HBase can be an intimidating beast for someone considering its adoption. For what kinds of workloads is it well suited? How does it integrate into the rest of my application infrastructure? What are the data semantics upon which applications can be built? What are the deployment and operational concerns? In this talk, I'll address each of these questions in turn. As supporting evidence, both high-level application architecture and internal details will be discussed. This is an interactive talk: bring your questions and your use-cases!
Data Lakehouse, Data Mesh, and Data Fabric (r2)James Serra
So many buzzwords of late: Data Lakehouse, Data Mesh, and Data Fabric. What do all these terms mean and how do they compare to a modern data warehouse? In this session I’ll cover all of them in detail and compare the pros and cons of each. They all may sound great in theory, but I'll dig into the concerns you need to be aware of before taking the plunge. I’ll also include use cases so you can see what approach will work best for your big data needs. And I'll discuss Microsoft version of the data mesh.
Building a Feature Store around Dataframes and Apache SparkDatabricks
A Feature Store enables machine learning (ML) features to be registered, discovered, and used as part of ML pipelines, thus making it easier to transform and validate the training data that is fed into machine learning systems. Feature stores can also enable consistent engineering of features between training and inference, but to do so, they need a common data processing platform.
This document discusses using caching to improve performance for web applications. It provides three key points:
1. Cache stores data to serve future requests faster by avoiding accessing the database. It is commonly used for things like login information, page content, and API responses.
2. There are different cache architectures like memcached and Redis that support storing data in-memory for fast retrieval. Factors like data size, update frequency, and consistency requirements determine the appropriate caching strategy.
3. Real-world examples show how companies like Facebook, Twitter, and Wonga use caching extensively to handle high volumes of traffic and database requests. Caching is critical to scaling applications in a cost-effective way.
This document discusses Apache Ranger, an open source framework for centralized security administration across Hadoop components like HDFS, Hive, HBase, Knox, Storm, YARN, Kafka, and Solr. It provides authorization and auditing capabilities. Ranger allows defining flexible access policies in a centralized manner and enforcing them. It has an extensible architecture to easily add new components and customize authorization decisions using conditions and context enrichers. The document outlines Ranger's key capabilities and provides examples of its policy definitions and extensibility features.
The document provides an overview of the activity feeds architecture. It discusses the fundamental entities of connections and activities. Connections express relationships between entities and are implemented as a directed graph. Activities form a log of actions by entities. To populate feeds, activities are copied and distributed to relevant entities and then aggregated. The aggregation process involves selecting connections, classifying activities, scoring them, pruning duplicates, and sorting the results into a merged newsfeed.
Hive LLAP: A High Performance, Cost-effective Alternative to Traditional MPP ...DataWorks Summit
The document discusses Hive LLAP (Live Long and Process) as a high performance and cost-effective alternative to traditional Massively Parallel Processing (MPP) databases for querying large datasets on Hadoop. It describes Walmart's implementation of Hive LLAP on their data lake to improve query performance for business users. A proof-of-concept found Hive LLAP queries were up to 50% faster when using 15 nodes instead of 10, and it performed comparably or better than two MPP databases with similar or larger infrastructures. Walmart plans to further evaluate Hive LLAP on newer Hadoop distributions and technologies to improve availability and workload management.
Data Lakehouse, Data Mesh, and Data Fabric (r1)James Serra
So many buzzwords of late: Data Lakehouse, Data Mesh, and Data Fabric. What do all these terms mean and how do they compare to a data warehouse? In this session I’ll cover all of them in detail and compare the pros and cons of each. I’ll include use cases so you can see what approach will work best for your big data needs.
Next generation intelligent data lakes, powered by GraphQL & AWS AppSync - MA...Amazon Web Services
GraphQL is a query language for APIs and a runtime to fulfill these queries, allowing applications to easily connect and access data stored on any type of database technology or API. AWS AppSync provides a powerful and flexible serverless GraphQL API that securely accesses, manipulates, and combines data from multiple sources at any scale, enabling you to build any kind of application on a range of data sources independently of the underlying database technology. In this session, we discuss different use cases where AWS AppSync and GraphQL power next-generation applications. Special guest, Candid Partners, shares how it uses AWS AppSync in its Data Fabric solution to simplify large-scale data management using a GraphQL API to interact with data lakes.
