A hash table is a data structure that uses a hash function to map keys to unique indices in an underlying array. Collisions occur when two keys hash to the same index and must be resolved. Open addressing resolves collisions by probing through alternative array locations until an empty slot is found. Double hashing is an open addressing collision resolution technique that uses a secondary hash of the key as an offset when probing for the next index. Hash tables provide efficient lookup, insertion and deletion of key-value pairs and are used widely in applications like databases, caching and cryptography.
This document discusses indexing and hashing in database management systems. It defines indexing as a technique to efficiently retrieve records from a database based on attributes. Indexing can be single-level or multi-level. Hashing uses hash functions to map keys directly to data locations, avoiding searches through an index structure. Static hashing assigns fixed locations while dynamic hashing allows buckets to expand as needed to avoid collisions.
Data Storage and Management project ReportTushar Dalvi
This paper aims at evaluating the performance of random reads and random writes the information of HBase and Cassandra and compare the results that we got through various ubuntu operation
Extendible hashing allows a hash table to dynamically expand by using an extendible index table. The index table directs lookups to buckets, each holding a fixed number of items. When a bucket fills, it splits into two buckets and the index expands accordingly. This allows the hash table size to increase indefinitely with added items while avoiding rehashing and maintaining fast access through the index.
Extendible hashing allows a hash table to dynamically expand by using an extendible index table. The index table directs lookups to buckets, each holding a fixed number of items. When a bucket fills, it splits into two buckets and the index expands accordingly. This allows the hash table size to increase indefinitely with added items while avoiding rehashing and maintaining fast access through the adjustable index.
Apache Hadoop India Summit 2011 talk "Searching Information Inside Hadoop Pla...Yahoo Developer Network
The document discusses different approaches for searching large datasets in Hadoop, including MapReduce, Lucene/Solr, and building a new search engine called HSearch. Some key challenges with existing approaches included slow response times and the need for manual sharding. HSearch indexes data stored in HDFS and HBase. The document outlines several techniques used in HSearch to improve performance, such as using SSDs selectively, reducing HBase table size, distributing queries across region servers, moving processing near data, byte block caching, and configuration tuning. Benchmarks showed HSearch could return results for common words from a 100 million page index within seconds.
Discover how database sharding https://bityl.co/Q6F3 can transform your application's performance by distributing data across multiple servers in our latest blog. With insights into key sharding techniques, you'll further learn how to implement sharding effectively and avoid common pitfalls. As you move forward, this blog will help you dive into real-life use cases to understand how sharding can optimize data management. Lastly, you'll get the most important factors to consider before sharding your database and learning to navigate the complexities of database management.
Optimization on Key-value Stores in Cloud EnvironmentFei Dong
This document discusses optimizing key-value stores like HBase in cloud environments. It introduces HBase, a distributed, column-oriented database built on HDFS that provides scalable storage and retrieval of large datasets. The document compares rule-based and cost-based optimization strategies, and explores using rule-based optimization to analyze HBase's performance when deployed on Amazon EC2 instances. It describes developing an HBase profiler to measure the costs of using HBase for storage.
A hash table is a data structure that uses a hash function to map keys to unique indices in an underlying array. Collisions occur when two keys hash to the same index and must be resolved. Open addressing resolves collisions by probing through alternative array locations until an empty slot is found. Double hashing is an open addressing collision resolution technique that uses a secondary hash of the key as an offset when probing for the next index. Hash tables provide efficient lookup, insertion and deletion of key-value pairs and are used widely in applications like databases, caching and cryptography.
This document discusses indexing and hashing in database management systems. It defines indexing as a technique to efficiently retrieve records from a database based on attributes. Indexing can be single-level or multi-level. Hashing uses hash functions to map keys directly to data locations, avoiding searches through an index structure. Static hashing assigns fixed locations while dynamic hashing allows buckets to expand as needed to avoid collisions.
