MariaDB ColumnStore is a high performance columnar storage engine that provides fast and efficient analytics on large datasets in distributed environments. It stores data column-by-column for high compression and read performance. Queries are processed in parallel across nodes for scalability. MariaDB ColumnStore is used for real-time analytics use cases in industries like healthcare, life sciences, and telecommunications to gain insights from large datasets for applications like customer behavior analysis, genome research, and call data monitoring.
Delivering fast, powerful and scalable analyticsMariaDB plc
This session will provide insight on making the most of your data assets with analytics, and what you need for your next analytics project. We’ll showcase how the MariaDB AX solution delivers fast and scalable analytics using real-world use cases.
This ppt includes an overview of
-OPS Data Mining method,
-mining incomplete servey data,
-automated decision systems,
-real-time data warehousing,
-KPIs,
-Six Sigma Strategy and its possible intergation with Lean approach,
-summary of my OLAP practice with Northwind data set (Access)
Driving Digital Transformation with Machine Learning in Oracle AnalyticsPerficient, Inc.
The adoption of machine learning (ML) is increasing at near-breakneck speed. As organizations seek innovative ideas on how to improve the business, Oracle Analytics Cloud with ML capabilities is leading the charge. With built-in drag-and-drop functions into visualizations and autonomous prediction execution, Oracle Analytics puts the power of machine learning in your hands.
We covered how Oracle Analytics can connect various data sources, allow you to apply ML without being statistically savvy, and easily build your story in presentation format.
Discussion included:
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-How to connect different data sources like SaaS applications, data lakes, external data sources and more
-Custom-trained ML models demonstration
-Real-world business use case from end to end
In his Data Management Workshop at the 8th ETOT Summit in London, October 2016 - DataGenic's CTO Colin Hartley looked at trends and best practice when it comes to commodity data management. As well as sharing the dos and don'ts of forward curve creation and management.
Delivering fast, powerful and scalable analyticsMariaDB plc
This session will provide insight on making the most of your data assets with analytics, and what you need for your next analytics project. We’ll showcase how the MariaDB AX solution delivers fast and scalable analytics using real-world use cases.
This ppt includes an overview of
-OPS Data Mining method,
-mining incomplete servey data,
-automated decision systems,
-real-time data warehousing,
-KPIs,
-Six Sigma Strategy and its possible intergation with Lean approach,
-summary of my OLAP practice with Northwind data set (Access)
Driving Digital Transformation with Machine Learning in Oracle AnalyticsPerficient, Inc.
The adoption of machine learning (ML) is increasing at near-breakneck speed. As organizations seek innovative ideas on how to improve the business, Oracle Analytics Cloud with ML capabilities is leading the charge. With built-in drag-and-drop functions into visualizations and autonomous prediction execution, Oracle Analytics puts the power of machine learning in your hands.
We covered how Oracle Analytics can connect various data sources, allow you to apply ML without being statistically savvy, and easily build your story in presentation format.
Discussion included:
-In-depth look at Oracle Analytics Cloud
-How to connect different data sources like SaaS applications, data lakes, external data sources and more
-Custom-trained ML models demonstration
-Real-world business use case from end to end
In his Data Management Workshop at the 8th ETOT Summit in London, October 2016 - DataGenic's CTO Colin Hartley looked at trends and best practice when it comes to commodity data management. As well as sharing the dos and don'ts of forward curve creation and management.
Securing data and preventing data breachesMariaDB plc
Today’s IT infrastructure is vulnerable to a wide range of attacks and threats from sources both outside and inside your organization. In this session we’ll share MariaDB Server and MaxScale security best practices so you can safeguard your MariaDB infrastructure.
These slides will help in understanding what is Data warehouse? why we need it? DWh architecture, OLAP, Metadata, Data Mart, Schemas for multidimensional data, partitioning of data warehouse
Maximizing performance via tuning and optimizationMariaDB plc
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Data Warehousing and Business Intelligence is one of the hottest skills today, and is the cornerstone for reporting, data science, and analytics. This course teaches the fundamentals with examples plus a project to fully illustrate the concepts.
Securing data and preventing data breachesMariaDB plc
Today’s IT infrastructure is vulnerable to a wide range of attacks and threats from sources both outside and inside your organization. In this session we’ll share MariaDB Server and MaxScale security best practices so you can safeguard your MariaDB infrastructure.