This document provides an overview of Ceph, a distributed storage system. It defines software defined storage and discusses different storage options like file, object, and block storage. It then introduces Ceph, highlighting that it provides a unified storage platform for block, object, and file storage using commodity hardware in a massively scalable, self-managing, and fault-tolerant manner. The document outlines Ceph's architecture including its components like OSDs, monitors, RADOS, RBD, RGW, and CephFS. It also discusses various access methods and use cases for Ceph like with OpenStack.
This document discusses metadata and the importance of metadata management. It introduces Apache Atlas as an open source platform for metadata management and governance. Key points include:
- Metadata is important for data reuse, analytics, and governance. It provides context and meaning about data.
- Current reality is that metadata is often not well supported or integrated across tools. Apache Atlas aims to provide an open, unified approach.
- Apache Atlas has graduated to a top-level Apache project. It provides a type-agnostic metadata store and interfaces that can be accessed by various tools.
- The vision is for an open ecosystem where metadata is shared and federated across repositories from different vendors and tools.
The document provides an overview of Amazon Aurora, a managed relational database service from AWS. Some key points:
- Aurora is optimized for high performance and availability and is compatible with MySQL and PostgreSQL. It uses a distributed, fault-tolerant storage system and automatically handles administrative tasks.
- Aurora leverages other AWS services like Lambda, S3, IAM and CloudWatch. Its scale-out architecture provides high throughput and its asynchronous replication enables quick failover.
- Performance monitoring tools like Performance Insights help users analyze database load and identify bottlenecks. Recent innovations improve availability further with features like zero downtime patching and database cloning.
Airbnb primarily leverages Spark to power mission critical data applications. In this talk, we would like to share our major production use cases including both Streaming applications and Batch processing applications. In addition, we would like share our optimizations on how to improve the throughput of Spark Kafka connector by 10X. Furthermore, we plan to share our journey and lessons learned during the process of upgrading Spark 1+ applications to Spark 2+. The key takeaways includes best practices learned from building and scaling production Spark applications as well as tips and benefits of migrating to Spark 2.x. We hope to share our experiences of making Spark successful at Airbnb with a broader audience of Spark users.
Best Practices for a Complete Postgres Enterprise Architecture SetupEDB
The document discusses best practices for setting up a complete PostgreSQL enterprise architecture, including components for OLTP infrastructure, high availability, disaster recovery, data integration, monitoring and management, and security. It also provides an overview of EnterpriseDB's integrated PostgreSQL product portfolio and tools that can be used to implement an enterprise-grade PostgreSQL setup. The presentation recommends using a reference architecture approach to accelerate implementation, lower costs, enhance performance, and reduce risk.
HBaseCon 2012 | HBase Schema Design - Ian Varley, SalesforceCloudera, Inc.
Most developers are familiar with the topic of “database design”. In the relational world, normalization is the name of the game. How do things change when you’re working with a scalable, distributed, non-SQL database like HBase? This talk will cover the basics of HBase schema design at a high level and give several common patterns and examples of real-world schemas to solve interesting problems. The storage and data access architecture of HBase (row keys, column families, etc.) will be explained, along with the pros and cons of different schema decisions.
AWS EMR을 사용하면서 비용을 최적화하기 위해 필요한 다양한 관점의 방안을 검토하여 정리한 자료.
비용 최적화 대상은 zeppelin/jupyter notebook과 apache spark를 활용하는 서비스를 대상으로 하였으며, 해당 작업이 aws emr에서 어떻게 동작하는지 내부 구조을 파악하여 확인함.
- AWS EMR이란?
- AWS EMR의 과금 방식은?
- 어떻게 비용을 최적화 할 것인가?