Data Storage and Management project ReportTushar Dalvi
This paper aims at evaluating the performance of random reads and random writes the information of HBase and Cassandra and compare the results that we got through various ubuntu operation
Extendible hashing allows a hash table to dynamically expand by using an extendible index table. The index table directs lookups to buckets, each holding a fixed number of items. When a bucket fills, it splits into two buckets and the index expands accordingly. This allows the hash table size to increase indefinitely with added items while avoiding rehashing and maintaining fast access through the index.
Extendible hashing allows a hash table to dynamically expand by using an extendible index table. The index table directs lookups to buckets, each holding a fixed number of items. When a bucket fills, it splits into two buckets and the index expands accordingly. This allows the hash table size to increase indefinitely with added items while avoiding rehashing and maintaining fast access through the adjustable index.
Apache Hadoop India Summit 2011 talk "Searching Information Inside Hadoop Pla...Yahoo Developer Network
The document discusses different approaches for searching large datasets in Hadoop, including MapReduce, Lucene/Solr, and building a new search engine called HSearch. Some key challenges with existing approaches included slow response times and the need for manual sharding. HSearch indexes data stored in HDFS and HBase. The document outlines several techniques used in HSearch to improve performance, such as using SSDs selectively, reducing HBase table size, distributing queries across region servers, moving processing near data, byte block caching, and configuration tuning. Benchmarks showed HSearch could return results for common words from a 100 million page index within seconds.
Discover how database sharding https://bityl.co/Q6F3 can transform your application's performance by distributing data across multiple servers in our latest blog. With insights into key sharding techniques, you'll further learn how to implement sharding effectively and avoid common pitfalls. As you move forward, this blog will help you dive into real-life use cases to understand how sharding can optimize data management. Lastly, you'll get the most important factors to consider before sharding your database and learning to navigate the complexities of database management.
Optimization on Key-value Stores in Cloud EnvironmentFei Dong
This document discusses optimizing key-value stores like HBase in cloud environments. It introduces HBase, a distributed, column-oriented database built on HDFS that provides scalable storage and retrieval of large datasets. The document compares rule-based and cost-based optimization strategies, and explores using rule-based optimization to analyze HBase's performance when deployed on Amazon EC2 instances. It describes developing an HBase profiler to measure the costs of using HBase for storage.
This document provides a quick guide to refresh skills on HBase architecture and concepts. It discusses HBase's limitations in satisfying the CAP theorem, its architecture components including the HMaster, Region Servers and Zookeeper. It also covers best practices for row key design, and differences between minor and major compactions. The HColumnDescriptor class and HBase catalog tables -.META. and -ROOT- are also summarized.
I have examined the performance of two databases - HBase and Cassandra in terms of their scalability, security, performance and compared the results thus obtained through different operations on the Ubuntu interface.
The project is focussed on Comparison Between HBASE and CASSANDRA using YCSB. It is a data storage and management project performed at National College Of Ireland
HBase is a column-oriented NoSQL database that provides random real-time read/write access to big data stored in Hadoop's HDFS. It is modeled after Google's Bigtable and sits on top of HDFS to allow fast access to large datasets. HBase architecture includes HMaster, HRegionServers, ZooKeeper, and HDFS. HMaster manages metadata and load balancing while HRegionServers serve read/write requests directly from clients. ZooKeeper coordinates the cluster and HDFS provides storage. Data is stored in tables divided into regions hosted by HRegionServers.
A hash table maps keys to values by applying a hash function to the keys, which then indexes into an array of buckets or slots. There are two main types: open-addressing hash tables, where keys are stored directly in the array slots, and separate chaining, where each slot contains a linked list of key-value entries. Some example applications include databases, symbol tables for compilers, and network processing algorithms.
CCS334 BIG DATA ANALYTICS UNIT 5 PPT ELECTIVE PAPERKrishnaVeni451953
HBase is an open source, column-oriented database built on top of Hadoop that allows for the storage and retrieval of large amounts of sparse data. It provides random real-time read/write access to this data stored in Hadoop and scales horizontally. HBase features include automatic failover, integration with MapReduce, and storing data as multidimensional sorted maps indexed by row, column, and timestamp. The architecture consists of a master server (HMaster), region servers (HRegionServer), regions (HRegions), and Zookeeper for coordination.
