The document describes the basic steps involved in query processing, including parsing, optimization, and evaluation. It discusses various algorithms for performing relational algebra operations like selection, sorting, and join. Selection algorithms include linear scan, binary search, and using indexes. Sorting can be done by building an index or using external sort-merge. The goal of optimization is to choose the most efficient evaluation plan based on estimated costs.
The document summarizes key aspects of query processing from the textbook "Database System Concepts, 6th Ed." by Silberschatz, Korth and Sudarshan. It discusses the basic steps in query processing including parsing, optimization, and evaluation. It also covers measures of query cost, algorithms for common operations like selection, sorting, and joining, and provides examples of query optimization.
The document discusses various techniques for processing database queries, including:
- Basic steps in query processing: parsing, optimization, and evaluation. Optimization involves choosing the most efficient evaluation plan from equivalent options.
- Measures for estimating query cost, primarily focusing on disk I/O like block transfers and seeks.
- Algorithms for different relational algebra operations like selection, sorting, and join. Selection algorithms include file scan, use of indexes, and handling complex conditions. Sorting algorithms include building an index versus external sort-merge. Join algorithms include nested-loop, block nested-loop, and merge-join.
This document discusses query processing in a database system. It describes the basic steps of query processing as parsing and translation, optimization, and evaluation. For optimization, it explains that a relational algebra expression can be evaluated in many ways and the goal is to choose the plan with the lowest estimated cost. It then covers algorithms for common relational operations like selection, sorting, and join and how they are implemented, including using indexes. The overall focus is on analyzing the costs of different algorithms and implementations.
The document discusses various algorithms for query processing operations like selection, sorting, and join. It provides cost estimates for each algorithm based on factors like the number of block transfers and seeks. The most efficient algorithms depend on characteristics of the relations and whether indices are available. Nested loop and block nested loop joins have high costs, while merge join and hash join may have lower costs depending on the situation.
This document discusses query processing and provides an overview of algorithms for evaluating relational algebra operations. It begins with an overview of the basic steps in query processing - parsing and translation, optimization, and evaluation. It then discusses how to measure query costs by focusing on resource consumption, particularly disk access. The document outlines algorithms for common relational operations like selection, sorting, and join. It provides cost estimates for different algorithms like file scan, index scan, and block nested loops join. The overall summary is that the document describes query processing and evaluation strategies for relational algebra operations like selection and join, providing cost estimates to help optimize queries.
This document discusses query processing and algorithms for evaluating relational algebra operations. It begins with an overview of the basic steps in query processing: parsing and translation, optimization, and evaluation. It then discusses how to measure query costs using a cost model based on disk access times. The document outlines several algorithms (A1-A10) for performing selection operations on relations using file scans and indexes. It provides cost estimates for each algorithm based on factors like the number of blocks accessed and index height. The algorithms can handle selections with equality and inequality conditions, as well as complex selections using conjunctions, disjunctions, and negation.
This document summarizes key concepts from Chapter 13 of the textbook "Database System Concepts". It discusses the basic steps in query processing: parsing and translation, optimization, and evaluation. It also describes various algorithms for common relational algebra operations like selection, sorting, and join. The goal of optimization is to choose the most efficient evaluation plan by estimating the cost of each plan using statistical information about operations and relations. Cost is typically estimated based on the number of disk accesses and seeks required.
The document discusses various steps and algorithms for processing database queries. It covers parsing and optimizing queries, estimating query costs, and algorithms for operations like selection, sorting, and joins. Selection algorithms include linear scans, binary searches, and using indexes. Sorting can use indexes or external merge sort. Join algorithms include nested loops, merge join, and hash join.
The document summarizes key aspects of query processing from the textbook "Database System Concepts, 6th Ed." by Silberschatz, Korth and Sudarshan. It discusses the basic steps in query processing including parsing, optimization, and evaluation. It also covers measures of query cost, algorithms for common operations like selection, sorting, and joining, and provides examples of query optimization.
The document discusses various techniques for processing database queries, including:
- Basic steps in query processing: parsing, optimization, and evaluation. Optimization involves choosing the most efficient evaluation plan from equivalent options.
- Measures for estimating query cost, primarily focusing on disk I/O like block transfers and seeks.
