SkySQL is the first and only database-as-a-service (DBaaS) to perform workload analysis with advanced deep learning models, identifying and classifying discrete workload patterns so DBAs can better understand database workloads, identify anomalies and predict changes.
In this session, we’ll explain the concepts behind workload analysis and show how it can be used in the real world (and with sample real-world data) to improve database performance and efficiency by identifying key metrics and changes to cyclical patterns.
“Opening Pandora’s box” - Why bother data model for ERP systems?
This presentation covers :
a. Why should you bother with data modelling when you’ve got or are planning to get an ERP?
i. For requirements gathering.
ii. For Data migration / take on
iii. Master Data alignment
iv. Data lineage (particularly important with Data Lineage & SoX compliance issues)
v. For reporting (Particularly Business Intelligence & Data Warehousing)
vi. But most importantly, for integration of the ERP metadata into your overall Information Architecture.
b. But don’t you get a data model with the ERP anyway?
i. Errr not with all of them (e.g. SAP) – in fact non of them to our knowledge
ii. What can be leveraged from the vendor?
c. How can you incorporate SAP metadata into your overall model?
i. What are the requirements?
ii. How to get inside the black box
iii. Is there any technology available?
iv. What about DIY?
d. So, what are the overall benefits of doing this:
i. Ease of integration
ii. Fitness for purpose
iii. Reuse of data artefacts
iv. No nasty data surprises
v. Alignment with overall data strategy
The document provides an overview of key concepts in data warehousing and business intelligence, including:
1) It defines data warehousing concepts such as the characteristics of a data warehouse (subject-oriented, integrated, time-variant, non-volatile), grain/granularity, and the differences between OLTP and data warehouse systems.
2) It discusses the evolution of business intelligence and key components of a data warehouse such as the source systems, staging area, presentation area, and access tools.
3) It covers dimensional modeling concepts like star schemas, snowflake schemas, and slowly and rapidly changing dimensions.
This document discusses various concepts in data warehouse logical design including data marts, types of data marts (dependent, independent, hybrid), star schemas, snowflake schemas, and fact constellation schemas. It defines each concept and provides examples to illustrate them. Dependent data marts are created from an existing data warehouse, independent data marts are stand-alone without a data warehouse, and hybrid data marts combine data from a warehouse and other sources. Star schemas have one table for each dimension that joins to a central fact table, while snowflake schemas have normalized dimension tables. Fact constellation schemas have multiple fact tables that share dimension tables.
A Practical Enterprise Feature Store on Delta LakeDatabricks
The feature store is a data architecture concept used to accelerate data science experimentation and harden production ML deployments. Nate Buesgens and Bryan Christian describe a practical approach to building a feature store on Delta Lake at a large financial organization. This implementation has reduced feature engineering “wrangling” time by 75% and has increased the rate of production model delivery by 15x. The approach described focuses on practicality. It is informed by innovative approaches such as Feast, but our primary goal is evolutionary extensions of existing patterns that can be applied to any Delta Lake architecture.
Key Takeaways:
– Understand the key use cases that motivate the feature store from both a data science and engineering perspective.
– Consider edge cases where there may be opportunities for simplification such as “online” predictions.
– Review a typical logical data model for a feature store and how that can be applied to your business domain.
– Consider options for physical storage of the feature store in the Delta Lake.
– Understand common access patterns including metadata-based feature discovery.
Real-Time Data Replication to Hadoop using GoldenGate 12c AdaptorsMichael Rainey
Oracle GoldenGate 12c is well known for its highly performant data replication between relational databases. With the GoldenGate Adaptors, the tool can now apply the source transactions to a Big Data target, such as HDFS. In this session, we'll explore the different options for utilizing Oracle GoldenGate 12c to perform real-time data replication from a relational source database into HDFS. The GoldenGate Adaptors will be used to load movie data from the source to HDFS for use by Hive. Next, we'll take the demo a step further and publish the source transactions to a Flume agent, allowing Flume to handle the final load into the targets.
Presented at the Oracle Technology Network Virtual Technology Summit February/March 2015.