- 최적의 EMR 클러스터 구성 방안
- 가성비 높은 Instance 선정 방안
- Apache Spark 성능 개선 방안
가장 중요한 것은 실행할 job의 자원사용량/성능을 모니터링하고, 이에 맞게 자원을 최적화하는 것이 필요함.
Serverless Kafka and Spark in a Multi-Cloud Lakehouse ArchitectureKai Wähner
Apache Kafka in conjunction with Apache Spark became the de facto standard for processing and analyzing data. Both frameworks are open, flexible, and scalable.
Unfortunately, the latter makes operations a challenge for many teams. Ideally, teams can use serverless SaaS offerings to focus on business logic. However, hybrid and multi-cloud scenarios require a cloud-native platform that provides automated and elastic tooling to reduce the operations burden.
This session explores different architectures to build serverless Apache Kafka and Apache Spark multi-cloud architectures across regions and continents.
We start from the analytics perspective of a data lake and explore its relation to a fully integrated data streaming layer with Kafka to build a modern data Data Lakehouse.
Real-world use cases show the joint value and explore the benefit of the "delta lake" integration.
This document provides a summary of improvements made to Hive's performance through the use of Apache Tez and other optimizations. Some key points include:
- Hive was improved to use Apache Tez as its execution engine instead of MapReduce, reducing latency for interactive queries and improving throughput for batch queries.
- Statistics collection was optimized to gather column-level statistics from ORC file footers, speeding up statistics gathering.
- The cost-based optimizer Optiq was added to Hive, allowing it to choose better execution plans.
- Vectorized query processing, broadcast joins, dynamic partitioning, and other optimizations improved individual query performance by over 100x in some cases.
Andy Jassy Illuminates Amazon Web ServicesMichael Skok
Andy Jassy, senior vice president of Amazon Web Services, provides an overview of AWS at the May 8, 2013 Startup Secrets session at Harvard innovation lab.
Intro to HBase Internals & Schema Design (for HBase users)alexbaranau
This document provides an introduction to HBase internals and schema design for HBase users. It discusses the logical and physical views of HBase, including how tables are split into regions and stored across region servers. It covers best practices for schema design, such as using row keys efficiently and avoiding redundancy. The document also briefly discusses advanced topics like coprocessors and compression. The overall goal is to help HBase users optimize performance and scalability based on its internal architecture.
This document introduces HBase, an open-source, non-relational, distributed database modeled after Google's BigTable. It describes what HBase is, how it can be used, and when it is applicable. Key points include that HBase stores data in columns and rows accessed by row keys, integrates with Hadoop for MapReduce jobs, and is well-suited for large datasets, fast random access, and write-heavy applications. Common use cases involve log analytics, real-time analytics, and messages-centered systems.
Apache HBase is the Hadoop opensource, distributed, versioned storage manager well suited for random, realtime read/write access. This talk will give an overview on how HBase achieve random I/O, focusing on the storage layer internals. Starting from how the client interact with Region Servers and Master to go into WAL, MemStore, Compactions and on-disk format details. Looking at how the storage is used by features like snapshots, and how it can be improved to gain flexibility, performance and space efficiency.
HBase and HDFS: Understanding FileSystem Usage in HBaseenissoz
This document discusses file system usage in HBase. It provides an overview of the three main file types in HBase: write-ahead logs (WALs), data files, and reference files. It describes durability semantics, IO fencing techniques for region server recovery, and how HBase leverages data locality through short circuit reads, checksums, and block placement hints. The document is intended help understand HBase's interactions with HDFS for tuning IO performance.
This document discusses tuning HBase and HDFS for performance and correctness. Some key recommendations include:
- Enable HDFS sync on close and sync behind writes for correctness on power failures.
- Tune HBase compaction settings like blockingStoreFiles and compactionThreshold based on whether the workload is read-heavy or write-heavy.
- Size RegionServer machines based on disk size, heap size, and number of cores to optimize for the workload.
- Set client and server RPC chunk sizes like hbase.client.write.buffer to 2MB to maximize network throughput.