- Algorithms for different relational algebra operations like selection, sorting, and join. Selection algorithms include file scan, use of indexes, and handling complex conditions. Sorting algorithms include building an index versus external sort-merge. Join algorithms include nested-loop, block nested-loop, and merge-join.
This document discusses query processing in a database system. It describes the basic steps of query processing as parsing and translation, optimization, and evaluation. For optimization, it explains that a relational algebra expression can be evaluated in many ways and the goal is to choose the plan with the lowest estimated cost. It then covers algorithms for common relational operations like selection, sorting, and join and how they are implemented, including using indexes. The overall focus is on analyzing the costs of different algorithms and implementations.
The document discusses various algorithms for query processing operations like selection, sorting, and join. It provides cost estimates for each algorithm based on factors like the number of block transfers and seeks. The most efficient algorithms depend on characteristics of the relations and whether indices are available. Nested loop and block nested loop joins have high costs, while merge join and hash join may have lower costs depending on the situation.
This document discusses query processing and provides an overview of algorithms for evaluating relational algebra operations. It begins with an overview of the basic steps in query processing - parsing and translation, optimization, and evaluation. It then discusses how to measure query costs by focusing on resource consumption, particularly disk access. The document outlines algorithms for common relational operations like selection, sorting, and join. It provides cost estimates for different algorithms like file scan, index scan, and block nested loops join. The overall summary is that the document describes query processing and evaluation strategies for relational algebra operations like selection and join, providing cost estimates to help optimize queries.
This document discusses query processing and algorithms for evaluating relational algebra operations. It begins with an overview of the basic steps in query processing: parsing and translation, optimization, and evaluation. It then discusses how to measure query costs using a cost model based on disk access times. The document outlines several algorithms (A1-A10) for performing selection operations on relations using file scans and indexes. It provides cost estimates for each algorithm based on factors like the number of blocks accessed and index height. The algorithms can handle selections with equality and inequality conditions, as well as complex selections using conjunctions, disjunctions, and negation.
This document summarizes key concepts from Chapter 13 of the textbook "Database System Concepts". It discusses the basic steps in query processing: parsing and translation, optimization, and evaluation. It also describes various algorithms for common relational algebra operations like selection, sorting, and join. The goal of optimization is to choose the most efficient evaluation plan by estimating the cost of each plan using statistical information about operations and relations. Cost is typically estimated based on the number of disk accesses and seeks required.
The document discusses various steps and algorithms for processing database queries. It covers parsing and optimizing queries, estimating query costs, and algorithms for operations like selection, sorting, and joins. Selection algorithms include linear scans, binary searches, and using indexes. Sorting can use indexes or external merge sort. Join algorithms include nested loops, merge join, and hash join.
The document discusses various steps and algorithms involved in query processing in a database system. It covers parsing and translating a query, optimizing the query plan, and evaluating the query. Key operations discussed include selection, sorting, and join. For each operation, multiple algorithms are presented and their costs are analyzed based on factors like disk accesses and memory usage.
Query Processing, Query Optimization and TransactionPrabu U
This document provides an overview of query processing and optimization techniques in database management systems. It discusses measures of query cost, various query operations like selection, sorting, joining, and aggregation. It also covers transaction processing concepts like atomicity, durability, and isolation levels. Specific algorithms covered include nested-loop join, merge join, hash join, and their cost analysis. The document is divided into sections on query processing, transaction processing, and covers various operations involved in query evaluation and optimization.
This document discusses query optimization in database systems. It covers generating equivalent query expressions using equivalence rules, estimating statistics of expression results using information stored in the catalog, and choosing evaluation plans using dynamic programming. The document provides examples of equivalence rules for selections, joins, and other relational algebra operations. It also describes how statistical information like tuple counts, distinct values, and histograms are used to estimate sizes of intermediate results during query optimization.
This document provides an overview of query processing costs, selection operations, join operations, and concurrency control in database systems. It discusses how the costs of queries are estimated based on factors like disk accesses and seeks. It then describes algorithms for common operations like selection, join, and concurrency control protocols. Selection algorithms include file scan, binary search, and using indexes. Join algorithms include nested loops, block nested loops, indexed nested loops, merge join, and hash join. Concurrency control protocols help manage concurrent transaction executions and maintain consistency.