Databricks + Snowflake: Catalyzing Data and AI InitiativesDatabricks
"Combining Databricks, the unified analytics platform with Snowflake, the data warehouse built for the cloud is a powerful combo.
Databricks offers the ability to process large amounts of data reliably, including developing scalable AI projects. Snowflake offers the elasticity of a cloud-based data warehouse that centralizes the access to data. Databricks brings the unparalleled utility of being based on a mature distributed big data processing and AI-enabled tool to the table, capable of integrating with nearly every technology, from message queues (e.g. Kafka) to databases (e.g. Snowflake) to object stores (e.g. S3) and AI tools (e.g. Tensorflow).
Key Takeaways:
How Databricks & Snowflake work;
Why they're so powerful;
How Databricks + Snowflake symbiotically catalyze analytics and AI initiatives"
This presentation focuses on the value proposition for Azure Databricks for Data Science. First, the talk includes an overview of the merits of Azure Databricks and Spark. Second, the talk includes demos of data science on Azure Databricks. Finally, the presentation includes some ideas for data science production.
The document summarizes Spark SQL, which is a Spark module for structured data processing. It introduces key concepts like RDDs, DataFrames, and interacting with data sources. The architecture of Spark SQL is explained, including how it works with different languages and data sources through its schema RDD abstraction. Features of Spark SQL are covered such as its integration with Spark programs, unified data access, compatibility with Hive, and standard connectivity.
This is a group assignment by my students on Chapter 2 Retail Sales of the book The Data Warehouse Toolkit: The Complete Guide to Dimensional Modeling
By Ralph Kimball, Margy Ross
All about Big Data components and the best tools to ingest, process, store and visualize the data.
This is a keynote from the series "by Developer for Developers" powered by eSolutionsGrup.
How to track and improve Customer Experience with LEO CDPTrieu Nguyen
This document discusses how to track and improve customer experience using LEO CDP. It begins by explaining why measuring customer experience is important, then introduces four key metrics: Customer Feedback Score, Customer Effort Score, Customer Satisfaction Score, and Net Promoter Score. It describes using journey maps to manage customer experience data and visualize the customer journey. Finally, it presents LEO CDP as a software solution for collecting customer experience data, building surveys, and generating reports to gain insights to improve products, services, and the overall customer experience.
Data platform modernization with Databricks.pptxCalvinSim10
The document discusses modernizing a healthcare organization's data platform from version 1.0 to 2.0 using Azure Databricks. Version 1.0 used Azure HDInsight (HDI) which was challenging to scale and maintain. It presented performance issues and lacked integrations. Version 2.0 with Databricks will provide improved scalability, cost optimization, governance, and ease of use through features like Delta Lake, Unity Catalog, and collaborative notebooks. This will help address challenges faced by consumers, data engineers, and the client.
Splunk produces software for searching, monitoring, and analyzing machine-generated big data. It turns machine data into valuable insights. With the Splunk log file generated using the Splunk cloud product, helps you to not only track your data over the Splunk cloud environment but also analyze and visualize the data as well.
This document outlines the design and implementation of a data warehouse for KostLess, a multinational retail company. It includes details on the business case, dimensional model, data definition language to create the schema, ETL processes, sample reports, and project management considerations. The dimensional model includes facts about sales and dimensions for customers, products, time and currency. The schema uses star schema design with dimension and fact tables linked by primary and foreign keys. Sample SQL is provided to define the tables, constraints, and indexes.
Spark (Structured) Streaming vs. Kafka StreamsGuido Schmutz
Independent of the source of data, the integration and analysis of event streams gets more important in the world of sensors, social media streams and Internet of Things. Events have to be accepted quickly and reliably, they have to be distributed and analyzed, often with many consumers or systems interested in all or part of the events. In this session we compare two popular Streaming Analytics solutions: Spark Streaming and Kafka Streams.
Spark is fast and general engine for large-scale data processing and has been designed to provide a more efficient alternative to Hadoop MapReduce. Spark Streaming brings Spark's language-integrated API to stream processing, letting you write streaming applications the same way you write batch jobs. It supports both Java and Scala.
Kafka Streams is the stream processing solution which is part of Kafka. It is provided as a Java library and by that can be easily integrated with any Java application.