- Configure various garbage collection settings in HBase like -Xmn512m and -XX:+UseCMSInit
This document summarizes a presentation about optimizing HBase performance through caching. It discusses how baseline tests showed low cache hit rates and CPU/memory utilization. Reducing the table block size improved cache hits but increased overhead. Adding an off-heap bucket cache to store table data minimized JVM garbage collection latency spikes and improved memory utilization by caching frequently accessed data outside the Java heap. Configuration parameters for the bucket cache are also outlined.
This document provides an overview of Apache Hadoop and HBase. It begins with an introduction to why big data is important and how Hadoop addresses storing and processing large amounts of data across commodity servers. The core components of Hadoop, HDFS for storage and MapReduce for distributed processing, are described. An example MapReduce job is outlined. The document then introduces the Hadoop ecosystem, including Apache HBase for random read/write access to data stored in Hadoop. Real-world use cases of Hadoop at companies like Yahoo, Facebook and Twitter are briefly mentioned before addressing questions.
Apache HBase - Introduction & Use CasesData Con LA
HBase is an open source, distributed, column-oriented database modeled after Google's BigTable. It sits atop Hadoop, using HDFS for storage. HBase scales horizontally and supports fast random reads and writes. It is well-suited for large tables and high throughput access. Facebook uses HBase extensively for messaging and other applications due to its high write throughput and low latency reads. Other users include Flurry and Yahoo.
Hadoop World 2011: Advanced HBase Schema DesignCloudera, Inc.
While running a simple key/value based solution on HBase usually requires an equally simple schema, it is less trivial to operate a different application that has to insert thousands of records per second.
This talk will address the architectural challenges when designing for either read or write performance imposed by HBase. It will include examples of real world use-cases and how they can be implemented on top of HBase, using schemas that optimize for the given access patterns.
The document discusses different types of block caches in HBase including LruBlockCache, SlabCache, and BucketCache. It explains that block caching improves performance by storing frequently accessed blocks in faster memory rather than slower disk storage. Each block cache has its own configuration options and memory usage characteristics. Benchmark results show that the off-heap BucketCache provides strong performance due to its use of off-heap memory for the L2 cache.
HBase is a distributed, scalable, big data store modeled after Google's Bigtable. The document outlines the key aspects of HBase, including that it uses HDFS for storage, Zookeeper for coordination, and can optionally use MapReduce for batch processing. It describes HBase's architecture with a master server distributing regions across multiple region servers, which store and serve data from memory and disks.
Lars George and Jon Hsieh presented archetypes for common Apache HBase application patterns. They defined archetypes as common architecture patterns extracted from multiple use cases to be repeatable. The presentation covered "good" archetypes that are well-suited to HBase's capabilities, such as storing simple entities, messaging data, and metrics. "Bad" archetypes that are not optimal fits for HBase included using it as a large blob store, naively porting a relational database schema, and as an analytic archive requiring frequent full scans. A discussion of access patterns and tradeoffs concluded the overview of HBase application archetypes.
Speaker: Jesse Anderson (Cloudera)
As optional pre-conference prep for attendees who are new to HBase, this talk will offer a brief Cliff's Notes-level talk covering architecture, API, and schema design. The architecture section will cover the daemons and their functions, the API section will cover HBase's GET, PUT, and SCAN classes; and the schema design section will cover how HBase differs from an RDBMS and the amount of effort to place on schema and row-key design.
This talk delves into the many ways that a user has to use HBase in a project. Lars will look at many practical examples based on real applications in production, for example, on Facebook and eBay and the right approach for those wanting to find their own implementation. He will also discuss advanced concepts, such as counters, coprocessors and schema design.
Tokyo HBase Meetup - Realtime Big Data at Facebook with Hadoop and HBase (ja)tatsuya6502
This is the Japanese translation of the presentation at Tokyo HBase Meetup (July 1, 2011)
Author:
Jonathan Gray
Software Engineer / HBase Commiter at Facebook
HBaseCon 2013: Full-Text Indexing for Apache HBaseCloudera, Inc.
