common File Formats
All File Formats
ColumnarStandard
Sequence Data Structure Data Parquet ORC
Serialization
Avro
4. Summary of some file formats’ features
Data Format Type of Format Splittable Changing Compression Meta Data
Json, XML Standards - + - +
CSV File Standards + - - -
JSON Records Standards + + - +
Sequence Files Standards + - + -
Avro Files Serialization + + + +
ORC Files Columnar + + + +
Parquet Files Columnar + + + +
5. Sequence File
- An optimal solution for small files
- Save as <key, value>
- Support compression
- Record
- Block
6. Parquet
- Optimized for Impala
- Used by Twitter
- Data Structure
- Data partitioned into rows
- Pages can be compressed
8. ORC
- Optimized for Hive, Presto
- Data Structure
- Index contain basic statistics
- File footer contain a list of stripes information
- Postscript holds compression parameters
9. Avro
- Row base storage
- Found in Apache Kafka
- Robust Support for changing schema
- Data Structure
10. Avro vs Parquet
- Avro is ideal for ETL
- Parquet is ideal for query analysis
- Read operation is better in Parquet
- Write operation is better in Avro
- Avro support full changing schema
- Parquet just support append
11. Parquet vs ORC
- Parquet is better for nested data
- ORC is more compression efficient