The document compares using SAS hash objects versus SQL joins to combine data from multiple tables. Hash objects store key-value pairs in memory for fast lookups, providing a potential alternative to joins. While hash objects can improve performance, especially for larger datasets, they require more code and memory than joins. The document evaluates performance differences between hash objects and joins for various scenarios and sizes of data. It also discusses additional capabilities and considerations for using hash objects.
HBase is a distributed column-oriented database built on top of Hadoop that provides quick random access to large amounts of structured data. It leverages the fault tolerance of HDFS and allows for real-time read/write access to data stored in HDFS. HBase sits above HDFS and provides APIs for reading and writing data randomly. It is a scalable, schema-less database modeled after Google's Bigtable.
Devise and implement a test strategy in order to perform a comparative analysis of the capabilities of two database management systems (Cassandra and HBase) in terms of performance.
Approach: Installation and implementation of instances of the two data storage and management systems. The Yahoo Cloud Serving Benchmark is used to compare the performances of HBase and Cassandra. Average latency and throughput were considered for analyzing the comparison of the two databases. The results obtained from YCSB are then analyzed and visualized with the help of Tableau.
Findings: HBase performs insertion, reading, and updating of records faster than Cassandra but only when the operations count is less. At heavier loads, Cassandra performs better than Hbase.
Tools: Hbase, Cassandra, Hadoop, Tableau, YCSB
Combining Efficiency, Fidelity, and Flexibility in Resource Information Servicesnexgentechnology
bulk ieee projects in pondicherry,ieee projects in pondicherry,final year ieee projects in pondicherry
Nexgen Technology Address:
Nexgen Technology
No :66,4th cross,Venkata nagar,
Near SBI ATM,
Puducherry.
Email Id: praveen@nexgenproject.com.
www.nexgenproject.com
Mobile: 9751442511,9791938249
Telephone: 0413-2211159.
NEXGEN TECHNOLOGY as an efficient Software Training Center located at Pondicherry with IT Training on IEEE Projects in Android,IEEE IT B.Tech Student Projects, Android Projects Training with Placements Pondicherry, IEEE projects in pondicherry, final IEEE Projects in Pondicherry , MCA, BTech, BCA Projects in Pondicherry, Bulk IEEE PROJECTS IN Pondicherry.So far we have reached almost all engineering colleges located in Pondicherry and around 90km
COMBINING EFFICIENCY, FIDELITY, AND FLEXIBILITY IN RESOURCE INFORMATION SERV...Nexgen Technology
This document discusses a resource information service that aims to provide high efficiency, fidelity, and flexibility for resource discovery in large-scale distributed systems like cloud computing and grids. It proposes using Locality-Sensitive Hashing (LSH) techniques to map resource descriptions to IDs in a way that preserves similarity, allowing efficient discovery of similar resources. The system is built on a Distributed Hash Table (DHT) for scalable storage and querying of resource information. Simulation and experimental results show the proposed LSH-based service outperforms other approaches in terms of efficiency, fidelity, and flexibility.
Combining efficiency, fidelity, and flexibility innexgentech15
Nexgen Technology Address:
Nexgen Technology
No :66,4th cross,Venkata nagar,
Near SBI ATM,
Puducherry.
Email Id: praveen@nexgenproject.com.
www.nexgenproject.com
Mobile: 9751442511,9791938249
Telephone: 0413-2211159.
NEXGEN TECHNOLOGY as an efficient Software Training Center located at Pondicherry with IT Training on IEEE Projects in Android,IEEE IT B.Tech Student Projects, Android Projects Training with Placements Pondicherry, IEEE projects in pondicherry, final IEEE Projects in Pondicherry , MCA, BTech, BCA Projects in Pondicherry, Bulk IEEE PROJECTS IN Pondicherry.So far we have reached almost all engineering colleges located in Pondicherry and around 90km
HBase is an open-source, distributed, versioned, key-value database modeled after Google's Bigtable. It is designed to store large volumes of sparse data across commodity hardware. HBase uses Hadoop for storage and provides real-time read and write capabilities. It scales horizontally and is highly fault tolerant through its master-slave architecture and use of Zookeeper for coordination. Data in HBase is stored in tables and indexed by row keys for fast lookup, with columns grouped into families and versions stored by timestamps.