This document discusses full-text indexing for HBase tables. It describes how Lucene indices are organized based on HBase regions. Index building is implemented using coprocessors to update indices on data changes. Index splitting is optimized to avoid blocking updates during region splits. Search performance of indexing 10 billion records was tested, showing search times of around 1 second.
HBase Vs Cassandra Vs MongoDB - Choosing the right NoSQL databaseEdureka!
NoSQL includes a wide range of different database technologies and were developed as a result of surging volume of data stored. Relational databases are not capable of coping with this huge volume and faces agility challenges. This is where NoSQL databases have come in to play and are popular because of their features. The session covers the following topics to help you choose the right NoSQL databases:
Traditional databases
Challenges with traditional databases
CAP Theorem
NoSQL to the rescue
A BASE system
Choose the right NoSQL database
HBase is a NoSQL database that stores data in HDFS in a distributed, scalable, reliable way for big data. It is column-oriented and optimized for random read/write access to big data in real-time. HBase is not a relational database and relies on HDFS. Common use cases include flexible schemas, high read/write rates, and real-time analytics. Apache Phoenix provides a SQL interface for HBase, allowing SQL queries, joins, and familiar constructs to manage data in HBase tables.
This document discusses integrating Apache Hive with Apache HBase. It provides an overview of Hive and HBase, the motivation for integrating the two systems, and how the integration works. Specifically, it covers how the schema and data types are mapped between Hive and HBase, how filters can be pushed down from Hive to HBase to optimize queries, bulk loading data from Hive into HBase, and security aspects of the integrated system. The document is intended to provide background and technical details on using Hive and HBase together.
This document discusses integrating Apache Hive and HBase. It provides an overview of Hive and HBase, describes use cases for querying HBase data using Hive SQL, and outlines features and improvements for Hive and HBase integration. Key points include mapping Hive schemas and data types to HBase tables and columns, pushing filters and other operations down to HBase, and using a storage handler to interface between Hive and HBase. The integration allows analysts to query both structured Hive and unstructured HBase data using a single SQL interface.
Apache Hive provides SQL-like access to your stored data in Apache Hadoop. Apache HBase stores tabular data in Hadoop and supports update operations. The combination of these two capabilities is often desired, however, the current integration show limitations such as performance issues. In this talk, Enis Soztutar will present an overview of Hive and HBase and discuss new updates/improvements from the community on the integration of these two projects. Various techniques used to reduce data exchange and improve efficiency will also be provided.
The document proposes using MapReduce jobs to perform scans over HBase snapshots. Snapshots provide immutable data from HBase tables. The MapReduce jobs would bypass region servers and scan snapshot files directly for improved performance. An initial implementation called TableSnapshotInputFormat is described which restores snapshot data and runs scans in parallel across map tasks. The implementation addresses security and performance aspects. An API for client-side scanning of snapshots is also proposed to allow snapshot scans outside of MapReduce.
Sept 17 2013 - THUG - HBase a Technical IntroductionAdam Muise
HBase Technical Introduction. This deck includes a description of memory design, write path, read path, some operational tidbits, SQL on HBase (Phoenix and Hive), as well as HOYA (HBase on YARN).
HBaseCon 2013: Apache HBase and HDFS - Understanding Filesystem Usage in HBaseCloudera, Inc.
This document discusses file system usage in HBase. It describes the main file types in HBase including write ahead logs (WALs), data files, and reference files. It covers topics like durability semantics, IO fencing, and data locality techniques used in HBase like short circuit reads, checksums, and block placement. The document is presented by Enis Söztutar and is intended to help understand how HBase performs IO operations over HDFS for tuning performance.
The document provides an introduction to NoSQL and HBase. It discusses what NoSQL is, the different types of NoSQL databases, and compares NoSQL to SQL databases. It then focuses on HBase, describing its architecture and components like HMaster, regionservers, Zookeeper. It explains how HBase stores and retrieves data, the write process involving memstores and compaction. It also covers HBase shell commands for creating, inserting, querying and deleting data.
