This document provides an introduction to the Netezza database appliance, including its architecture and key components. The Netezza uses an Asymmetric Massively Parallel Processing (AMPP) architecture with an array of servers (S-Blades) connected to disks. Each S-Blade contains a Database Accelerator card that offloads processing from the CPU. The document outlines the various hardware components and how they work together to process queries in parallel. It also defines common Netezza objects like users, groups, tables and databases that can be created and managed.
Meta/Facebook's database serving social workloads is running on top of MyRocks (MySQL on RocksDB). This means our performance and reliability depends a lot on RocksDB. Not just MyRocks, but also we have other important systems running on top of RocksDB. We have learned many lessons from operating and debugging RocksDB at scale.
In this session, we will offer an overview of RocksDB, key differences from InnoDB, and share a few interesting lessons learned from production.
This document provides an overview of installing and configuring a 3 node GPFS cluster. It discusses using 8 shared LUNs across the 3 servers to simulate having disks from 2 different V7000 storage arrays for redundancy. The disks will be divided into 2 failure groups, with hdisk1-4 in one failure group representing one simulated array, and hdisk5-8 in the other failure group representing the other simulated array. This is to ensure redundancy in case of failure of an entire storage array.
Deploying MariaDB databases with containers at Nokia NetworksMariaDB plc
Nokia is focused on providing software and products that facilitate rapid development, deployment and scaling of products and services to customers. The Common Software Foundation (CSF) within Nokia develops and supports product reuse by multiple applications within Nokia, including MariaDB. Their focus over the last year has been to develop a containerized MariaDB solution supporting multiple architectures, including both clustering and primary/secondary replication with MariaDB MaxScale. In this talk, Rick Lane discusses this journey of these containerized solutions from development to customer trials, including problems encountered and solutions.
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.
The document is a slide presentation about running Linux on IBM Power systems. It discusses why Linux is widely used, best practices for installing and configuring Linux on Power systems, and options for deploying Linux workloads including the Integrated Facility for Linux (IFL). The IFL allows customers to activate unused cores and memory on Power 770, 780, and 795 systems running only Linux at a lower cost than other hardware platforms.
Linux provide facilities to expose emulated LUNs to initiators using Linux-IO (LIO) scsi target implementation . LIO not only support exposing conventional block devices but also supports other storage interfaces like file or memory based LUNs. Also it supports multiple fabric interfaces - FC, FCoE, iscsi and many more.
LIO can be used in SAN environments with minimal storage resources.
Native support for LIO in linux hypervisors and in Openstack make it a good storage option for cloud deployments.
MariaDB Performance Tuning and OptimizationMariaDB plc
This document discusses MariaDB performance tuning and optimization. It covers common principles like tuning from the start of application development. Specific topics discussed include server hardware, OS settings, MariaDB configuration settings like innodb_buffer_pool_size, database design best practices, and query monitoring and tuning tools. The overall goal is to efficiently use hardware resources, ensure best performance for users, and avoid outages.
My Experience Using Oracle SQL Plan Baselines 11g/12cNelson Calero
This presentation shows how to use the Oracle database functionality SQL Plan Baselines, with examples from real life usage on production (mostly 11gR2) and how to troubleshoot it.
SQL Plan Baselines is a feature introduced on 11g to manage SQL execution plans to prevent performance regressions. The concepts will be presented, along with examples, and some edge cases.
Seastore: Next Generation Backing Store for CephScyllaDB
Ceph is an open source distributed file system addressing file, block, and object storage use cases. Next generation storage devices require a change in strategy, so the community has been developing crimson-osd, an eventual replacement for ceph-osd intended to minimize cpu overhead and improve throughput and latency. Seastore is a new backing store for crimson-osd targeted at emerging storage technologies including persistent memory and ZNS devices.
DB2 is a multi-platform database server that can scale from laptops to large systems handling terabytes of data. It provides tools for extending capabilities to support multimedia, is fully integrated for web access, and supports universal access and multiple platforms. The tutorial covered key DB2 concepts like instances, schemas, tables, and indexes. It demonstrated how to use Control Center and other GUIs to perform tasks like creating databases and tables, querying data, and setting user privileges. Java applications can also access DB2 data through JDBC.