The document provides information on various components of the Hadoop ecosystem including Pig, Zookeeper, HBase, Spark, and Hive. It discusses how HBase offers random access to data stored in HDFS, allowing for faster lookups than HDFS alone. It describes the architecture of HBase including its use of Zookeeper, storage of data in regions on region servers, and secondary indexing capabilities. Finally, it summarizes Hive and how it allows SQL-like queries on large datasets stored in HDFS or other distributed storage systems using MapReduce or Spark jobs.
Comparative study of no sql document, column store databases and evaluation o...ijdms
In the last decade, rapid growth in mobile applications, web technologies, social media generating
unstructured data has led to the advent of various nosql data stores. Demands of web scale are in
increasing trend everyday and nosql databases are evolving to meet up with stern big data requirements.
The purpose of this paper is to explore nosql technologies and present a comparative study of document
and column store nosql databases such as cassandra, MongoDB and Hbase in various attributes of
relational and distributed database system principles. Detailed study and analysis of architecture and
internal working cassandra, Mongo DB and HBase is done theoretically and core concepts are depicted.
This paper also presents evaluation of cassandra for an industry specific use case and results are
published.
Cassandra is a highly scalable, distributed database designed to handle large amounts of structured data across multiple nodes without single points of failure. It uses a column-oriented data model and peer-to-peer architecture to distribute data among nodes in a cluster, providing high availability and performance. Some key limitations of Cassandra queries include a lack of support for aggregation, joins, grouping, filtering without indexes, and most comparison operators beyond clustering columns.
This document provides an overview of NoSQL databases and the HBase framework. It discusses key aspects of NoSQL including advantages like high scalability and schema flexibility. It then describes the different categories of NoSQL databases including key-value, column-oriented, graph and document oriented. The document proceeds to explain aggregate data models and how key-value and document databases are aggregate-oriented. It provides details on HBase, describing it as a column-oriented database, and its architecture, data model involving tables, rows, column families and cells.
Big Data Frameworks: Introduction to NoSQL – Aggregate Data Models – Hbase: Data Model and Implementations – Hbase Clients – Examples – .Cassandra: Data Model – Examples – Cassandra Clients – Hadoop Integration. Pig – Grunt – Pig Data Model – Pig Latin – developing and testing Pig Latin scripts. Hive – Data Types and File Formats – HiveQL Data Definition – HiveQL Data Manipulation – HiveQL Queries
This document provides an overview of HBase, including its architecture and how it compares to relational databases and HDFS. Some key points:
- HBase is a non-relational, distributed, column-oriented database that runs on top of Hadoop. It uses a master-slave architecture with an HMaster and multiple HRegionServers.
- Unlike relational databases, HBase is schema-less, column-oriented, and designed for denormalized data in wide, sparsely populated tables.
- Compared to HDFS, HBase provides low-latency random reads/writes instead of batch processing. Data is accessed via APIs instead of MapReduce.
- HBase uses LSM
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.
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
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Similar to Introduction-to-Extendable -Hashing.pptx
This document provides a quick guide to refresh skills on HBase architecture and concepts. It discusses HBase's limitations in satisfying the CAP theorem, its architecture components including the HMaster, Region Servers and Zookeeper. It also covers best practices for row key design, and differences between minor and major compactions. The HColumnDescriptor class and HBase catalog tables -.META. and -ROOT- are also summarized.
I have examined the performance of two databases - HBase and Cassandra in terms of their scalability, security, performance and compared the results thus obtained through different operations on the Ubuntu interface.
The project is focussed on Comparison Between HBASE and CASSANDRA using YCSB. It is a data storage and management project performed at National College Of Ireland
HBase is a column-oriented NoSQL database that provides random real-time read/write access to big data stored in Hadoop's HDFS. It is modeled after Google's Bigtable and sits on top of HDFS to allow fast access to large datasets. HBase architecture includes HMaster, HRegionServers, ZooKeeper, and HDFS. HMaster manages metadata and load balancing while HRegionServers serve read/write requests directly from clients. ZooKeeper coordinates the cluster and HDFS provides storage. Data is stored in tables divided into regions hosted by HRegionServers.