Hive is a data warehouse infrastructure tool used to process large datasets in Hadoop. It allows users to query data using SQL-like queries. Hive resides on HDFS and uses MapReduce to process queries in parallel. It includes a metastore to store metadata about tables and partitions. When a query is executed, Hive's execution engine compiles it into a MapReduce job which is run on a Hadoop cluster. Hive is better suited for large datasets and queries compared to traditional RDBMS which are optimized for transactions.
Horizon is a distributed SQL database that allows users to query and analyze big data stored in HBase using a familiar SQL interface. It uses the H2 database engine and customizes HBase's data model to provide features like indexing, partitioning, and SQL support. Horizon aims to make big data more accessible while maintaining HBase's scalability. It will integrate with Hadoop ecosystems and provide high performance data loading, scanning, and analysis tools. Horizon's architecture distributes the SQL engine across servers and uses HBase as the distributed storage layer.
HBaseCon 2013: Integration of Apache Hive and HBaseCloudera, Inc.
This document discusses integrating Apache Hive with HBase. It describes how Hive can be used to query HBase tables via a storage handler. Key features covered include using HBase as a data source or sink for Hive, mapping Hive schemas and types to HBase schemas, pushing filters down to HBase, and bulk loading data. The future of Hive and HBase integration could include improvements to schema mapping, filter pushdown support, and leveraging new HBase typing APIs.
Atlanta meetup presentation, discussion around big data processing engines (Hive, HBase, Druid, Spark). Weighs the relative strengths of each engine and which use cases each of the engines are most suited for
Hadoop Demystified + MapReduce (Java and C#), Pig, and Hive DemosLester Martin
A walk-thru of core Hadoop, the ecosystem tools, and Hortonworks Data Platform (HDP) followed by code examples in MapReduce (Java and C#), Pig, and Hive.
Presented at the Atlanta .NET User Group meeting in July 2014.
This document summarizes a talk about Facebook's use of HBase for messaging data. It discusses how Facebook migrated data from MySQL to HBase to store metadata, search indexes, and small messages in HBase for improved scalability. It also outlines performance improvements made to HBase, such as for compactions and reads, and future plans such as cross-datacenter replication and running HBase in a multi-tenant environment.
The document discusses Facebook's use of HBase to store messaging data. It provides an overview of HBase, including its data model, performance characteristics, and how it was a good fit for Facebook's needs due to its ability to handle large volumes of data, high write throughput, and efficient random access. It also describes some enhancements Facebook made to HBase to improve availability, stability, and performance. Finally, it briefly mentions Facebook's migration of messaging data from MySQL to their HBase implementation.
The document discusses Facebook's use of HBase as the database storage engine for its messaging platform. It provides an overview of HBase, including its data model, architecture, and benefits like scalability, fault tolerance, and simpler consistency model compared to relational databases. The document also describes Facebook's contributions to HBase to improve performance, availability, and achieve its goal of zero data loss. It shares Facebook's operational experiences running large HBase clusters and discusses its migration of messaging data from MySQL to a de-normalized schema in HBase.
hbaseconasia2019 Bridging the Gap between Big Data System Software Stack and ...Michael Stack
Huan-Ping Su (蘇桓平), Yi-Sheng Lien (連奕盛) National Cheng Kung University
Track 2: Ecology and Solutions
https://open.mi.com/conference/hbasecon-asia-2019
THE COMMUNITY EVENT FOR APACHE HBASE™
July 20th, 2019 - Sheraton Hotel, Beijing, China
https://hbase.apache.org/hbaseconasia-2019/
HDFS has several strengths: horizontally scale its IO bandwidth and scale its storage to petabytes of storage. Further, it provides very low latency metadata operations and scales to over 60K concurrent clients. Hadoop 3.0 recently added Erasure Coding. One of HDFS’s limitations is scaling a number of files and blocks in the system. We describe a radical change to Hadoop’s storage infrastructure with the upcoming Ozone technology. It allows Hadoop to scale to tens of billions of files and blocks and, in the future, to every larger number of smaller objects. Ozone fundamentally separates the namespace layer and the block layer allowing new namespace layers to be added in the future. Further, the use of RAFT protocol has allowed the storage layer to be self-consistent. We show how this technology helps a Hadoop user and also what it means for evolving HDFS in the future. We will also cover the technical details of Ozone.