The document discusses algorithms and data structures. It begins by introducing common data structures like arrays, stacks, queues, trees, and hash tables. It then explains that data structures allow for organizing data in a way that can be efficiently processed and accessed. The document concludes by stating that the choice of data structure depends on effectively representing real-world relationships while allowing simple processing of the data.
This document discusses query processing in a database system. It covers parsing queries, optimization to choose the most efficient evaluation plan, and executing the plan. Query optimization aims to minimize costs like I/O by choosing plans with the lowest estimated execution time. The document describes different algorithms for operations like selection, sorting, joins, and expression evaluation, and how equivalence rules and heuristics can transform queries into more efficient forms.
Query Processing and Optimisation - Lecture 10 - Introduction to Databases (1...Beat Signer
This document discusses query processing and optimization in databases. It covers the basic steps of query processing including parsing, optimization, and evaluation. It also describes different algorithms for query operations like selection, join, and sorting that are used to process queries efficiently. The goals of query optimization are to select the most efficient query execution plan based on the given data and minimize the number of disk accesses.
The document discusses different types of indexes that can be used to organize data files on external storage. It compares file organizations like heap files, sorted files, and various indexing techniques including B-tree and hash indexes. It outlines the basic structure of indexes like B-trees, including leaf pages containing data entries and non-leaf pages containing index entries. The document also discusses concepts like clustered vs unclustered indexes, primary vs secondary indexes, and different alternatives for storing data entries in indexes.
The document provides an overview of the layers and processes involved in executing a query in Oracle, from when a client connects and sends a query to when the results are returned. It describes the layers of Oracle's architecture, the parsing, optimization, execution plan generation and execution of the query. Key steps include connecting, parsing, optimizing, generating and executing a query plan, updating and committing any changes, and fetching the results.
The document discusses query execution in database management systems. It begins with an example query on a City, Country database and represents it in relational algebra. It then discusses different query execution strategies like table scan, nested loop join, sort merge join, and hash join. The strategies are compared based on their memory and disk I/O requirements. The document emphasizes that query execution plans can be optimized for parallelism and pipelining to improve performance.
The document discusses the basic steps in query processing, including parsing and translation, optimization, and evaluation. It describes parsing a query into its internal form, translating it to relational algebra, and generating multiple evaluation plans. Optimization selects the most efficient plan based on estimated costs. The selected plan is then used to iteratively execute the query and return the result set.
The document discusses query optimization in databases. It explains that the goal of query optimization is to determine the most efficient execution plan for a query to minimize the time needed. It outlines the typical steps in query optimization, including parsing/translation, applying relational algebra, and optimizing the query plan. It also discusses techniques like generating alternative execution plans using equivalence rules, estimating plan costs based on statistical data, and using heuristics or dynamic programming to choose the optimal plan.
IRJET- Review of Existing Methods in K-Means Clustering AlgorithmIRJET Journal
The document reviews existing methods for the k-means clustering algorithm. It discusses how k-means clustering works and some of its limitations when dealing with large datasets, such as being dependent on the initial choice of centroids. It then proposes using Hadoop to overcome big data challenges and calculate preliminary centroids for k-means clustering in a distributed manner. Finally, it reviews different techniques that have been proposed in other research to improve k-means clustering, such as methods for selecting better initial centroids or determining the optimal number of clusters.
This document provides an overview and agenda for a course on data structures and algorithms. The course objectives are to understand the concepts and costs/benefits of commonly used data structures, how to select appropriate structures based on requirements, and implement structures in code. The agenda covers introduction to structures like linked lists, stacks, queues, trees and graphs as well as sorting algorithms. It also discusses analyzing algorithm efficiency and the types and methodologies for selecting optimal data structures.
The document discusses information retrieval systems and concepts. It describes how information retrieval systems use simpler data models than databases, organizing information as unstructured documents without a schema. It covers techniques for indexing documents, measuring retrieval effectiveness, relevance ranking using terms and hyperlinks, handling synonyms and homonyms, and the role of directories and classification hierarchies. Information retrieval systems are used to locate relevant documents based on keywords, and their applications include web search engines.