This document provides an overview of Exadata patching. It discusses that patching has improved over time. Oracle will patch Exadata systems for customers with support contracts. Exadata patches are applied using patchmgr and involve pushing new OS images to storage cells which reboot multiple times. Database servers are patched using yum. Quarterly database patches contain RDBMS, CRS, and Diskmon patches applied together using opatch. It is important to test patches in non-production first and have a patching plan.
This document discusses various types of enqueue waits in Oracle related to locks, including row locks, transaction locks, and table modification locks. It provides examples of how to interpret the lock type and mode from the event and parameter values seen in wait events. It also demonstrates how to use Active Session History, logminer, and other views to identify the blocking session, lock details, and blocking SQL associated with enqueue waits.
About the author: Priya Autee is software engineer at Intel working on various leading edge IA features and Intel(R) RDT expert. She is focused on prototyping and researching open source APIs like DPDK, Intel(R) RDT etc. to support NFV/compute sensitive requirements on Intel Architecture. She holds Masters in Computer Science from Arizona State University, Arizona.
MySQL Enterprise Backup - BnR ScenariosKeith Hollman
A quick intro of what MEB is, but then a more hands-on approach to how to backup MySQL, what options are available and then how to restore accordingly.
This document discusses indexing in Oracle Exadata. It begins by providing background on the speaker and their experience. It then discusses how Exadata storage server software, including hybrid columnar compression and smart flash cache, can accelerate queries. The document shows an example of how a query that previously took minutes can take seconds on Exadata due to smart scans. It discusses how indexes may no longer provide benefits and can even reduce performance on Exadata. The document considers whether indexes should be dropped or if the decision is more complex. It analyzes the costs of using indexes versus full table scans on Exadata. Finally, it provides examples to illustrate smart scans.
The document is a presentation about Exadata and Oracle 11gR2. It provides background on the speaker's experience with parallel query over the years. It then covers key features of Exadata storage solutions and new parallel query capabilities in 11gR2 like auto degree of parallelism and statement queuing. The presentation notes some practical considerations for implementing Exadata and parallel query, such as partitioning workloads and monitoring statement queuing.
The document discusses HDFS architecture and components. It describes how HDFS uses NameNodes and DataNodes to store and retrieve file data in a distributed manner across clusters. The NameNode manages the file system namespace and regulates access to files by clients. DataNodes store file data in blocks and replicate them for fault tolerance. The document outlines the write and read workflows in HDFS and how NameNodes and DataNodes work together to manage data storage and access.
Parallel processing involves executing multiple tasks simultaneously using multiple cores or processors. It can provide performance benefits over serial processing by reducing execution time. When developing parallel applications, developers must identify independent tasks that can be executed concurrently and avoid issues like race conditions and deadlocks. Effective parallelization requires analyzing serial code to find optimization opportunities, designing and implementing concurrent tasks, and testing and tuning to maximize performance gains.
The document provides an overview of the fundamentals of Websphere MQ including:
- The key MQ objects like messages, queues, channels and how they work
- Basic MQ administration tasks like defining, displaying, altering and deleting MQ objects using MQSC commands
- Hands-on exercises are included to demonstrate programming with MQ and administering MQ objects
The document discusses project risk management. It defines project risk as the loss multiplied by the likelihood. Successful project leaders plan thoroughly to understand challenges, anticipate problems, and minimize variation. Project failures can occur if objectives are impossible, deliverables are possible but other objectives are unrealistic, or feasible deliverables and objectives but insufficient planning. Risk management includes qualitative and quantitative risk assessment to understand probability and impact of risks. It is important to document risks, have risk management plans, and regularly review assumptions and risks.
There are two main types of relational database management systems (RDBMS): row-based and columnar. Row-based systems store all of a row's data contiguously on disk, while columnar systems store each column's data together across all rows. Columnar databases are generally better for read-heavy workloads like data warehousing that involve aggregating or retrieving subsets of columns, whereas row-based databases are better for transactional systems that require updating or retrieving full rows frequently. The optimal choice depends on the specific access patterns and usage of the data.