A hash table maps keys to values by applying a hash function to the keys, which then indexes into an array of buckets or slots. There are two main types: open-addressing hash tables, where keys are stored directly in the array slots, and separate chaining, where each slot contains a linked list of key-value entries. Some example applications include databases, symbol tables for compilers, and network processing algorithms.
CCS334 BIG DATA ANALYTICS UNIT 5 PPT ELECTIVE PAPERKrishnaVeni451953
HBase is an open source, column-oriented database built on top of Hadoop that allows for the storage and retrieval of large amounts of sparse data. It provides random real-time read/write access to this data stored in Hadoop and scales horizontally. HBase features include automatic failover, integration with MapReduce, and storing data as multidimensional sorted maps indexed by row, column, and timestamp. The architecture consists of a master server (HMaster), region servers (HRegionServer), regions (HRegions), and Zookeeper for coordination.
The document compares using SAS hash objects versus SQL joins to combine data from multiple tables. Hash objects store key-value pairs in memory for fast lookups, providing a potential alternative to joins. While hash objects can improve performance, especially for larger datasets, they require more code and memory than joins. The document evaluates performance differences between hash objects and joins for various scenarios and sizes of data. It also discusses additional capabilities and considerations for using hash objects.
HBase is a distributed column-oriented database built on top of Hadoop that provides quick random access to large amounts of structured data. It leverages the fault tolerance of HDFS and allows for real-time read/write access to data stored in HDFS. HBase sits above HDFS and provides APIs for reading and writing data randomly. It is a scalable, schema-less database modeled after Google's Bigtable.
Devise and implement a test strategy in order to perform a comparative analysis of the capabilities of two database management systems (Cassandra and HBase) in terms of performance.
Approach: Installation and implementation of instances of the two data storage and management systems. The Yahoo Cloud Serving Benchmark is used to compare the performances of HBase and Cassandra. Average latency and throughput were considered for analyzing the comparison of the two databases. The results obtained from YCSB are then analyzed and visualized with the help of Tableau.
Findings: HBase performs insertion, reading, and updating of records faster than Cassandra but only when the operations count is less. At heavier loads, Cassandra performs better than Hbase.
Tools: Hbase, Cassandra, Hadoop, Tableau, YCSB
Combining Efficiency, Fidelity, and Flexibility in Resource Information Servicesnexgentechnology
bulk ieee projects in pondicherry,ieee projects in pondicherry,final year ieee projects in pondicherry
Nexgen Technology Address:
Nexgen Technology
No :66,4th cross,Venkata nagar,
Near SBI ATM,
Puducherry.
Email Id: praveen@nexgenproject.com.
www.nexgenproject.com
Mobile: 9751442511,9791938249
Telephone: 0413-2211159.
NEXGEN TECHNOLOGY as an efficient Software Training Center located at Pondicherry with IT Training on IEEE Projects in Android,IEEE IT B.Tech Student Projects, Android Projects Training with Placements Pondicherry, IEEE projects in pondicherry, final IEEE Projects in Pondicherry , MCA, BTech, BCA Projects in Pondicherry, Bulk IEEE PROJECTS IN Pondicherry.So far we have reached almost all engineering colleges located in Pondicherry and around 90km
COMBINING EFFICIENCY, FIDELITY, AND FLEXIBILITY IN RESOURCE INFORMATION SERV...Nexgen Technology
This document discusses a resource information service that aims to provide high efficiency, fidelity, and flexibility for resource discovery in large-scale distributed systems like cloud computing and grids. It proposes using Locality-Sensitive Hashing (LSH) techniques to map resource descriptions to IDs in a way that preserves similarity, allowing efficient discovery of similar resources. The system is built on a Distributed Hash Table (DHT) for scalable storage and querying of resource information. Simulation and experimental results show the proposed LSH-based service outperforms other approaches in terms of efficiency, fidelity, and flexibility.