This document summarizes a technical report describing a new multi-resolution particle data format called ADAPTER. ADAPTER uses a hierarchical k-d tree structure to store particle data at multiple resolutions, allowing for rapid access to either a large subset of data at low resolution or a small subset at full resolution, without increasing storage requirements. The format is designed to enable efficient exploration and analysis of very large particle datasets in the range of terabytes to petabytes on desktop computers. It aims to address limitations of existing formats in supporting adaptive spatial indexing, multi-resolution access, and set operations for extracting and merging subsets of data at different resolutions.
This paper describes how the optimizer uses statistics and determines plans for executing SQL statement. It explains how the 10053 trace file can be used to understand Oracle's decisions on execution plans.
Hadoop Map-Reduce To Generate Frequent Item Set on Large Datasets Using Impro...BRNSSPublicationHubI
This document presents an improved Apriori algorithm for generating frequent item sets on large datasets using Hadoop MapReduce. The classical Apriori algorithm suffers from repeated database scans, high candidate generation costs, and memory issues. The proposed improved Apriori algorithm aims to address these issues by leveraging Hadoop MapReduce to parallelize the processing and reduce unnecessary database scans. It presents the pseudocode for the classical and improved algorithms. The improved algorithm is evaluated to show it provides better performance than the classical Apriori algorithm in terms of time and number of iterations required.
This document provides an overview and introduction to data structures. It discusses key terminology like data, data items, and fields. It also covers different types of data structures like linear (arrays, linked lists) and non-linear (trees, graphs) structures. Common data structure operations like traversing, searching, inserting and deleting are explained. The document stresses the importance of selecting the appropriate data structure based on the problem and required operations. It also briefly discusses algorithm design, implementation, testing, and analysis of time and space complexity.
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
The document discusses various steps and algorithms involved in query processing in a database system. It covers parsing and translating a query, optimizing the query plan, and evaluating the query. Key operations discussed include selection, sorting, and join. For each operation, multiple algorithms are presented and their costs are analyzed based on factors like disk accesses and memory usage.
Query Processing, Query Optimization and TransactionPrabu U
This document provides an overview of query processing and optimization techniques in database management systems. It discusses measures of query cost, various query operations like selection, sorting, joining, and aggregation. It also covers transaction processing concepts like atomicity, durability, and isolation levels. Specific algorithms covered include nested-loop join, merge join, hash join, and their cost analysis. The document is divided into sections on query processing, transaction processing, and covers various operations involved in query evaluation and optimization.
This document discusses query optimization in database systems. It covers generating equivalent query expressions using equivalence rules, estimating statistics of expression results using information stored in the catalog, and choosing evaluation plans using dynamic programming. The document provides examples of equivalence rules for selections, joins, and other relational algebra operations. It also describes how statistical information like tuple counts, distinct values, and histograms are used to estimate sizes of intermediate results during query optimization.
This document provides an overview of query processing costs, selection operations, join operations, and concurrency control in database systems. It discusses how the costs of queries are estimated based on factors like disk accesses and seeks. It then describes algorithms for common operations like selection, join, and concurrency control protocols. Selection algorithms include file scan, binary search, and using indexes. Join algorithms include nested loops, block nested loops, indexed nested loops, merge join, and hash join. Concurrency control protocols help manage concurrent transaction executions and maintain consistency.
The document discusses algorithms and data structures. It begins by introducing common data structures like arrays, stacks, queues, trees, and hash tables. It then explains that data structures allow for organizing data in a way that can be efficiently processed and accessed. The document concludes by stating that the choice of data structure depends on effectively representing real-world relationships while allowing simple processing of the data.
This document discusses query processing in a database system. It covers parsing queries, optimization to choose the most efficient evaluation plan, and executing the plan. Query optimization aims to minimize costs like I/O by choosing plans with the lowest estimated execution time. The document describes different algorithms for operations like selection, sorting, joins, and expression evaluation, and how equivalence rules and heuristics can transform queries into more efficient forms.
Query Processing and Optimisation - Lecture 10 - Introduction to Databases (1...Beat Signer
This document discusses query processing and optimization in databases. It covers the basic steps of query processing including parsing, optimization, and evaluation. It also describes different algorithms for query operations like selection, join, and sorting that are used to process queries efficiently. The goals of query optimization are to select the most efficient query execution plan based on the given data and minimize the number of disk accesses.