This presentation shows how you can implement stream processing solutions with each of the two frameworks, discusses how they compare and highlights the differences and similarities.
Data warehousing and business intelligence project reportsonalighai
Developed Data warehouse project with a structured, semi-structured and unstructured sources of data
and generated Business Intelligence reports. Topic for the project was Tobacco products consumption in
America. Studied on which products are more famous among people across and also got to know that
middle school students are the soft targets for the tobacco companies as maximum people start taking
tobacco products at this age.
Tools used: SSMS, SSIS, SSAS, SSRS, R-Studio, Power BI, Excel
Project report on the design and build of a data warehouse from unstructured and structured data sources (Quandl, yelp and UK Office for National Statistics) using SQL Server 2016, MongoDB and IBM Watson. Design and implementation of business intelligence visualisations using Tableau to answer cross domain business questions
Cloud Dataflow is a fully managed service and SDK from Google that allows users to define and run data processing pipelines. The Dataflow SDK defines the programming model used to build streaming and batch processing pipelines. Google Cloud Dataflow is the managed service that will run and optimize pipelines defined using the SDK. The SDK provides primitives like PCollections, ParDo, GroupByKey, and windows that allow users to build unified streaming and batch pipelines.
- The document discusses market basket analysis and association rule mining, which are techniques used to analyze purchasing patterns in transactional data.
- It provides an example of an association rule discovered from store transaction data: "If a basket contains beer, it is likely to also contain diapers." Knowing this, the store changed its layout to place diapers and beer next to each other, increasing sales of both products.
- The key measures for evaluating association rules are support, confidence and lift, which indicate how often items are purchased together versus by chance alone. Market basket analysis can help businesses promote complementary products and increase overall revenue.
Migrating Data Warehouse Solutions from Oracle to non-Oracle DatabasesJade Global
Though there are many standardized data warehouse solutions in the industry today, many organizations want to migrate their data warehouses to Oracle based ones primarily because of its widely accepted user base and superb support.
This whitepaper is a step-by-step guide for migrating non-Oracle database solutions to Oracle ones.
Agile Big Data Analytics Development: An Architecture-Centric ApproachSoftServe
Presented at The Hawaii International Conference on System Sciences by Hong-Mei Chen and Rick Kazman (University of Hawaii), Serge Haziyev (SoftServe).
It is a fascinating, explosive time for enterprise analytics.
It is from the position of analytics leadership that the mission will be executed and company leadership will emerge. The data professional is absolutely sitting on the performance of the company in this information economy and has an obligation to demonstrate the possibilities and originate the architecture, data, and projects that will deliver analytics. After all, no matter what business you’re in, you’re in the business of analytics.
The coming years will be full of big changes in enterprise analytics and Data Architecture. William will kick off the fourth year of the Advanced Analytics series with a discussion of the trends winning organizations should build into their plans, expectations, vision, and awareness now.
Saurabh Kumar Gupta is presenting to the Special Selection Committee for a promotion. He has over 10 years of experience as a Project Engineer working with Oracle databases, Tuxedo, and WebLogic technologies. In his role, he has led installations, migrations, performance tuning, and support work. He is seeking a job profile as a core database and storage team member or team lead. He highlights past work optimizing the FOIS infrastructure and contributions to projects implementing industry best practices.
How to Restructure and Modernize Active DirectoryQuest
In this presentation, you’ll learn how to apply best practices for reducing migration risk and avoiding disruption, improve security, ensure compliance and simplify your consolidation, and carefully manage your project before, during and after the active directory merger. You can listen to the presentation here: http://bit.ly/2gowzqI.
Iod session 3423 analytics patterns of expertise, the fast path to amazing ...Rachel Bland
This document provides an overview of the IBM Business Intelligence Pattern with BLU Acceleration. It discusses how this pattern provides a pre-configured deployment for a predictable, high performance analytics solution. It delivers order of magnitude improvements in performance, storage savings, and time to value through the use of in-memory acceleration technologies like Dynamic Cubes and DB2 BLU. Typical performance improvements range from 8-25x over traditional approaches. The pattern allows for a simple, streamlined approach to achieve fast analytics results.
