Data Pipelining Across AWS and GCP
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Several considerations for data pipelining across AWS and GCP include ease of use, speed, and cost. While AWS may be easier to use and cheaper in general, GCP offers services that are fully-managed and have faster data processing. The two cloud providers also differ in their data warehouse approaches, with Redshift using a schema-on-write model and BigQuery using schema-on-read.
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This document summarizes key announcements from re:Invent 2016, AWS's annual user conference. The main themes included artificial intelligence, serverless computing, devops, data, and migration tools. Notable product announcements included AWS Batch for batch processing, Aurora for PostgreSQL, Athena for querying data lakes, and X-Ray for debugging distributed applications. The document also discusses AWS's strategy around machine learning and deep learning using MXNet as its primary framework.Cool NoSQL on Azure with DocumentDB
Cool NoSQL on Azure with DocumentDBJan Hentschel
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This document summarizes a presentation about DocumentDB on Azure. It discusses what DocumentDB is, how it works as a fully managed NoSQL database, and some key features for developers. DocumentDB allows storing and querying JSON documents, offers tunable consistency levels, and exposes APIs for common languages like .NET, Node.js, and Python. The presentation provides an overview of DocumentDB's capabilities and when it would be a good fit compared to relational databases or other document stores.OSS DB on Azure
OSS DB on Azurerockplace
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This document provides an overview of using open source databases on Microsoft Azure. It discusses trends in open source databases and how Azure supports popular open source databases like MySQL, MariaDB, and PostgreSQL as fully managed database services. It covers benefits of migrating on-premises or third party databases to Azure databases, including cost savings, global scale, built-in high availability, security, and integration with other Azure services. Migration from commercial databases like Oracle to open source databases on Azure like PostgreSQL is also discussed.Introduction to AWS Glue: Data Analytics Week at the SF Loft
Introduction to AWS Glue: Data Analytics Week at the SF LoftAmazon Web Services
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Introduction to AWS Glue: Data Analytics Week at the San Francisco Loft
AWS Glue is a fully managed extract, transform, and load (ETL) service that makes it easy for customers to prepare and load their data for analytics. You can create and run an ETL job with a few clicks in the AWS Management Console. You simply point AWS Glue to your data stored on AWS, and AWS Glue discovers your data and stores the associated metadata (e.g. table definition and schema) in the AWS Glue Data Catalog. Once cataloged, your data is immediately searchable, queryable, and available for ETL. AWS Glue generates the code to execute your data transformations and data loading processes.
Level: Intermediate
Speakers:
John Mallory - Principal Business Development Manager, Storage, AWS
Asim Kumar Sasmal - Big Data Consultant, AWS Professional ServicesNoSQL vs SQL (by Dmitriy Beseda, JS developer and coach Binary Studio Academy)
NoSQL vs SQL (by Dmitriy Beseda, JS developer and coach Binary Studio Academy)Binary Studio
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It is first lecture from noSQL course for students of Lviv Polytechnic National University. Check out our educational portal: http://academy.binary-studio.com/
Augmenting Mongo DB with Treasure Data
Augmenting Mongo DB with Treasure DataTreasure Data, Inc.
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Sometimes , some things work better than other things. MongoDB is great for quick access to low-latency data; Treasure Data is great for infinitely scalable historical data store. A lambda architecture is also explained.Augmenting Mongo DB with treasure data
Augmenting Mongo DB with treasure dataTreasure Data, Inc.
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How to get the best of both: MongoDB is great for low latency quick access of recent data; Treasure Data is great for infinitely growing store of historical data. In the latter case, one need not worry about scaling.Recently uploaded (20)
Large Language Models (LLMs) part one.pptx
Large Language Models (LLMs) part one.pptxharmardir
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**The Rise and Impact of Large Language Models (LLMs)**
**Introduction**
In the rapidly evolving landscape of artificial intelligence (AI), one of the most groundbreaking advancements has been the development of Large Language Models (LLMs). These AI systems, trained on massive amounts of text data, have demonstrated remarkable capabilities in understanding, generating, and manipulating human language. LLMs have transformed industries, reshaped the way people interact with technology, and raised ethical concerns regarding their usage. This essay delves into the history, development, applications, challenges, and future of LLMs, providing a comprehensive understanding of their significance.
**Historical Background and Development**
The foundation of LLMs is built on decades of research in natural language processing (NLP) and machine learning (ML). Early language models were relatively simple and rule-based, relying on statistical methods to predict word sequences. However, the emergence of deep learning, particularly the introduction of neural networks, revolutionized NLP. The introduction of recurrent neural networks (RNNs) and long short-term memory (LSTM) networks in the late 1990s and early 2000s allowed for better sequential data processing.
The breakthrough moment for LLMs came with the development of Transformer architectures, introduced in the seminal 2017 paper "Attention Is All You Need" by Vaswani et al. The Transformer model enabled more efficient parallel processing and improved context understanding. This led to the creation of models like BERT (Bidirectional Encoder Representations from Transformers) and OpenAIs GPT (Generative Pre-trained Transformer) series, which have since set new benchmarks in AI-driven text generation and comprehension.