Combining efficiency, fidelity, and flexibility innexgentech15
Nexgen Technology Address:
Nexgen Technology
No :66,4th cross,Venkata nagar,
Near SBI ATM,
Puducherry.
Email Id: praveen@nexgenproject.com.
www.nexgenproject.com
Mobile: 9751442511,9791938249
Telephone: 0413-2211159.
NEXGEN TECHNOLOGY as an efficient Software Training Center located at Pondicherry with IT Training on IEEE Projects in Android,IEEE IT B.Tech Student Projects, Android Projects Training with Placements Pondicherry, IEEE projects in pondicherry, final IEEE Projects in Pondicherry , MCA, BTech, BCA Projects in Pondicherry, Bulk IEEE PROJECTS IN Pondicherry.So far we have reached almost all engineering colleges located in Pondicherry and around 90km
HBase is an open-source, distributed, versioned, key-value database modeled after Google's Bigtable. It is designed to store large volumes of sparse data across commodity hardware. HBase uses Hadoop for storage and provides real-time read and write capabilities. It scales horizontally and is highly fault tolerant through its master-slave architecture and use of Zookeeper for coordination. Data in HBase is stored in tables and indexed by row keys for fast lookup, with columns grouped into families and versions stored by timestamps.
The document provides information on various components of the Hadoop ecosystem including Pig, Zookeeper, HBase, Spark, and Hive. It discusses how HBase offers random access to data stored in HDFS, allowing for faster lookups than HDFS alone. It describes the architecture of HBase including its use of Zookeeper, storage of data in regions on region servers, and secondary indexing capabilities. Finally, it summarizes Hive and how it allows SQL-like queries on large datasets stored in HDFS or other distributed storage systems using MapReduce or Spark jobs.
Comparative study of no sql document, column store databases and evaluation o...ijdms
In the last decade, rapid growth in mobile applications, web technologies, social media generating
unstructured data has led to the advent of various nosql data stores. Demands of web scale are in
increasing trend everyday and nosql databases are evolving to meet up with stern big data requirements.
The purpose of this paper is to explore nosql technologies and present a comparative study of document
and column store nosql databases such as cassandra, MongoDB and Hbase in various attributes of
relational and distributed database system principles. Detailed study and analysis of architecture and
internal working cassandra, Mongo DB and HBase is done theoretically and core concepts are depicted.
This paper also presents evaluation of cassandra for an industry specific use case and results are
published.
Cassandra is a highly scalable, distributed database designed to handle large amounts of structured data across multiple nodes without single points of failure. It uses a column-oriented data model and peer-to-peer architecture to distribute data among nodes in a cluster, providing high availability and performance. Some key limitations of Cassandra queries include a lack of support for aggregation, joins, grouping, filtering without indexes, and most comparison operators beyond clustering columns.
This document provides an overview of NoSQL databases and the HBase framework. It discusses key aspects of NoSQL including advantages like high scalability and schema flexibility. It then describes the different categories of NoSQL databases including key-value, column-oriented, graph and document oriented. The document proceeds to explain aggregate data models and how key-value and document databases are aggregate-oriented. It provides details on HBase, describing it as a column-oriented database, and its architecture, data model involving tables, rows, column families and cells.
Big Data Frameworks: Introduction to NoSQL – Aggregate Data Models – Hbase: Data Model and Implementations – Hbase Clients – Examples – .Cassandra: Data Model – Examples – Cassandra Clients – Hadoop Integration. Pig – Grunt – Pig Data Model – Pig Latin – developing and testing Pig Latin scripts. Hive – Data Types and File Formats – HiveQL Data Definition – HiveQL Data Manipulation – HiveQL Queries
This document provides an overview of HBase, including its architecture and how it compares to relational databases and HDFS. Some key points:
- HBase is a non-relational, distributed, column-oriented database that runs on top of Hadoop. It uses a master-slave architecture with an HMaster and multiple HRegionServers.
- Unlike relational databases, HBase is schema-less, column-oriented, and designed for denormalized data in wide, sparsely populated tables.