The document discusses different types of indexes that can be used to organize data files on external storage. It compares file organizations like heap files, sorted files, and various indexing techniques including B-tree and hash indexes. It outlines the basic structure of indexes like B-trees, including leaf pages containing data entries and non-leaf pages containing index entries. The document also discusses concepts like clustered vs unclustered indexes, primary vs secondary indexes, and different alternatives for storing data entries in indexes.
The document provides an overview of the layers and processes involved in executing a query in Oracle, from when a client connects and sends a query to when the results are returned. It describes the layers of Oracle's architecture, the parsing, optimization, execution plan generation and execution of the query. Key steps include connecting, parsing, optimizing, generating and executing a query plan, updating and committing any changes, and fetching the results.
The document discusses query execution in database management systems. It begins with an example query on a City, Country database and represents it in relational algebra. It then discusses different query execution strategies like table scan, nested loop join, sort merge join, and hash join. The strategies are compared based on their memory and disk I/O requirements. The document emphasizes that query execution plans can be optimized for parallelism and pipelining to improve performance.
The document discusses the basic steps in query processing, including parsing and translation, optimization, and evaluation. It describes parsing a query into its internal form, translating it to relational algebra, and generating multiple evaluation plans. Optimization selects the most efficient plan based on estimated costs. The selected plan is then used to iteratively execute the query and return the result set.
The document discusses query optimization in databases. It explains that the goal of query optimization is to determine the most efficient execution plan for a query to minimize the time needed. It outlines the typical steps in query optimization, including parsing/translation, applying relational algebra, and optimizing the query plan. It also discusses techniques like generating alternative execution plans using equivalence rules, estimating plan costs based on statistical data, and using heuristics or dynamic programming to choose the optimal plan.
IRJET- Review of Existing Methods in K-Means Clustering AlgorithmIRJET Journal
The document reviews existing methods for the k-means clustering algorithm. It discusses how k-means clustering works and some of its limitations when dealing with large datasets, such as being dependent on the initial choice of centroids. It then proposes using Hadoop to overcome big data challenges and calculate preliminary centroids for k-means clustering in a distributed manner. Finally, it reviews different techniques that have been proposed in other research to improve k-means clustering, such as methods for selecting better initial centroids or determining the optimal number of clusters.
This document provides an overview and agenda for a course on data structures and algorithms. The course objectives are to understand the concepts and costs/benefits of commonly used data structures, how to select appropriate structures based on requirements, and implement structures in code. The agenda covers introduction to structures like linked lists, stacks, queues, trees and graphs as well as sorting algorithms. It also discusses analyzing algorithm efficiency and the types and methodologies for selecting optimal data structures.
The document discusses information retrieval systems and concepts. It describes how information retrieval systems use simpler data models than databases, organizing information as unstructured documents without a schema. It covers techniques for indexing documents, measuring retrieval effectiveness, relevance ranking using terms and hyperlinks, handling synonyms and homonyms, and the role of directories and classification hierarchies. Information retrieval systems are used to locate relevant documents based on keywords, and their applications include web search engines.
This document summarizes a technical report describing a new multi-resolution particle data format called ADAPTER. ADAPTER uses a hierarchical k-d tree structure to store particle data at multiple resolutions, allowing for rapid access to either a large subset of data at low resolution or a small subset at full resolution, without increasing storage requirements. The format is designed to enable efficient exploration and analysis of very large particle datasets in the range of terabytes to petabytes on desktop computers. It aims to address limitations of existing formats in supporting adaptive spatial indexing, multi-resolution access, and set operations for extracting and merging subsets of data at different resolutions.
This paper describes how the optimizer uses statistics and determines plans for executing SQL statement. It explains how the 10053 trace file can be used to understand Oracle's decisions on execution plans.
Hadoop Map-Reduce To Generate Frequent Item Set on Large Datasets Using Impro...BRNSSPublicationHubI
This document presents an improved Apriori algorithm for generating frequent item sets on large datasets using Hadoop MapReduce. The classical Apriori algorithm suffers from repeated database scans, high candidate generation costs, and memory issues. The proposed improved Apriori algorithm aims to address these issues by leveraging Hadoop MapReduce to parallelize the processing and reduce unnecessary database scans. It presents the pseudocode for the classical and improved algorithms. The improved algorithm is evaluated to show it provides better performance than the classical Apriori algorithm in terms of time and number of iterations required.