The document provides an overview of the state of the Apache HBase database project. It discusses the project goals of availability, stability, and scalability. It also summarizes the mature codebase, active development areas like region replicas and ProcedureV2, and the growing ecosystem of SQL interfaces and other Hadoop components integrated with HBase. Recent releases include 1.1.2 which improved scanners and introduced quotas and throttling, and the 1.0 release which adopted semantic versioning and added region replicas.
This document provides an overview of Apache Phoenix, including:
- What Phoenix is and how it provides a SQL interface for Apache HBase
- The current state of Phoenix including SQL support, secondary indexes, and optimizations
- New features in Phoenix 4.4 like functional indexes, user defined functions, and integration with Spark
The presentation covers the evolution and capabilities of Phoenix as a relational layer for HBase that transforms SQL queries into native HBase API calls.
We start by looking at distributed database features that impact latency. Then we take a deeper look at the HBase read and write paths with a focus on request latency. We examine the sources of latency and how to minimize them.
If you've used a modern, interactive map such as Google or Bing Maps, you've consumed "map tiles". Map tiles are small images rendering a piece of the mosaic that is the whole map. Using conventional means, rendering tiles for the whole globe at multiple resolutions is a huge data processing effort. Even highly optimized, it spans a couple TBs and a few days of computation. Enter Hadoop. In this talk, I'll show you how to generate your own custom tiles using Hadoop. There will be pretty pictures.
The document discusses the HBase client API for connecting to HBase clusters from applications like webapps. It describes the Java, Ruby, Python, and Thrift client interfaces as well as examples of using scans and puts with these interfaces. It also briefly mentions the REST client interface and some other alternative client libraries like asynchbase and Orderly.
This document introduces Pig, an open source platform for analyzing large datasets that sits on top of Hadoop. It provides an example of using Pig Latin to find the top 5 most visited websites by users aged 18-25 from user and website data. Key points covered include who uses Pig, how it works, performance advantages over MapReduce, and upcoming new features. The document encourages learning more about Pig through online documentation and tutorials.
Introduction to Hadoop, HBase, and NoSQLNick Dimiduk
The document is a presentation on NoSQL databases given by Nick Dimiduk. It begins with an introduction of the speaker and their background. The presentation then covers what NoSQL is not, the motivations for NoSQL databases, an overview of Hadoop and its components, and a description of HBase as a structured, distributed database built on Hadoop.
"Hands-on development experience using wasm Blazor", Furdak Vladyslav.pptxFwdays
I will share my personal experience of full-time development on wasm Blazor
What difficulties our team faced: life hacks with Blazor app routing, whether it is necessary to write JavaScript, which technology stack and architectural patterns we chose
What conclusions we made and what mistakes we committed
Top 12 AI Technology Trends For 2024.pdfMarrie Morris
Technology has become an irreplaceable component of our daily lives. The role of AI in technology revolutionizes our lives for the betterment of the future. In this article, we will learn about the top 12 AI technology trends for 2024.
Keynote : Presentation on SASE TechnologyPriyanka Aash
Secure Access Service Edge (SASE) solutions are revolutionizing enterprise networks by integrating SD-WAN with comprehensive security services. Traditionally, enterprises managed multiple point solutions for network and security needs, leading to complexity and resource-intensive operations. SASE, as defined by Gartner, consolidates these functions into a unified cloud-based service, offering SD-WAN capabilities alongside advanced security features like secure web gateways, CASB, and remote browser isolation. This convergence not only simplifies management but also enhances security posture and application performance across global networks and cloud environments. Discover how adopting SASE can streamline operations and fortify your enterprise's digital transformation strategy.