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
NENUG Apr14 Talk - data modeling for netezzaBiju Nair
This document discusses considerations for data modeling on Netezza appliances to optimize performance. It recommends distributing data uniformly across snippet processors to maximize parallel processing. When joining tables, the distribution key should match join columns to keep processors independent. Zone maps and clustered tables can reduce data reads from disk. Materialized views on frequently accessed columns further improve performance for single table and join queries.
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 introduction to Netezza fundamentals for application developers. It describes Netezza's Asymmetric Massively Parallel Processing architecture, which uses an array of servers called S-Blades connected to disks and database accelerator cards to process large volumes of data in parallel. The document aims to help readers quickly understand and use the Netezza appliance through explanations of its components and query processing. It also defines key Netezza terminology and objects.
Actian Vector is a high performance analytics database that exploits modern CPU features like SIMD instructions to process large volumes of data much faster than traditional databases. It uses CPU caches rather than RAM for execution memory and avoids overhead by processing vectors of data at once. Actian Vector also leverages industry best practices like columnar storage and compression to optimize input/output and further improve performance for data warehouse workloads. Its unique ability to fully utilize CPU features allows it to run workloads on a single server that would require multiple servers on other databases.
Xd planning guide - storage best practicesNuno Alves
This document provides guidelines for planning storage infrastructure for Citrix XenDesktop environments. It discusses organizational requirements like alignment with IT strategy and high availability needs. Technical requirements covered include performance needs like typical I/O rates and functional requirements like supported protocols. The document recommends avoiding bottlenecks, choosing appropriate RAID levels based on read/write ratios, validating storage performance, and involving storage vendors in planning.
Log Analysis Engine with Integration of Hadoop and SparkIRJET Journal
The document proposes a log analysis system that integrates Hadoop, Spark, Hive, and Shark to analyze large volumes of log data efficiently. The system would extract, transform, and load log data into Hadoop and Hive for batch processing using MapReduce. It would also use Spark and Shark for faster interactive querying and iterative algorithms. This combination of tools is meant to provide a scalable, high-performance platform for log analysis that can handle both large-scale batch processing and real-time queries.
IRJET - The 3-Level Database Architectural Design for OLAP and OLTP OpsIRJET Journal
This document proposes a 3-level database design to improve performance for both OLTP and OLAP operations. It involves categorizing tables based on usage and applying different techniques at each level. Highly transactional tables are partitioned and stored in memory. Frequently used small tables are kept solely in memory. Larger analytical tables use partitioning. Archived data uses compression. This stratified design aims to optimize access speeds and query performance by placing frequently and recently used data in faster memory tiers while compressing less used historical data.
Here are three summaries of 300 words or less on the topics you provided:
Essay 1:
Web services allow different applications and systems to communicate and exchange data over the internet in a standardized way, regardless of programming language or operating system. This is achieved through the use of XML, SOAP, WSDL and UDDI. XML provides the data format, SOAP defines how to encode operation calls and responses, WSDL describes the services available and UDDI allows services to be published and discovered.
Together these technologies provide a standardized way for systems to programmatically discover, describe and integrate web services in a platform and language independent manner. This interoperability is a key benefit, allowing organizations to more easily integrate
This document summarizes key differences between front-end applications like Access and the SQL Server backend. It also provides overviews of SQL Server transactions, server architecture including protocols and components, how select and update requests are processed, and uses of dynamic management views.
Cosmos DB Real-time Advanced Analytics WorkshopDatabricks
The workshop implements an innovative fraud detection solution as a PoC for a bank who provides payment processing services for commerce to their merchant customers all across the globe, helping them save costs by applying machine learning and advanced analytics to detect fraudulent transactions. Since their customers are around the world, the right solutions should minimize any latencies experienced using their service by distributing as much of the solution as possible, as closely as possible, to the regions in which their customers use the service. The workshop designs a data pipeline solution that leverages Cosmos DB for both the scalable ingest of streaming data, and the globally distributed serving of both pre-scored data and machine learning models. Cosmos DB’s major advantage when operating at a global scale is its high concurrency with low latency and predictable results.