These slides will help in understanding what is Data warehouse? why we need it? DWh architecture, OLAP, Metadata, Data Mart, Schemas for multidimensional data, partitioning of data warehouse
Maximizing performance via tuning and optimizationMariaDB plc
Ensuring that your end users get the performance they expect from your system requires an organized approach to performance management. This session will cover the planning and measurement necessary to ensure satisfied customers, and will also include tips and tricks learned from MariaDB’s years of supporting many of the most demanding installations in the world.
Data Warehousing and Business Intelligence is one of the hottest skills today, and is the cornerstone for reporting, data science, and analytics. This course teaches the fundamentals with examples plus a project to fully illustrate the concepts.
The seminar is about Data warehousing, in here we are gonna discuss about what is data warehousing, comparison b/w database and data warehouse, different data warehouse models.about Data mart, and disadvantages of data warehousing.
[db tech showcase Tokyo 2017] C37: MariaDB ColumnStore analytics engine : use...Insight Technology, Inc.
MariaDB ColumnStore is the analytics engine for MariaDB. This talk will introduce the product, use cases, and also introduce the new features coming in the next major release 1.1.
Types of database processing,OLTP VS Data Warehouses(OLAP), Subject-oriented
Integrated
Time-variant
Non-volatile,
Functionalities of Data Warehouse,Roll-Up(Consolidation),
Drill-down,
Slicing,
Dicing,
Pivot,
KDD Process,Application of Data Mining
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• What is a Data Warehouse
• Data Warehouse Approaches
• Why Invest in a Data Warehouse
• Getting Started
• BI Models
• BI Solutions
Choosing the Right Business Intelligence Tools for Your Data and Architectura...Victor Holman
Watch video presentation and get a FREE performance management kit at
http://www.lifecycle-performance-pros.com
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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.
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.
Large Language Models and the End of ProgrammingMatt Welsh
Talk by Matt Welsh at Craft Conference 2024 on the impact that Large Language Models will have on the future of software development. In this talk, I discuss the ways in which LLMs will impact the software industry, from replacing human software developers with AI, to replacing conventional software with models that perform reasoning, computation, and problem-solving.
In software engineering, the right architecture is essential for robust, scalable platforms. Wix has undergone a pivotal shift from event sourcing to a CRUD-based model for its microservices. This talk will chart the course of this pivotal journey.
Event sourcing, which records state changes as immutable events, provided robust auditing and "time travel" debugging for Wix Stores' microservices. Despite its benefits, the complexity it introduced in state management slowed development. Wix responded by adopting a simpler, unified CRUD model. This talk will explore the challenges of event sourcing and the advantages of Wix's new "CRUD on steroids" approach, which streamlines API integration and domain event management while preserving data integrity and system resilience.
Participants will gain valuable insights into Wix's strategies for ensuring atomicity in database updates and event production, as well as caching, materialization, and performance optimization techniques within a distributed system.
Join us to discover how Wix has mastered the art of balancing simplicity and extensibility, and learn how the re-adoption of the modest CRUD has turbocharged their development velocity, resilience, and scalability in a high-growth environment.
Why React Native as a Strategic Advantage for Startup Innovation.pdfayushiqss
Do you know that React Native is being increasingly adopted by startups as well as big companies in the mobile app development industry? Big names like Facebook, Instagram, and Pinterest have already integrated this robust open-source framework.
In fact, according to a report by Statista, the number of React Native developers has been steadily increasing over the years, reaching an estimated 1.9 million by the end of 2024. This means that the demand for this framework in the job market has been growing making it a valuable skill.
But what makes React Native so popular for mobile application development? It offers excellent cross-platform capabilities among other benefits. This way, with React Native, developers can write code once and run it on both iOS and Android devices thus saving time and resources leading to shorter development cycles hence faster time-to-market for your app.
Let’s take the example of a startup, which wanted to release their app on both iOS and Android at once. Through the use of React Native they managed to create an app and bring it into the market within a very short period. This helped them gain an advantage over their competitors because they had access to a large user base who were able to generate revenue quickly for them.
OpenFOAM solver for Helmholtz equation, helmholtzFoam / helmholtzBubbleFoamtakuyayamamoto1800
In this slide, we show the simulation example and the way to compile this solver.