This document provides an overview and introduction to data structures. It discusses key terminology like data, data items, and fields. It also covers different types of data structures like linear (arrays, linked lists) and non-linear (trees, graphs) structures. Common data structure operations like traversing, searching, inserting and deleting are explained. The document stresses the importance of selecting the appropriate data structure based on the problem and required operations. It also briefly discusses algorithm design, implementation, testing, and analysis of time and space complexity.
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
End-to-end pipeline agility - Berlin Buzzwords 2024Lars Albertsson
We describe how we achieve high change agility in data engineering by eliminating the fear of breaking downstream data pipelines through end-to-end pipeline testing, and by using schema metaprogramming to safely eliminate boilerplate involved in changes that affect whole pipelines.
A quick poll on agility in changing pipelines from end to end indicated a huge span in capabilities. For the question "How long time does it take for all downstream pipelines to be adapted to an upstream change," the median response was 6 months, but some respondents could do it in less than a day. When quantitative data engineering differences between the best and worst are measured, the span is often 100x-1000x, sometimes even more.
A long time ago, we suffered at Spotify from fear of changing pipelines due to not knowing what the impact might be downstream. We made plans for a technical solution to test pipelines end-to-end to mitigate that fear, but the effort failed for cultural reasons. We eventually solved this challenge, but in a different context. In this presentation we will describe how we test full pipelines effectively by manipulating workflow orchestration, which enables us to make changes in pipelines without fear of breaking downstream.
Making schema changes that affect many jobs also involves a lot of toil and boilerplate. Using schema-on-read mitigates some of it, but has drawbacks since it makes it more difficult to detect errors early. We will describe how we have rejected this tradeoff by applying schema metaprogramming, eliminating boilerplate but keeping the protection of static typing, thereby further improving agility to quickly modify data pipelines without fear.
Predictably Improve Your B2B Tech Company's Performance by Leveraging DataKiwi Creative
Harness the power of AI-backed reports, benchmarking and data analysis to predict trends and detect anomalies in your marketing efforts.
Peter Caputa, CEO at Databox, reveals how you can discover the strategies and tools to increase your growth rate (and margins!).
From metrics to track to data habits to pick up, enhance your reporting for powerful insights to improve your B2B tech company's marketing.
- - -
This is the webinar recording from the June 2024 HubSpot User Group (HUG) for B2B Technology USA.
Watch the video recording at https://youtu.be/5vjwGfPN9lw
Sign up for future HUG events at https://events.hubspot.com/b2b-technology-usa/
4th Modern Marketing Reckoner by MMA Global India & Group M: 60+ experts on W...Social Samosa
The Modern Marketing Reckoner (MMR) is a comprehensive resource packed with POVs from 60+ industry leaders on how AI is transforming the 4 key pillars of marketing – product, place, price and promotions.
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
The Building Blocks of QuestDB, a Time Series Databasejavier ramirez
Talk Delivered at Valencia Codes Meetup 2024-06.
Traditionally, databases have treated timestamps just as another data type. However, when performing real-time analytics, timestamps should be first class citizens and we need rich time semantics to get the most out of our data. We also need to deal with ever growing datasets while keeping performant, which is as fun as it sounds.
It is no wonder time-series databases are now more popular than ever before. Join me in this session to learn about the internal architecture and building blocks of QuestDB, an open source time-series database designed for speed. We will also review a history of some of the changes we have gone over the past two years to deal with late and unordered data, non-blocking writes, read-replicas, or faster batch ingestion.
ViewShift: Hassle-free Dynamic Policy Enforcement for Every Data LakeWalaa Eldin Moustafa
Dynamic policy enforcement is becoming an increasingly important topic in today’s world where data privacy and compliance is a top priority for companies, individuals, and regulators alike. In these slides, we discuss how LinkedIn implements a powerful dynamic policy enforcement engine, called ViewShift, and integrates it within its data lake. We show the query engine architecture and how catalog implementations can automatically route table resolutions to compliance-enforcing SQL views. Such views have a set of very interesting properties: (1) They are auto-generated from declarative data annotations. (2) They respect user-level consent and preferences (3) They are context-aware, encoding a different set of transformations for different use cases (4) They are portable; while the SQL logic is only implemented in one SQL dialect, it is accessible in all engines.
#SQL #Views #Privacy #Compliance #DataLake