This combination is unique to Cosmos DB and ideal for the bank needs. The solution leverages the Cosmos DB change data feed in concert with the Azure Databricks Delta and Spark capabilities to enable a modern data warehouse solution that can be used to create risk reduction solutions for scoring transactions for fraud in an offline, batch approach and in a near real-time, request/response approach. https://github.com/Microsoft/MCW-Cosmos-DB-Real-Time-Advanced-Analytics Takeaway: How to leverage Azure Cosmos DB + Azure Databricks along with Spark ML for building innovative advanced analytics pipelines.
The document discusses troubleshooting performance issues for SQL Server. It begins with an introduction and case study on the MS Society of Canada's website. It then discusses optimizing the environment, using Performance Monitor (PerfMon) to monitor performance, and concludes with recommendations to address issues like high CPU usage, slow disk speeds, and insufficient memory.
This document discusses moving from batch processing of data using MapReduce jobs to real-time event-driven collection of analytical data. It analyzes data sets from a company called Kivra to determine if a traditional SQL database could handle the scale needed. Tests show the technique of using an asynchronous logging system to insert real-time event data into a SQL database can efficiently handle over 1 million insertions per hour per database node while still providing historical and aggregated data for analysis. The resulting system provides improved scalability over the previous batch-based approach.
Extending Apache Spark SQL Data Source APIs with Join Push Down with Ioana De...Databricks
This document summarizes a presentation on extending Spark SQL Data Sources APIs with join push down. The presentation discusses how join push down can significantly improve query performance by reducing data transfer and exploiting data source capabilities like indexes. It provides examples of join push down in enterprise data pipelines and SQL acceleration use cases. The presentation also outlines the challenges of network speeds and exploiting data source capabilities, and how join push down addresses these challenges. Future work discussed includes building a cost model for global optimization across data sources.
Whitepaper: Exadata Consolidation Success StoryKristofferson A
1. The document discusses database and server consolidation using Oracle Exadata and describes the challenges of managing highly consolidated environments to ensure quality of service.
2. It outlines a 4-step process for accurate provisioning and capacity planning using a tool called the Provisioning Worksheet: collecting database details, defining the target Exadata hardware capacity, creating a provisioning plan, and reviewing resource utilization.
3. The process relies on basic capacity planning to ensure workload requirements fit available capacity. Database CPU and storage requirements are gathered, a target Exadata configuration is set, databases are mapped to nodes in the plan, and final utilization is summarized to identify any capacity shortfalls.
Ssis Best Practices Israel Bi U Ser Group Itay Braunsqlserver.co.il
This document provides best practices and recommendations for SQL Server Integration Services (SSIS). It discusses topics such as logging package runtime information, establishing performance baselines, package configuration, lookup optimization, data profiling, resource utilization, and network optimization. The document also provides tips on narrowing data types, sorting data, using SQL for set operations, and change data capture functionality.
This document provides information about inplant training programs offered by KAASHIV INFOTECH in Chennai, India. It outlines 5-day training schedules for students of CSE/IT/MCA, ECE/EEE, and Mechanical/Civil engineering. The CSE/IT/MCA schedule focuses on topics like Big Data, app development, ethical hacking, and cloud computing. The ECE/EEE schedule covers embedded systems, wireless systems, and CCNA networking. The mechanical/civil schedule includes aircraft design, vehicle movement, and 3D modeling and packaging. The training is handled by professionals and aims to equip students with strong technical skills.
This document provides information about Venkatesan Prabu Jayakantham (Venkat), the Managing Director of KAASHIV INFOTECH, a software company in Chennai. It outlines Venkat's experience in Microsoft technologies and certifications. It also details the various awards he has received throughout his career. Finally, it advertises KAASHIV INFOTECH's inplant training programs for students in fields like computer science, electronics, and mechanical engineering.