The document discusses the database development life cycle (DBLC), which follows a similar process to the systems development life cycle (SDLC). The DBLC involves gathering requirements, database analysis, design, implementation, testing and evaluation, and maintenance. It describes each stage in detail, including conceptual, logical, and physical data modeling during the design stage. The goal is to systematically plan and develop a database to meet requirements while ensuring completeness, integrity, flexibility, and usability.
3 Keys to Performance Testing at the Speed of AgileNeotys
Many teams on the journey toward true agility attempt to “go faster,” but most fail to bake performance checks into their delivery process. This leads to more rework than new work, fighting fires in production, burnout, a poor customer experience, and maybe most importantly, no real improvement in speed.
Join this web seminar to learn how to fit performance testing into a tight work schedule and discover how to create a performance testing strategy that meets the agile cadence of small batch sizes and sprint deadlines.
www.neotys.com
Achal Dalvi has over 7 years of experience in IT testing. He has expertise in functional, regression, and database testing. He has experience working on projects for clients in various industries using technologies like Oracle, SQL Server, ALM, and Informatica. He has held roles including team lead and software tester and has experience in the full SDLC. He has a Bachelor's degree in Computer Engineering and is proficient in languages including SQL.
The Shifting Landscape of Data IntegrationDATAVERSITY
This document discusses the shifting landscape of data integration. It begins with an introduction by William McKnight, who is described as the "#1 Global Influencer in Data Warehousing". The document then discusses how challenges in data integration are shifting from dealing with volume, velocity and variety to dealing with dynamic, distributed and diverse data in the cloud. It also discusses IDC's view that this shift is occurring from the traditional 3Vs to the 3Ds. The rest of the document discusses Matillion, a vendor that provides a modern solution for cloud data integration challenges.
The document describes scientific workflows for big data and the challenges they present. It discusses Prof. Shiyong Lu's work on developing the VIEW system for designing, executing, and analyzing scientific workflows. The VIEW system provides a runtime environment for workflows, supports their execution on servers or clouds, and enables efficient storage, querying and visualization of workflow provenance data.
Azure SQL Database (SQL DB) is a database-as-a-service (DBaaS) that provides nearly full T-SQL compatibility so you can gain tons of benefits for new databases or by moving your existing databases to the cloud. Those benefits include provisioning in minutes, built-in high availability and disaster recovery, predictable performance levels, instant scaling, and reduced overhead. And gone will be the days of getting a call at 3am because of a hardware failure. If you want to make your life easier, this is the presentation for you.
This document provides an overview of Virtual Instruments and its products for infrastructure performance analytics. Virtual Instruments was founded in 2008 and helps Global 2000 customers across industries manage the performance, availability, and cost of their infrastructure and applications. The company's products include VirtualWisdom for real-time monitoring and analytics, and Load DynamiX for workload modeling and simulation. These tools help organizations optimize infrastructure investments, mitigate risk, guarantee performance, accelerate troubleshooting, and innovate infrastructure with confidence.
- Sudheer Kumar is seeking a challenging position as an Oracle Database and Application Database Administrator with over 3 years of experience in areas such as Oracle database administration, backup and recovery, database creation, installation, cloning, upgrading, and migrating Oracle databases from versions 9i to 12c.
- He has expertise in application upgrading from R12.1.3 to R12.2.4, database administration tasks like monitoring, troubleshooting, backup strategies using RMAN, and disaster recovery planning.
- His technical skills include Oracle database versions 9i to 12c, Linux, and Oracle E-Business applications. He is proficient in SQL, PL/SQL, and has experience working on high availability environments
How to Restructure Active Directory with ZeroIMPACTQuest
We’ll explore best practices for reducing risk and avoiding disruption during AD migrations, ways to improve security, ensure compliance and simplify AD consolidations, and integration processes that can help carefully manage your project before, during and after the actual merger.
Oracle RAC provides high availability, scalability and performance for databases across clustered servers with no application changes required. It uses a shared cache architecture to overcome limitations of traditional shared-nothing and shared-disk approaches. iONE provides Oracle RAC implementation and maintenance services to deliver continuous uptime for database applications through server pool management, datacenter HA, and scaling to 100 nodes.