**Core Mechanisms of LLMs**
LLMs rely on deep neural networks trained on extensive datasets comprising books, articles, websites, and other textual resources. The training process involves:
1. **Tokenization:** Breaking down text into smaller units (words, subwords, or characters) to be processed by the model.
2. **Pretraining:** The model learns general language patterns through unsupervised learning, predicting missing words or the next sequence in a text.
3. **Fine-tuning:** Adjusting the model for specific tasks, such as summarization, translation, or question-answering, using supervised learning.
4. **Inference:** The trained model generates text based on user input, leveraging probabilistic predictions to produce coherent responses.
Through these mechanisms, LLMs can perform a wide range of linguistic tasks with human-like proficiency.
**Applications of LLMs**
LLMs have found applications across various domains, including but not limited to:
1. **Content Generation:** LLMs assist in writing articles, blogs, poetry, and even code, helping content creators enhance productivity.
2. **Customer Support:** Chatbots and virtual assistants powered by LLMs provide automated yet contOptimizing Common Table Expressions in Apache Hive with Calcite
Optimizing Common Table Expressions in Apache Hive with CalciteStamatis Zampetakis
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In many real-world queries, certain expressions may appear multiple times, requiring repeated computations to construct the final result. These recurring computations, known as common table expressions (CTEs), can be explicitly defined in SQL queries using the WITH clause or implicitly derived through transformation rules. Identifying and leveraging CTEs is essential for reducing the cost of executing complex queries and is a critical component of modern data management systems.
Apache Hive, a SQL-based data management system, provides powerful mechanisms to detect and exploit CTEs through heuristic and cost-based optimization techniques.
This talk delves into the internals of Hive's planner, focusing on its integration with Apache Calcite for CTE optimization. We will begin with a high-level overview of Hive's planner architecture and its reliance on Calcite in various planning phases. The discussion will then shift to the CTE rewriting phase, highlighting key Calcite concepts and demonstrating how they are employed to optimize CTEs effectively.9th Edition of International Research Awards
9th Edition of International Research Awardssciencereviewerview
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9th Edition of International Research Awards |28-29 March 2025 | San Francisco, United States
The International Research Awards recognize exceptional research contributions, innovation, and excellence across various fields. This prestigious award honors outstanding researchers, scientists, and scholars who have made significant impacts in their respective disciplines, fostering a culture of innovation and discovery.Globalwits Global Standard Quotation 2025
Globalwits Global Standard Quotation 2025AvenGeorge1
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Globalwits GTIS6.0, the next-generation foreign trade big data analysis tool, covering 50%+ of global trade volume across 82 countries, 6 continents, and 255 trade databases. Designed to empower businesses with actionable insights, GTIS6.0 combines real-time analytics, AI-driven decision-making, and multi-dimensional intelligence to transform your global trade strategy.
Why Choose GTIS6.0?
Comprehensive Data Coverage
Track 100+ processing parameters, including buyer/supplier details, pricing trends, trade routes, and more.
Access real-time customs transaction data with 24/7 updates across 255 global trade databases.
Advanced Analytics & Visualization
Leverage RFM analysis, price interval tracking, and supply chain breakdown tools.
Generate customizable, business-grade reports with interactive charts (histograms, heatmaps, relational graphs).
Precision Targeting & Efficiency
Millisecond-level search speed powered by ElasticSearch and Hadoop technologies.
Identify high-potential markets and customers through multi-layered trade relationship mapping (up to 8 layers).
Seamless Integration & Resource Optimization
Bridge transaction data, public records, and business insights in one platform.
Utilize the Quality Supply-Demand Resource Sharing Platform to connect with verified buyers/suppliers worldwide.
Key Features:
Market Analysis: Visualize regional trade trends, product cycles, and competitor landscapes.
Customer Profiling: Evaluate buyer strength via procurement frequency, transaction value, and RFM metrics.
Decision-Maker Access: Retrieve official emails, LinkedIn profiles, and direct contacts to accelerate negotiations.
Dynamic Pricing Strategy: Analyze global price ranges and terms of trade for science-based quotations.
Real-Time Alerts: Monitor shifts in customer demand, partner changes, and supply chain risks.
SEO-Optimized Keywords:
Global Trade Data Analytics Tool, GTIS6.0 System, Foreign Trade Intelligence, Customs Data Insights, B2B Market Research, Supplier and Buyer Analysis, RFM Metrics, Trade Relationship Mapping, Globalwits Solutions, International Trade Optimization
Globalwits Your Partner in Data-Driven Success
As the global standard for trade data, Globalwits bridges the gap between raw data and strategic decisions. Whether analyzing macro trends, profiling customers, or optimizing pricing, GTIS6.0 delivers unparalleled accuracy, speed, and depth.
Contact Us Today to Revolutionize Your Trade Strategy!