Vectorization is a new database technology that provides significant performance improvements through parallel processing. It fully utilizes multiple types of parallelism including symmetric multiprocessing (SMP), massively parallel processing (MPP) clusters, graphics processing units (GPUs), and vector processing instructions in Intel CPUs. Early adopters using fully vectorized databases are seeing dramatically lower costs and ability to handle new types of workloads and applications compared to traditional database technologies.
EOUG95 - Client Server Very Large Databases - PaperDavid Walker
The document discusses building large scaleable client/server solutions. It describes breaking the solution into four server components: database server, application server, batch server, and print server. It focuses on the database server, discussing how to make it resilient through clustering and scaleable by partitioning applications and using parallel query options. It also covers backup and recovery strategies.
Fast Analytics (FA) uses an Enterprise Service Bus (ESB) to process high volumes of big data in real time, enabling decision makers to understand new trends and shifts as they occur. FA delivers analytics at decision-making speeds through technologies like Apache Kudu, which provides low latency random access and efficient analytical queries on columnar data. Kudu uses a log-structured storage approach and Raft consensus algorithm to replicate data across nodes for reliability and high availability.
This white paper explains how JethroData can help you achieve truly interactive response times for BI on big data, and how the underlying technology works.
It analyzes the challenges of implementing indexes for big data and how JethroData solved these challenges. It then discusses how the JethroData design of separating compute from storage works with Hadoop and with Amazon S3. Finally, it briefly discusses some of the main features behind JethroData's performance, including I/O, query planning and execution features.
Powering Real-Time Big Data Analytics with a Next-Gen GPU DatabaseKinetica
Freed from the constraints of storage, network and memory, many big data analytics systems now are routinely revealing themselves to be compute bound. To compensate, big data analytic systems often result in wide horizontal sprawl (300-node Spark or NoSQL clusters are not unusual!)— to bring in enough compute for the task at hand. High system complexity and crushing operational costs often result. As the world shifts from physical to virtual assets and methods of engagement, there is an increasing need for systems of intelligence to live alongside the more traditional systems of record and systems of analysis. New approaches to data processing are required to support the real-time processing of data required to drive these systems of intelligence.
Join 451 Research and Kinetica to learn:
•An overview of the business and technical trends driving widespread interest in real-time analytics
•Why systems of analysis need to be transformed and augmented with systems of intelligence bringing new approaches to data processing
•How a new class of solution—a GPU-accelerated, scale out, in-memory database–can bring you orders of magnitude more compute power, significantly smaller hardware footprint, and unrivaled analytic capabilities.
•Hear how other companies in a variety of industries, such as financial services, entertainment, pharmaceutical, and oil and gas, benefit from augmenting their legacy systems with a modern analytics database.
Similar to Netezza fundamentals for developers (20)
Chef conf-2015-chef-patterns-at-bloomberg-scaleBiju Nair
This document discusses various patterns used at Bloomberg for managing infrastructure at scale using Chef. It describes how dedicated bootstrap servers are used to regularly build clusters in an isolated manner. The use of lightweight VMs for bootstrapping is explained. Techniques for building the bootstrap server, cleaning up configurations and converting it to an admin client are outlined. The document also covers topics like dynamic resource creation, injecting logic into community cookbooks, handling service restarts and implementing pluggable alerts.
This document provides an overview of HBase internals and operations. It discusses how HBase is used at Bloomberg to store over 51 TB of compressed data across billions of reads and writes per day. The document then covers key aspects of HBase including its ordered key-value store architecture, write process, read process, versioning, and ACID compliance. It also discusses HBase deployment configurations including masters, region servers, and Zookeeper coordination.
Kafka is a distributed streaming platform. It uses Zookeeper for coordination between brokers. Producers send data to topics which are divided into partitions. Consumers join consumer groups and are assigned partitions. Brokers elect leaders for each partition and replicate data across in-sync replicas for fault tolerance.