In this solver, the Helmholtz equation can be solved by helmholtzFoam. Also, the Helmholtz equation with uniformly dispersed bubbles can be simulated by helmholtzBubbleFoam.
Enhancing Research Orchestration Capabilities at ORNL.pdfGlobus
Cross-facility research orchestration comes with ever-changing constraints regarding the availability and suitability of various compute and data resources. In short, a flexible data and processing fabric is needed to enable the dynamic redirection of data and compute tasks throughout the lifecycle of an experiment. In this talk, we illustrate how we easily leveraged Globus services to instrument the ACE research testbed at the Oak Ridge Leadership Computing Facility with flexible data and task orchestration capabilities.
Paketo Buildpacks : la meilleure façon de construire des images OCI? DevopsDa...Anthony Dahanne
Les Buildpacks existent depuis plus de 10 ans ! D’abord, ils étaient utilisés pour détecter et construire une application avant de la déployer sur certains PaaS. Ensuite, nous avons pu créer des images Docker (OCI) avec leur dernière génération, les Cloud Native Buildpacks (CNCF en incubation). Sont-ils une bonne alternative au Dockerfile ? Que sont les buildpacks Paketo ? Quelles communautés les soutiennent et comment ?
Venez le découvrir lors de cette session ignite
Field Employee Tracking System| MiTrack App| Best Employee Tracking Solution|...informapgpstrackings
Keep tabs on your field staff effortlessly with Informap Technology Centre LLC. Real-time tracking, task assignment, and smart features for efficient management. Request a live demo today!
For more details, visit us : https://informapuae.com/field-staff-tracking/
Check out the webinar slides to learn more about how XfilesPro transforms Salesforce document management by leveraging its world-class applications. For more details, please connect with sales@xfilespro.com
If you want to watch the on-demand webinar, please click here: https://www.xfilespro.com/webinars/salesforce-document-management-2-0-smarter-faster-better/
Into the Box Keynote Day 2: Unveiling amazing updates and announcements for modern CFML developers! Get ready for exciting releases and updates on Ortus tools and products. Stay tuned for cutting-edge innovations designed to boost your productivity.
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Modern design is crucial in today's digital environment, and this is especially true for SharePoint intranets. The design of these digital hubs is critical to user engagement and productivity enhancement. They are the cornerstone of internal collaboration and interaction within enterprises.
Listen to the keynote address and hear about the latest developments from Rachana Ananthakrishnan and Ian Foster who review the updates to the Globus Platform and Service, and the relevance of Globus to the scientific community as an automation platform to accelerate scientific discovery.
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Designing for Privacy in Amazon Web ServicesKrzysztofKkol1
Data privacy is one of the most critical issues that businesses face. This presentation shares insights on the principles and best practices for ensuring the resilience and security of your workload.
Drawing on a real-life project from the HR industry, the various challenges will be demonstrated: data protection, self-healing, business continuity, security, and transparency of data processing. This systematized approach allowed to create a secure AWS cloud infrastructure that not only met strict compliance rules but also exceeded the client's expectations.
Gamify Your Mind; The Secret Sauce to Delivering Success, Continuously Improv...Shahin Sheidaei
Games are powerful teaching tools, fostering hands-on engagement and fun. But they require careful consideration to succeed. Join me to explore factors in running and selecting games, ensuring they serve as effective teaching tools. Learn to maintain focus on learning objectives while playing, and how to measure the ROI of gaming in education. Discover strategies for pitching gaming to leadership. This session offers insights, tips, and examples for coaches, team leads, and enterprise leaders seeking to teach from simple to complex concepts.