Director WhatsApp: +86 171 402 01001
Director Email: anyuchen@globalwits.comKey Benefits of Implementing Contify's M&CI Platform
Key Benefits of Implementing Contify's M&CI PlatformContify
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Contify's Market & Competitive Intelligence platform empowers businesses with real-time insights, automated intelligence gathering, and AI-driven analytics. It enhances decision-making, streamlines competitor tracking, and delivers personalized intelligence reports. With Contify, organizations gain a strategic edge by identifying trends, mitigating risks, and seizing growth opportunities in dynamic market landscapes.
For more information please visit here https://www.contify.com/platform/Design Data Model Objects for Analytics, Activation, and AI
Design Data Model Objects for Analytics, Activation, and AIaaronmwinters
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Explore using industry-specific data standards to design data model objects in Data Cloud that can consolidate fragmented and multi-format data sources into a single view of the customer.
Design of the data model objects is a critical first step in setting up Data Cloud and will impact aspects of the implementation, including the data harmonization and mappings, as well as downstream automations and AI processing. This session will provide concrete examples of data standards in the education space and how to design a Data Cloud data model that will hold up over the long-term as new source systems and activation targets are added to the landscape. This will help architects and business analysts accelerate adoption of Data Cloud.
The truth behind the numbers: spotting statistical misuse.pptx
The truth behind the numbers: spotting statistical misuse.pptxandyprosser3
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As a producer of official statistics, being able to define what misinformation means in relation to data and statistics is so important to us.
For our sixth webinar, we explored how we handle statistical misuse especially in the media. We were also joined by speakers from the Office for Statistics Regulation (OSR) to explain how they play an important role in investigating and challenging the misuse of statistics across government.ev3-software-tutorial-dc terminologia en ibgles.ppt
ev3-software-tutorial-dc terminologia en ibgles.ppthectormartinez817322
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en ingles esta prressetnacion es muy buena lasdla lkaskl siempreew aaslkd llasaslas as asasd lk aslk al kjal kj allks
When Models Meet Data: From ancient science to todays Artificial Intelligence...
When Models Meet Data: From ancient science to todays Artificial Intelligence...ssuserbbbef4
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A presentation about Data and Machine Learning
PRGTUG: Lost in Data? Let's Chart the Way Out!
PRGTUG: Lost in Data? Let's Chart the Way Out!Stanislava Tropcheva
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Prague Tableau User Group (PRGTUG) Meeting:
Lost in Data?
Let's Chart the Way Out!
27th February 2025
Plant Disease Prediction with Image Classification using CNN.pdf
Plant Disease Prediction with Image Classification using CNN.pdfTheekshana Wanniarachchi
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Plant diseases pose a significant threat to agricultural productivity. Early detection and classification of plant diseases can help mitigate losses. This project focuses on building a Plant Disease Prediction system using Convolutional Neural Networks (CNNs). The system leverages NumPy, TensorFlow, and Streamlit to develop a model and deploy a web-based application. The final model is also containerized using Docker for efficient deployment.Scaling & Measurement, Classification, and Types
Scaling & Measurement, Classification, and Typessrikanthmrt
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Scaling and Measuring usefiul for Data analysis and Research Purpose.Data Pipelining Across AWS and GCP
- 1. Data Pipelining Across AWS and GCP stanley@repute.io Note that this document is based on my individual opinion.
- 2. Data Pipeline Architecture 2 CloudFront ELB EC2 S3 RDS Stackdriver GCS BigQuery ML Engine Datalab Data Studio
- 3. Which do you like? Why? A B C D 3
- 4. Several Considerations Q1) How easy? Q2) How fast? Q3) How cheap? 4 AWS GCP Different from person to person Depends on which more familiar with Documented in Korean Ready-made Fully-managed Application response (Korea region) Data processing Cheaper in general
- 5. Other Considerations 5 Q4) How transparent? Q5) What different?
- 6. Q4) How transparent? Opaque 6 https://en.wikipedia.org/wiki/Data_warehouse
- 7. Q4) How transparent? Honest and fair! 7
- 11. Athena vs. BigQuery 11 Athena BigQuery Schema-on-read Reads are slow No loads Schema-on-write Reads are fast Loads are also fast Support View that can derive new schema on read time External Table (just links of S3 objects; on-demand parsing) Native Table (pre-parsed) Support External Table additionally (links of GCS objects or Google Sheets) Preview? Preview for free
- 13. Data Pipeline Architecture (Recap) 13 CloudFront ELB EC2 S3 RDS Stackdriver GCS BigQuery ML Engine Datalab Data Studio ≠
- 14. OLTP Data 14 (for job : jobs) { (for task : tasks) { } } RDS BigQuery
- 15. , , , 15 Data How to ingest Files https://cloud.google.com/storage/docs/migr ating Monitoring Metrics https://cloud.google.com/logging/docs/ Application Logs Event Logs https://support.google.com/firebase/answer /6318765