Serving queries at low latency using HBaseBiju Nair
This document discusses how Bloomberg uses HBase to serve billions of queries with millisecond latency. It covers HBase principles like being an ordered key-value store and providing ACID transactions. It also discusses modeling data for HBase, including dealing with data and query skew. Implementation details covered include caching, block size tuning, column families, and compaction. The overall goal is to optimize HBase for Bloomberg's low-latency data storage and retrieval needs.
This document discusses Bloomberg's experience moving to a multi-tenant HBase cluster. It provides an overview of HBase features that support multi-tenancy like namespaces, region server groups, storage quotas, and request throttling. It also summarizes Bloomberg's implementation including creation of namespaces, region server groups, and quotas. Performance results showed region server groups improved data locality and throughput. Overall, the speaker concluded HBase's multi-tenancy story is good but could be improved further with enhancements to features like system table availability and memory quotas.
The document discusses cursors in Apache Phoenix. It describes the need for cursors to support row pagination in queries. It outlines the cursor lifecycle including declaring, opening, fetching rows, and closing a cursor. It presents options for implementing cursors by rewriting queries or wrapping result sets. Challenges with cursors include maintaining data consistency across fetches and optimizing caching. Contributors to cursors in Phoenix are also acknowledged.
This document provides an overview of securing Hadoop applications and clusters. It discusses authentication using Kerberos, authorization using POSIX permissions and HDFS ACLs, encrypting HDFS data at rest, and configuring secure communication between Hadoop services and clients. The principles of least privilege and separating duties are important to apply for a secure Hadoop deployment. Application code may need changes to use Kerberos authentication when accessing Hadoop services.
This document summarizes patterns for building clusters using Chef and providing services on demand. It discusses using node attributes to store service requests, templates to generate configuration, and recipes to start services. Separate roles are used to define services and handle restarts. Pluggable alerts allow defining metrics and alerts. Logic injection techniques allow customizing community cookbooks by intercepting notifications and including custom recipes.
Keynote : AI & Future Of Offensive SecurityPriyanka Aash
In the presentation, the focus is on the transformative impact of artificial intelligence (AI) in cybersecurity, particularly in the context of malware generation and adversarial attacks. AI promises to revolutionize the field by enabling scalable solutions to historically challenging problems such as continuous threat simulation, autonomous attack path generation, and the creation of sophisticated attack payloads. The discussions underscore how AI-powered tools like AI-based penetration testing can outpace traditional methods, enhancing security posture by efficiently identifying and mitigating vulnerabilities across complex attack surfaces. The use of AI in red teaming further amplifies these capabilities, allowing organizations to validate security controls effectively against diverse adversarial scenarios. These advancements not only streamline testing processes but also bolster defense strategies, ensuring readiness against evolving cyber threats.
Welcome to Cyberbiosecurity. Because regular cybersecurity wasn't complicated...Snarky Security
How wonderful it is that in our modern age, every bit of our biological data can be digitized, stored, and potentially pilfered by cyber thieves! Isn't it just splendid to think that while scientists are busy pushing the boundaries of biotechnology, hackers could be plotting the next big bio-data heist? This delightful scenario is brought to you by the ever-expanding digital landscape of biology and biotechnology, where the integration of computer science, engineering, and data science transforms our understanding and manipulation of biological systems.
While the fusion of technology and biology offers immense benefits, it also necessitates a careful consideration of the ethical, security, and associated social implications. But let's be honest, in the grand scheme of things, what's a little risk compared to potential scientific achievements? After all, progress in biotechnology waits for no one, and we're just along for the ride in this thrilling, slightly terrifying, adventure.
So, as we continue to navigate this complex landscape, let's not forget the importance of robust data protection measures and collaborative international efforts to safeguard sensitive biological information. After all, what could possibly go wrong?
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This document provides a comprehensive analysis of the security implications biological data use. The analysis explores various aspects of biological data security, including the vulnerabilities associated with data access, the potential for misuse by state and non-state actors, and the implications for national and transnational security. Key aspects considered include the impact of technological advancements on data security, the role of international policies in data governance, and the strategies for mitigating risks associated with unauthorized data access.