The document outlines a multi-month implementation plan for a BI project with the following key stages:
1) Preparation and Planning in Month 1 involving prioritization, hardware installation, staffing, and software procurement.
2) ETL development from Month 1-3 involving requirement analysis, design, development and testing of the ETL processes.
3) Initial deployment from Month 2-3 setting up the metadata framework and data governance with report reductions.
4) Ongoing development from Month 4-10 involving further report reductions, incremental deployments, building the data library and dashboards. Headcount savings also take effect during this stage.
5) Long term operations starting from Month 11 involving targeting
Validation and Business Considerations for Clinical Study MigrationsPerficient, Inc.
There are a variety of essential validation and business considerations that should be evaluated when migrating clinical studies from one database to another (with or without existing data). Having a clear understanding of downstream study processes and receiving input from cross-functional teams are just some of the keys to a successful migration.
In this SlideShare we discuss several case studies that provide insight into:
The steps followed during a study migration process, from validation and business perspectives
Validation considerations for migrating into an empty database, as well as a database that already contains data
Suggested documentation for the migration process
Business continuity considerations to ensure a smooth study migration for all team members
Best practices and lessons learned
Scott D. Adams has over 15 years of experience as an Oracle and SQL Server database administrator. He currently works as a senior Oracle DBA at Carter's, where he manages over 30 Oracle databases and provides 24/7 support for critical systems. Previously, he worked as an Oracle DBA for Digicon Corporation and the US Army, and as a database specialist for Rockdale County Public Schools.
MariaDB Paris Workshop 2023 - NewpharmaMariaDB plc
This document summarizes Newpharma's transition from a standalone database server to an enterprise MariaDB Galera cluster configuration between 2018-2023. It discusses the business needs that drove the change, including increased traffic and access to multiple data sources. Key benefits of the Galera cluster are highlighted like synchronous replication, read/write access from any node, and automatic node joining. Challenges of migrating like converting table types and splitting large transactions are also outlined. The transition has supported Newpharma's growth to over 100 million euro in turnover.
MariaDB Paris Workshop 2023 - Performance OptimizationMariaDB plc
MariaDB is an open-source database that is highly tunable and modular. It allows for various storage engines, plugins, and configurations to optimize performance depending on usage. Key aspects that impact performance include memory allocation, disk access, query optimization, and architecture choices like replication, sharding, or using ColumnStore for analytics. Solutions like MyRocks, Spider, MaxScale can improve performance for transactional or large scale workloads by optimizing resources, adding high availability, and distributing load.
MariaDB Paris Workshop 2023 - MaxScale MariaDB plc
The document outlines requirements and criteria for a database solution involving two buildings 30km apart with a WAN link. The chosen solution was MariaDB with Galera cluster for high availability and synchronous replication across sites, along with Maxscale for read/write splitting and failover. Maxscale instances on each site allow for zero downtime database patching and upgrades per site, while the Galera cluster provides structure-independent synchronous replication between sites.
MariaDB Tech und Business Update Hamburg 2023 - MariaDB Enterprise Server MariaDB plc
MariaDB Enterprise Server 10.6 includes the following key features:
- New JSON functions and data types like UUID and INET4.
- Improved Oracle compatibility with function parameters.
- Enhanced partitioning capabilities like converting partitions.
- Optimistic ALTER TABLE for replicas to reduce downtime.
- Online schema changes without locking tables for improved performance.
- Security enhancements including password policies and privilege changes.
MariaDB SkySQL is a cloud database service that provides autonomous scaling, observability, and cloud backup capabilities. It offers multi-cloud and hybrid operations across AWS, Google Cloud, and on-premises databases. The service includes features like the Remote Observability Service (ROS) for monitoring across environments, and a Cloud Backup Service. It aims to provide a simple yet advanced service for scaling databases from small to extreme sizes with tools for automation, self-service, and unified operations.