The Zaitechno Handheld Raman Spectrometer is a powerful and portable tool for rapid, non-destructive chemical analysis. It utilizes Raman spectroscopy, a technique that analyzes the vibrational fingerprint of molecules to identify their chemical composition. This handheld instrument allows for on-site analysis of materials, making it ideal for a variety of applications, including:
Material identification: Identify unknown materials, minerals, and contaminants.
Quality control: Ensure the quality and consistency of raw materials and finished products.
Pharmaceutical analysis: Verify the identity and purity of pharmaceutical compounds.
Food safety testing: Detect contaminants and adulterants in food products.
Field analysis: Analyze materials in the field, such as during environmental monitoring or forensic investigations.
The Zaitechno Handheld Raman Spectrometer is easy to use and features a user-friendly interface. It is compact and lightweight, making it ideal for field applications. With its rapid analysis capabilities, the Zaitechno Handheld Raman Spectrometer can help you improve efficiency and productivity in your research or quality control workflows.
Garbage In, Garbage Out: Why poor data curation is killing your AI models (an...Zilliz
Enterprises have traditionally prioritized data quantity, assuming more is better for AI performance. However, a new reality is setting in: high-quality data, not just volume, is the key. This shift exposes a critical gap – many organizations struggle to understand their existing data and lack effective curation strategies and tools. This talk dives into these data challenges and explores the methods of automating data curation.
Cracking AI Black Box - Strategies for Customer-centric Enterprise ExcellenceQuentin Reul
The democratization of Generative AI is ushering in a new era of innovation for enterprises. Discover how you can harness this powerful technology to deliver unparalleled customer value and securing a formidable competitive advantage in today's competitive market. In this session, you will learn how to:
- Identify high-impact customer needs with precision
- Harness the power of large language models to address specific customer needs effectively
- Implement AI responsibly to build trust and foster strong customer relationships
Whether you're at the early stages of your AI journey or looking to optimize existing initiatives, this session will provide you with actionable insights and strategies needed to leverage AI as a powerful catalyst for customer-driven enterprise success.
Choosing the Best Outlook OST to PST Converter: Key Features and Considerationswebbyacad software
When looking for a good software utility to convert Outlook OST files to PST format, it is important to find one that is easy to use and has useful features. WebbyAcad OST to PST Converter Tool is a great choice because it is simple to use for anyone, whether you are tech-savvy or not. It can smoothly change your files to PST while keeping all your data safe and secure. Plus, it can handle large amounts of data and convert multiple files at once, which can save you a lot of time. It even comes with 24*7 technical support assistance and a free trial, so you can try it out before making a decision. Whether you need to recover, move, or back up your data, Webbyacad OST to PST Converter is a reliable option that gives you all the support you need to manage your Outlook data effectively.
This PDF delves into the aspects of information security from a forensic perspective, focusing on privacy leaks. It provides insights into the methods and tools used in forensic investigations to uncover and mitigate privacy breaches in mobile and cloud environments.
Finetuning GenAI For Hacking and DefendingPriyanka Aash
Generative AI, particularly through the lens of large language models (LLMs), represents a transformative leap in artificial intelligence. With advancements that have fundamentally altered our approach to AI, understanding and leveraging these technologies is crucial for innovators and practitioners alike. This comprehensive exploration delves into the intricacies of GenAI, from its foundational principles and historical evolution to its practical applications in security and beyond.
It's your unstructured data: How to get your GenAI app to production (and spe...Zilliz
So you've successfully built a GenAI app POC for your company -- now comes the hard part: bringing it to production. Aparavi addresses the challenges of AI projects while addressing data privacy and PII. Our Service for RAG helps AI developers and data scientists to scale their app to 1000s to millions of users using corporate unstructured data. Aparavi’s AI Data Loader cleans, prepares and then loads only the relevant unstructured data for each AI project/app, enabling you to operationalize the creation of GenAI apps easily and accurately while giving you the time to focus on what you really want to do - building a great AI application with useful and relevant context. All within your environment and never having to share private corporate data with anyone - not even Aparavi.