This view offers valuable insights for security professionals, policymakers, and industry leaders across various sectors, highlighting the importance of robust data protection measures and collaborative international efforts to safeguard sensitive biological information. The analysis serves as a crucial resource for understanding the complex dynamics at the intersection of biotechnology and security, providing actionable recommendations to enhance biosecurity in an digital and interconnected world.
The evolving landscape of biology and biotechnology, significantly influenced by advancements in computer science, engineering, and data science, is reshaping our understanding and manipulation of biological systems. The integration of these disciplines has led to the development of fields such as computational biology and synthetic biology, which utilize computational power and engineering principles to solve complex biological problems and innovate new biotechnological applications. This interdisciplinary approach has not only accelerated research and development but also introduced new capabilities such as gene editing and biomanufact
DefCamp_2016_Chemerkin_Yury-publish.pdf - Presentation by Yury Chemerkin at DefCamp 2016 discussing mobile app vulnerabilities, data protection issues, and analysis of security levels across different types of mobile applications.
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.
"Making .NET Application Even Faster", Sergey Teplyakov.pptxFwdays
In this talk we're going to explore performance improvement lifecycle, starting with setting the performance goals, using profilers to figure out the bottle necks, making a fix and validating that the fix works by benchmarking it. The talk will be useful for novice and seasoned .NET developers and architects interested in making their application fast and understanding how things work under the hood.
Discovery Series - Zero to Hero - Task Mining Session 1DianaGray10
This session is focused on providing you with an introduction to task mining. We will go over different types of task mining and provide you with a real-world demo on each type of task mining in detail.
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.
Retrieval Augmented Generation Evaluation with RagasZilliz
Retrieval Augmented Generation (RAG) enhances chatbots by incorporating custom data in the prompt. Using large language models (LLMs) as judge has gained prominence in modern RAG systems. This talk will demo Ragas, an open-source automation tool for RAG evaluations. Christy will talk about and demo evaluating a RAG pipeline using Milvus and RAG metrics like context F1-score and answer correctness.
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.
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.
The Challenge of Interpretability in Generative AI Models.pdfSara Kroft
Navigating the intricacies of generative AI models reveals a pressing challenge: interpretability. Our blog delves into the complexities of understanding how these advanced models make decisions, shedding light on the mechanisms behind their outputs. Explore the latest research, practical implications, and ethical considerations, as we unravel the opaque processes that drive generative AI. Join us in this insightful journey to demystify the black box of artificial intelligence.
Dive into the complexities of generative AI with our blog on interpretability. Find out why making AI models understandable is key to trust and ethical use and discover current efforts to tackle this big challenge.
The History of Embeddings & Multimodal EmbeddingsZilliz
Frank Liu will walk through the history of embeddings and how we got to the cool embedding models used today. He'll end with a demo on how multimodal RAG is used.
How UiPath Discovery Suite supports identification of Agentic Process Automat...DianaGray10
📚 Understand the basics of the newly persona-based LLM-powered Agentic Process Automation and discover how existing UiPath Discovery Suite products like Communication Mining, Process Mining, and Task Mining can be leveraged to identify APA candidates.
Topics Covered:
💡 Idea Behind APA: Explore the innovative concept of Agentic Process Automation and its significance in modern workflows.
🔄 How APA is Different from RPA: Learn the key differences between Agentic Process Automation and Robotic Process Automation.
🚀 Discover the Advantages of APA: Uncover the unique benefits of implementing APA in your organization.
🔍 Identifying APA Candidates with UiPath Discovery Products: See how UiPath's Communication Mining, Process Mining, and Task Mining tools can help pinpoint potential APA candidates.
🔮 Discussion on Expected Future Impacts: Engage in a discussion on the potential future impacts of APA on various industries and business processes.
Enhance your knowledge on the forefront of automation technology and stay ahead with Agentic Process Automation. 🧠💼✨
Speakers:
Arun Kumar Asokan, Delivery Director (US) @ qBotica and UiPath MVP
Naveen Chatlapalli, Solution Architect @ Ashling Partners and UiPath MVP