Multiple Your Crypto Portfolio with the Innovative Features of Advanced Crypt...Hivelance Technology
Cryptocurrency trading bots are computer programs designed to automate buying, selling, and managing cryptocurrency transactions. These bots utilize advanced algorithms and machine learning techniques to analyze market data, identify trading opportunities, and execute trades on behalf of their users. By automating the decision-making process, crypto trading bots can react to market changes faster than human traders
Hivelance, a leading provider of cryptocurrency trading bot development services, stands out as the premier choice for crypto traders and developers. Hivelance boasts a team of seasoned cryptocurrency experts and software engineers who deeply understand the crypto market and the latest trends in automated trading, Hivelance leverages the latest technologies and tools in the industry, including advanced AI and machine learning algorithms, to create highly efficient and adaptable crypto trading bots
2. Agenda
• The Task - Analytics – Why and what
• The Requirements – What do we need for analytics
• The Solution – Column Based Storage
• The Product – MariaDB AX and MariaDB ColumnStore
• The Uses – MariaDB ColumnStore in action
7. Diagnostics: Why did it happen
• Aggregates: aggregate measure over one or
more dimension
– Find total sales
– Top five product ranked by sales
• Roll-ups: Aggregate at different levels of
dimension hierarchy
– given total sales by city, roll-up to get sales by
state
• Drill-down: Inverse of roll-ups
– given total sales by state, drill-down to get
total by city
• Slicing and Dicing:
– Equality and range selections on one or more
dimensions
8. Predictive: What is likely to happen
• Sales Prediction
– Analyze data to identify trends, spot
weakness or determine conditions
among broader data sets for making
decisions about the future
• Targeted marketing
– what is likelihood of a customer buying
a particular product based on past
buying behavior
10. Prescriptive: What is the best course of action?
Paradox of choices
With too many choices, which one is the best?
11. Big Data Analytics Use Cases
By industry
Finance
Identify trade patterns
Detect fraud and anomolies
Predict trading outcomes
Manufacturing
Simulations to improve design/yield
Detect production anomolies
Predict machine failures (sensor data)
Telecom
Behavioral analysis of customer calls
Network analysis (perf and reliability)
Healthcare
Find genetic profiles/matches
Analyze health vs spending
Predict viral oubreaks
13. What is an OLTP workload?
• OLTP applications are represents the most common database workload
• OLTP applications has a read / write ratio of maybe 50/50
– Web apps / E-commerce has more reads, ending with maybe 90/10
• OLTP applications deals with data on a row by row level
– Customer data, product data, order items etc.
– Single rows are selected, inserted, updated and deleted, one by one or in small groups
• OLTP data structures is somewhat of a representation of the business or the
applications that manage the data
– An order reference a customer, and order item is linked to an order
– Typically 3rd normal form or higher
– Sometimes individual aspects break the normal form, for performance reasons
• Transactions and ACID properties are required
14. The analytics workload
• Deals with data from a high level perspective
• Handles data in large groups of rows
– SELECTs data by date, customer location, product id etc.
– Data is loaded in batch or streamed in
– Data is mostly just INSERTed
• Dealing with individual data items is usually ineffective
• Data structures are optimized for analytics use and performance
• Data is sometimes purged, but just as often not
• Contains structured, semi-structured and sometimes unstructured data
• Data often comes from many different sources, internal and external
• Queries are ad-hoc, largely
• Transactions and ACID requirements are relaxed
15. Analytics database requirements
• Fast access to large amounts of data
• Scalable as data grows over time
– Analytics requirements increasing
– Regulatory requirements
– New data sources are added
• Load performance must be fast, scalable and predictable
• Data loading should be very flexible due to the different sources of data
– Some data loaded in batch, other is streamed
• Query performance also need to be scalable
• Data compression is a requirement
– Data size constraints, as well as read performance from disk
16. B-tree indexes
The good
B-tree indexes
The bad
• Well known technology
• Works with most types of data
• Scales reasonably well
• Really good for OLTP
transactional data
• Really bad for unbalanced data
• Index modifications can be really
slow
• Index modifications are largely single
threaded
• Slows down with the amount of data
• Really not scalable with large
amount of data
17. In summary, what do we need
• Something that can compress data A LOT
• Something that can be written to with fast and predictable performance
• Something that doesn't necessarily support transactions
– It doesn't hurt, but performance is so much more important
• Something that can support analytics queries
– Ad-hoc queries
– Aggregate queries
• Something that can scale as data grows
• Something that can still have a level of high availability
• Something that works with analytics tools, like Tableau, R etc.