- Compared to HDFS, HBase provides low-latency random reads/writes instead of batch processing. Data is accessed via APIs instead of MapReduce.
- HBase uses LSM
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Before long, the world will wake up. But right now, there are perhaps a few hundred people, most of them in San Francisco and the AI labs, that have situational awareness. Through whatever peculiar forces of fate, I have found myself amongst them. A few years ago, these people were derided as crazy—but they trusted the trendlines, which allowed them to correctly predict the AI advances of the past few years. Whether these people are also right about the next few years remains to be seen. But these are very smart people—the smartest people I have ever met—and they are the ones building this technology. Perhaps they will be an odd footnote in history, or perhaps they will go down in history like Szilard and Oppenheimer and Teller. If they are seeing the future even close to correctly, we are in for a wild ride.
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Postgres is the most advanced open-source database in the world and it's supported by a community, not a single company. So how does this work? How does code actually get into Postgres? I recently had a patch submitted and committed and I want to share what I learned in that process. I’ll give you an overview of Postgres versions and how the underlying project codebase functions. I’ll also show you the process for submitting a patch and getting that tested and committed.
Codeless Generative AI Pipelines
(GenAI with Milvus)
https://ml.dssconf.pl/user.html#!/lecture/DSSML24-041a/rate
Discover the potential of real-time streaming in the context of GenAI as we delve into the intricacies of Apache NiFi and its capabilities. Learn how this tool can significantly simplify the data engineering workflow for GenAI applications, allowing you to focus on the creative aspects rather than the technical complexities. I will guide you through practical examples and use cases, showing the impact of automation on prompt building. From data ingestion to transformation and delivery, witness how Apache NiFi streamlines the entire pipeline, ensuring a smooth and hassle-free experience.
Timothy Spann
https://www.youtube.com/@FLaNK-Stack
https://medium.com/@tspann
https://www.datainmotion.dev/
milvus, unstructured data, vector database, zilliz, cloud, vectors, python, deep learning, generative ai, genai, nifi, kafka, flink, streaming, iot, edge
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
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/
2. Hashing Fundamentals
1 Hash Functions
Hashing maps data to a
unique fixed-size value
or "hash code" using a
hash function.
2 Hash Tables
Data is stored in hash
tables and accessed by
its hash code, providing
constant-time lookup.
3 Collisions
Collisions occur when
different inputs map to
the same hash code,
leading to performance
issues.
3. Limitations of Traditional Hashing
Fixed Size
Traditional hash tables have a fixed
size, limiting their ability to adapt to
changing data volumes.
Collisions
Collisions can lead to performance
degradation as the hash table fills up.
Resizing
Resizing a hash table is an expensive operation, requiring rehashing of all existing data.
4. Extendable Hashing Concept
1 Dynamic Directories
Extendable hashing uses a dynamic directory structure to handle growing
data volumes.
2 Adaptive Bucket Size
Bucket sizes are adjusted based on data distribution, avoiding performance
issues.
3 Efficient Scaling
The hash table can be expanded or contracted as needed, without the need
for rehashing.
5. Extendable Hashing Structure
Global Depth
Indicates the maximum
number of bits used to index
the directory.
Local Depth
Specifies the number of
significant bits used to index
each bucket.
Buckets
Data is stored in variable-
sized buckets, with the
number of buckets
dynamically adjusted.
6. Extendable Hashing Operations
Insertion
New data is hashed and stored in the appropriate bucket.
Splitting
Buckets are split when they reach capacity, updating the directory structure.
Searching
Data is efficiently located using the dynamic directory and bucket structure.
7. Advantages of Extendable Hashing
Scalability
Extendable hashing can
dynamically adapt to changes
in data volume.
Performance
Efficient data storage and
retrieval, with minimal collisions
and rehashing.
Flexibility
The hashing structure can be
easily adjusted to suit the data
distribution.
8. Conclusion and Applications
Extendable hashing is a powerful technique that addresses the limitations of traditional hashing. It has
wide-ranging applications in database management, caching systems, and other data-intensive domains
that require efficient and scalable data storage and retrieval.