The document discusses high availability solutions for MariaDB databases. It begins by defining high availability and concepts like Recovery Time Objective (RTO) and Recovery Point Objective (RPO). It then presents different MariaDB and MaxScale architectures that provide high availability, including single node, primary-replica, Galera cluster, and SkySQL solutions. Key aspects covered are automatic failover, load balancing, data filtering, and service level agreements.
Die Neuheiten in MariaDB Enterprise ServerMariaDB plc
This document summarizes new features in MariaDB Enterprise Server. Key points include:
- MariaDB Enterprise Server is geared toward enterprise customers and focuses on stability, robustness, and predictability.
- It has a longer release cycle than Community Server, with new versions every 2 years and long maintenance cycles. New features from Community Server are backported.
- Recent additions include analytics functions, JSON support, bi-temporal modeling, schema changes, database compatibility features, and security enhancements.
- The upcoming 23.x release will include new JSON functions, data types like UUID and INET4, Oracle compatibility features, partitioning improvements, and Galera enhancements.
Global Data Replication with Galera for Ansell Guardian®MariaDB plc
Ansell Guardian® faced challenges with their previous database replication solution as their data and usage grew globally. They evaluated MariaDB/Galera and implemented it to replace their legacy solution. The implementation was smooth using automation scripts. MariaDB/Galera provided increased performance, faster deployment times, and more reliable data synchronization across their 3 data centers compared to their previous solution. It helped resolve a critical data divergence issue and improved the user experience. They plan to further enhance their database infrastructure using MaxScale in the future.
SkySQL uses best-of-breed software, and when it comes to metrics and monitoring that means Prometheus and Grafana. SkySQL Monitor is built on both, and provides customers with interactive dashboards for both real-time and historic metrics monitoring. In addition, it meets the same high availability and security requirements as other SkySQL components, ensuring metrics are always available and always secure.
In this session, we’ll explain how SkySQL Monitor works, walk through its dashboards and show how to monitor key metrics for performance and replication.
Introducing the R2DBC async Java connectorMariaDB plc
Not too long ago, a reactive variant of the JDBC driver was released, known as Reactive Relational Database Connectivity (R2DBC for short). While R2DBC started as an experiment to enable integration of SQL databases into systems that use reactive programming models, it now specifies a full-fledged service-provider interface that can be used to retrieve data from a target data source.
In this session, we’ll take a look at the new MariaDB R2DBC connector and examine the advantages of fully reactive, non-blocking development with MariaDB. And, of course, we’ll dive in and get a first-hand look at what it’s like to use the new connector with some live coding!
The capabilities and features of MariaDB Platform continue to expand, resulting in larger and more sophisticated production deployments – and the need for better tools. To provide DBAs with comprehensive, consolidating tooling, we created MariaDB Enterprise Tools: an easy-to-use, modular command-line interface for interacting with any part of MariaDB Platform.
In this session, we will provide a preview of the MariaDB Enterprise Client, walk through current and planned modules and discuss future plans for MariaDB Enterprise Tools – including SkySQL modules and the ability to create custom modules.
Faster, better, stronger: The new InnoDBMariaDB plc
For MariaDB Enterprise Server 10.5, the default transactional storage engine, InnoDB, has been significantly rewritten to improve the performance of writes and backups. Next, we removed a number of parameters to reduce unnecessary complexity, not only in terms of configuration but of the code itself. And finally, we improved crash recovery thanks to better consistency checks and we reduced memory consumption and file I/O thanks to an all new log record format.
In this session, we’ll walk through all of the improvements to InnoDB, and dive deep into the implementation to explain how these improvements help everything from configuration and performance to reliability and recovery.
SkySQL implements a groundbreaking, state-of-the-art architecture based on Kubernetes and ServiceNow, and with a strong emphasis on cloud security – using compartmentalization and indirect access to secure and protect customer databases.
In this session, we’ll walk through the architecture of SkySQL and discuss how MariaDB leverages an advanced Kubernetes operator and powerful ServiceNow configuration/workflow management to deploy and manage databases on cloud infrastructure.