19. Existing Approaches
Limited real time analytics
Slow releases of product innovation
Expensive hardware and software
Data Warehouses
Hadoop / NoSQL
LIMITED SQL
SUPPORT
DIFFICULT TO
INSTALL/MANAGE
LIMITED TALENT POOL
DATA LAKE W/ NO DATA
MANAGEMENT
Hard to use
20. To the rescue – Column Based Storage
• Data is stored column by column
• Each column is stored in one or more extents
– Each extent is represented by 1 file
• Each extent is arranged in fixed size blocks
• Extents are compressed (using Snappy)
• Data is one of
– Fixed size (1, 2, 4 or 8 bytes)
– Dictionary based with a fixed size pointer
• Meta data is in an extent map
– Extent map is in memory
– Extent map contains meta data on each
extent, like min and max values
Table
Column1 Column N
Extent 1
(8MB~64MB
8 million rows)
Extent N
(8MB~64MB
8 million rows)
21. To the rescue – Distributed data processing
• Clients connect to a User Module
• The User Module optimizes and
controls the execution
• Data is distributed among the
Performance Modules
• Data is stored, processed and
managed by Performance Modules
• Performance Modules process
query primitives in parallel
• The User Module combines the
results from the Performance
Modules
User Modules
Performance
Module 1 ... Performance
Module N
Performance
Module 2
Performance
Module 3
Clients
User Connections
23. MariaDB ColumnStore
High performance columnar storage engine that supports a wide variety
of analytical use cases in highly scalable distributed environments
Parallel query
processing for distributed
environments
Faster, More
Efficient Queries
Single Interface for
OLTP and analytics
Easy to Manage and
Scale
Easier Enterprise
Analytics
Power of SQL and
Freedom of Open
Source to Big Data
Analytics
Better Price
Performance
24. MariaDB AX
MariaDB Server
MariaDB MaxScale
MariaDB ColumnStore
Parallel queries
Distributed storage
No indexes
Automatic partitioning
Read optimized
High compression
Low disk IO
ColumnStore
Storage
ColumnStore
Storage
ColumnStore
Storage
MariaDB Server
ColumnStore
MariaDB Server
ColumnStore
MariaDB MaxScale
MariaDB Server
ColumnStore
ColumnStore
Storage
MariaDB MaxScale
25. Easier Enterprise
Analytics
ANSI SQL
Single SQL Front-end
• Use a single SQL interface for analytics and OLTP
• Leverage MariaDB Security features - Encryption for
data in motion , role based access and auditability
Full ANSI SQL
• No more SQL “like” query
• Support complex join, aggregation and window
function
Easy to manage and scale
• Eliminate needs for indexes and views
• Automated horizontal/vertical partitioning
• Linear scalable by adding new nodes as data grows
• Out of box connection with BI tools
26. Faster, More
Efficient Queries
Optimized for Columnar storage
• Columnar storage reduces disk I/O
• Blazing fast read-intensive workload
• Ultra fast data import
Parallel distributed query execution
• Distributed queries into series of parallel operations
• Fully parallel high speed data ingestion
Highly available analytic environment
• Built-in Redundancy
• Automatic fail-over
Parallel
Query Processing
28. Healthcare / Life Science Industry
Genome analysis
• In-depth genome research for the dairy industry to improve production of milk and protein.
• Fast data load for large amount of genome dataset (DNA data for 7billion cows in US - 20GB per load)
Healthcare spending analysis
• Analyze 3TB of US health care spending for 155 conditions with 7 years of historical data
• Used sankey diagram, treemap, and pyramid chart to analyze trends by age, sex, type of care, and condition
Why MariaDB ColumnStore
• Strong security features including role based data access and audit plug in
• MPP architecture handles analytics on big data with high speed
• Easy to analyze archived data with SQL based analytics
• Does not require DBA to index or partition data
29. Telecommunication Industry
Customer behavior analysis
• Analyze call data record to segment customers based on their behavior
• Data-driven analysis for customer satisfaction
• Create behavioral based upsell or cross-sell opportunity
Call data analysis
• Data size: 6TB
• Ingest 1.5 million rows of logs per day with 30million texts and 3million calls
• Call and network quality analysis
• Provide higher quality customer services based on data
Why MariaDB ColumnStore
• ColumnStore support time based partitioning and time-series analysis
• Fast data load for real-time analytics
• MPP architecture handles analytics on big data with high speed
• Easy to analyze the archived data with SQL based analytics
30. In Conclusion
• Analytics require a different technology to be able to cope with
– Different types of data
– Different types of data access
• OLTP databases has different requirements compared to Analytics
• Column Based storage allows high compression
• Metadata can replace indexing
• Distributed processing allows for performance and scalability
• MariaDB ColumnStore implement a fast an efficient distributed database for
analytics
• MariaDB AX is the subscription for professional use of MariaDB ColumnStore
• MariaDB ColumnStore is gaining wide acceptance