What to expect from MariaDB Platform X5, part 1MariaDB plc
MariaDB Platform X5 will be based on MariaDB Enterprise Server 10.5. This release includes Xpand, a fully distributed storage engine for scaling out, as well as many new features and improvements for DBAs and developers alike, including enhancements to temporal tables, additional JSON functions, a new performance schema, non-blocking schema changes with clustering and a Hashicorp Vault plugin for key management.
In this session, we’ll walk through all of the new features and enhancements available in MariaDB Enterprise Server 10.5. In addition, we will highlight those being backported to maintenance releases of MariaDB Enterprise Server 10.2, 10.3 and 10.4.
Introduction to Data Science
1.1 What is Data Science, importance of data science,
1.2 Big data and data Science, the current Scenario,
1.3 Industry Perspective Types of Data: Structured vs. Unstructured Data,
1.4 Quantitative vs. Categorical Data,
1.5 Big Data vs. Little Data, Data science process
1.6 Role of Data Scientist
Overview of Statistical software such as ODK, surveyCTO,and CSPro
2. Software installation(for computer, and tablet or mobile devices)
3. Create a data entry application
4. Create the data dictionary
5. Create the data entry forms
6. Enter data
7. Add Edits to the Data Entry Application
8. CAPI questions and texts
Towards an Analysis-Ready, Cloud-Optimised service for FAIR fusion dataSamuel Jackson
We present our work to improve data accessibility and performance for data-intensive tasks within the fusion research community. Our primary goal is to develop services that facilitate efficient access for data-intensive applications while ensuring compliance with FAIR principles [1], as well as adoption of interoperable tools, methods and standards.
The major outcome of our work is the successful creation and deployment of a data service for the MAST (Mega Ampere Spherical Tokamak) experiment [2], leading to substantial enhancements in data discoverability, accessibility, and overall data retrieval performance, particularly in scenarios involving large-scale data access. Our work follows the principles of Analysis-Ready, Cloud Optimised (ARCO) data [3] by using cloud optimised data formats for fusion data.
Our system consists of a query-able metadata catalogue, complemented with an object storage system for publicly serving data from the MAST experiment. We will show how our solution integrates with the Pandata stack [4] to enable data analysis and processing at scales that would have previously been intractable, paving the way for data-intensive workflows running routinely with minimal pre-processing on the part of the researcher. By using a cloud-optimised file format such as zarr [5] we can enable interactive data analysis and visualisation while avoiding large data transfers. Our solution integrates with common python data analysis libraries for large, complex scientific data such as xarray [6] for complex data structures and dask [7] for parallel computation and lazily working with larger that memory datasets.
The incorporation of these technologies is vital for advancing simulation, design, and enabling emerging technologies like machine learning and foundation models, all of which rely on efficient access to extensive repositories of high-quality data. Relying on the FAIR guiding principles for data stewardship not only enhances data findability, accessibility, and reusability, but also fosters international cooperation on the interoperability of data and tools, driving fusion research into new realms and ensuring its relevance in an era characterised by advanced technologies in data science.
[1] Wilkinson, M., Dumontier, M., Aalbersberg, I. et al. The FAIR Guiding Principles for scientific data management and stewardship. Sci Data 3, 160018 (2016) https://doi.org/10.1038/sdata.2016.18
[2] M Cox, The Mega Amp Spherical Tokamak, Fusion Engineering and Design, Volume 46, Issues 2–4, 1999, Pages 397-404, ISSN 0920-3796, https://doi.org/10.1016/S0920-3796(99)00031-9
[3] Stern, Charles, et al. "Pangeo forge: crowdsourcing analysis-ready, cloud optimized data production." Frontiers in Climate 3 (2022): 782909.
[4] Bednar, James A., and Martin Durant. "The Pandata Scalable Open-Source Analysis Stack." (2023).
[5] Alistair Miles (2024) ‘zarr-developers/zarr-python: v2.17.1’. Zenodo. doi: 10.5281/zenodo.10790679
[6] Hoyer, S. & Hamman, J., (20
Combined supervised and unsupervised neural networks for pulse shape discrimi...Samuel Jackson
Our methodology for pulse shape discrimination is split into two steps. Firstly, we learn a model to discriminate between pulses using "clean" low-rate examples by removing pile-up & saturated events. In addition to traditional tail sum discrimination, we investigate three different choices for discrimination between γ-pulses, fast, thermal neutrons. We consider clustering the pulses directly using Gaussian Mixture Modelling (GMM), using variational autoencoders to learn a representation of the pulses and then clustering the learned representation (VAE+GMM) and using density ratio estimation to discriminate between a mixed (γ + neutron) and pure (γ only) sources using a multi-layer perceptron (MLP) as a supervised learning problem.
Secondly, we aim to classify and recover pile-up events in the < 150 ns regime by training a single unified multi-label MLP. To frame the problem as a multi-label supervised learning method, we first simulate pile-up events with known components. Then, using the simulated data and combining it with single event data, we train a final multi-label MLP to output a binary code indicating both how many and which type of events are present within an event window.
Solution Manual for First Course in Abstract Algebra A, 8th Edition by John B...rightmanforbloodline
Solution Manual for First Course in Abstract Algebra A, 8th Edition by John B. Fraleigh, Verified Chapters 1 - 56,.pdf
Solution Manual for First Course in Abstract Algebra A, 8th Edition by John B. Fraleigh, Verified Chapters 1 - 56,.pdf
Big Data and Analytics Shaping the future of PaymentsRuchiRathor2
The payments industry is experiencing a data-driven revolution powered by big data and analytics.
Here's a glimpse into 5 ways this dynamic duo is transforming how we pay.
In essence, big data and analytics are playing a pivotal role in building a future filled with faster, more secure, and convenient payment methods for everyone.
2. Workload analysis – why
2
● Gain deeper insights into database usage
● Optimize resource allocation
○ Reduce costs, improve performance
○ Rinse and repeat (e.g., day vs. night, weekday vs. weekend)
● Take proactive measures (vs. reactive)
● Maintain quality of service (QoS)
● Build a foundation for autonomous services
3. Workload analysis – definition
● Categories
○ Transactional vs. analytical
○ Read vs. write vs. mixed
○ Too simplistic
● Discrete queries
○ Which ones to optimize for?
�� Which ones will be hurt?
○ Too many different queries
Conventional
3
● Resource based
● Database state
● Identifiable
● Time bound
○ Cycles and patterns
○ Evolution
● Statistical
○ Distributions
○ Properties
Modern
4. Workload analysis – insight
● Is the workload changing?
● Are workload changes getting smaller or bigger?
● Do workload changes justify further resource optimization?
● How are workload changes impacting the business?
4
5. Workload analysis – application
● Most important metrics
● Define the workload
● Strong correlation
● Change with/to workload
● Learned by WLA
Critical metrics
5
● Time intervals
● Temporal changes
● Trends and spikes
Historical context
6. Workload analysis – coming next
● Dynamic vs. static
● Per workload vs. global
● Based on change
○ Similarity index
○ Rate, distribution, spread
● No more
○ Manual analysis
○ Needle in a haystack
● Personalized health checks
Proactive monitoring
● Maintaining consistency
● Learned QoS metrics
● Predictive alerts
○ Or, autonomous changes
Quality of Service (QoS)
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8. Workload analysis
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● SkySQL app that lets users
explore database workloads that
were automatically detected by
our Machine Learning platform.
● It gives easy access to interactive
visualizations to help users
understand how database
workloads change over time.
9. Machine learning pipeline
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1. Collect database metrics at 5-sec intervals
2. Extract data from Monitor repository, on an hourly basis
3. Preprocess data to reduce ”noise” and strongly correlated metrics
4. Apply Deep Learning to create working tensor
a. 2000+ sample data points, 600+ model steps
b. Approximately 100+ critical features
5. Cluster the matrix into workloads that exhibit similar behavior
6. Visualize via D3
10. Daily max over time
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Visualize changes in the daily maximum values of 100+
metrics, making it easy to identify historical trends and
recurring patterns
11. Correlated metrics
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Visualize the collective impact of correlated metrics,
identified by deep-learning, on all database workloads
(i.e., metrics that change together).
12. Distribution impact
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Visualize the spread and distribution of metrics so DBAs
can anticipate and optimize resources usage like memory
for performance