Name: How to Handle Mixed Workloads with Fewer Databases
Start: 2022-04-19T17:00:00Z
End: 2022-04-19T17:00:41.000Z
Location: BrightTALK
Rating: 0

MariaDB frees companies from the costs, constraints and complexity of proprietary databases, enabling them to reinvest in what matters most – rapidly developing innovative, customer-facing applications. Deployed in minutes, MariaDB uses pluggable, purpose-built storage engines for transactional, analytical or hybrid use cases that previously required specialized databases. Enterprises can now depend on a single complete database for all their needs, whether on commodity hardware or their cloud of choice. MariaDB is trusted by organizations such as Deutsche Bank, DBS Bank, Nasdaq, Red Hat, ServiceNow,  Verizon and Walgreens. Real business relies on MariaDB™

Amazon Aurora looks like a fantastic deal at first glance: a scalable database in the cloud provided by the world’s leading cloud provider. It has a unique approach of moving much of what databases usually do to the storage layer. However, it also has some shocking limitations when you look under the covers, including write scalability, consistency and cost.

MariaDB SkySQL, on the other hand, includes high availability features, multiple database topologies, better read and write scaling, and an architectural cost advantage in terms of lower IOPS. Moreover, by becoming a MariaDB customer, you support the continued development of open source database technology.

In this presentation, you will learn:
• The basics of how Aurora works, and what is included and not included
• Aurora's limitations
• SkySQL's features and offerings
• Exactly how MariaDB SkySQL and Amazon Aurora compare in terms of features, price and capabilities

Amazon Aurora vs. MariaDB SkySQL: The Best AWS-Hosted DBaaS

Beyond the hype, what is distributed SQL? And how does it work? Where should you use it, and where should you not? What are the benefits and drawbacks? Join this webinar for straightforward, factual answers to these questions. While we will use the specific distributed SQL implementation in MariaDB Xpand as an example, the concepts apply universally.

In this webinar, we will cover:
• What makes distributed SQL different from traditional relational database technology
• How distributed SQL works, including group membership, data, and query distribution
• Achieving fault tolerance and high availability with distributed SQL
• Distributed SQL's approach to elastic scalability
• Infrastructure requirements for distributed SQL

What is Distributed SQL?

It's probably not news to you, but handling the problem of data scalability – really handling it – hasn't been a trivial feat. You may have tried everything from buying more powerful hardware to creating overly complex and brittle data partitioning methods to using entirely new data storage and access models. These approaches may have helped in some regards, but they also came with some pretty big trade-offs.

Enter distributed SQL.

Over the years a lot of things have been deemed a “game-changer” – but distributed SQL actually deserves the moniker. Distributed SQL databases are relational databases engineered with a distributed architecture to scale out without any trade-offs. These databases retain standard SQL, ACID transactions and strong consistency while adding unprecedented levels of scalability. But that's only the tip of the iceberg.

In this session, you'll:
• Gain an understanding of the fundamental concepts of distributed SQL
• Learn how MariaDB's distributed SQL implementation, Xpand, takes database elasticity, scale and high availability to the next level
• Discover how you can use columnar indexing with distributed SQL to dramatically improve the execution speed of analytical queries on massive datasets

How to Manage Operational & Analytical Data at Massive Scale

Cloud architects have a lot to think about. Today’s cloud services require 24x7x365 uptime while changing rapidly. They have to meet usage spikes while controlling costs. Meanwhile, everyone from thieves to government-sponsored black hats are trying to exploit every weakness. Having a well-defined list of requirements as you evaluate cloud services will help you keep everything straight and ensure your applications run smoothly and safely.  Watch this webinar to learn: • The major concerns of modern cloud architecture • How your data architecture affects performance and resiliency • How to evaluate a service’s fault tolerance, security, scalability and maintainability

Cloud Architect Checklist: Evaluating Infrastructure, Architecture & Software

When moving your relational database to the cloud, Amazon Relational Database Service (RDS) seems to be a simple choice. It automates a lot of the work of a DBA, supports familiar SQL dialects such as MariaDB, Oracle, PostgreSQL or MySQL, and provides read scaling. RDS even provides some degree of failover, so it might be a good solution for applications that are not growing and do not require hyper-scale.

However, many applications require mixed workload support where some queries are transactional, and some are analytical or long-running. Often these applications require higher levels of scale and cannot accept a 60-120s failover time. Additionally, many organizations are pursuing a multi-cloud strategy. If any of these apply to you, you'll need to look beyond RDS. MariaDB SkySQL was built for these scenarios.

By the end of this webinar, you'll know:
• The benefits, capabilities and features of both RDS and SkySQL
• The situations in which SkySQL might not be appropriate
• When SkySQL is the best choice for meeting business or technical requirements

When Should You Use MariaDB SkySQL Instead of Amazon RDS?

The first release of MariaDB was a fork of MySQL with additional features, performance improvements and bug fixes. However, while they both implement the MySQL protocol, MariaDB and MySQL have evolved differently – becoming separate databases.

MariaDB has grown into a complete database solution with distributed SQL, columnar storage and temporal tables while MySQL 8.0 was a significant release with its transactional data dictionary and improved JSON support.

In this webinar, we will discuss:
-  The similarities between MariaDB and MySQL
-  Where MariaDB and MYSQL are compatible and where they are not
-  The differentiating features and advantages
-  How you can migrate from MySQL to MariaDB

MariaDB vs. MySQL – What's the Difference, and Why Does it Matter?

Handling a few hundred or even a few thousand transactions per second is rather easy for traditional database deployment. And scaling up will get you beyond 10,000 transactions per second. But what happens when throughput continues to rise and latency with it?

Sharding is an option, but it can be difficult. Perhaps a different type of database is needed. NoSQL databases can provide scalability but at the cost of transactions and data integrity. There is distributed SQL, but databases like Google Spanner have their own limitations.

So, how can you start small and incrementally reach a massive scale without changing databases and modifying applications? In this webinar, we're exploring all options for scaling MariaDB from hundreds to millions of transactions per second.

Watch this webcast to:
  • Learn how to optimize your database for different types of workloads
  • Discover the different ways to scale out (vs. scaling up)
  • See how to scale from a single node to hundreds

A Guide to Scaling Databases: Supporting High-Throughput, Low-Latency Workloads

By extending relational data with JSON documents, you get schema flexibility without sacrificing transactions or data integrity. You can index and query data regardless of whether it is structured, semi-structured or both.

In this webinar, you'll learn how you can take advantage of structured AND semi-structured data using hybrid data models. We'll explore the breadth of JSON functionality you can use to query, modify, enforce data integrity and so much more.

Finally, we'll jump into a live coding demonstration where you'll get a firsthand look at how you can tap into the JSON capabilities within MariaDB.

Watch this webinar to learn:
• How to model structured and semi-structured data
• How to index and query JSON documents with SQL
• How to enforce the data integrity of JSON documents

How to Use Hybrid Data Models: JavaScript Object Notation

Amazon Aurora looks like a fantastic deal at first glance: a scalable database in the cloud provided by the world’s leading cloud provider. It has a unique approach of moving much of what databases usually do to the storage layer. However, it also has some shocking limitations when you look under the covers, including write scalability, consistency and cost.

MariaDB SkySQL, on the other hand, includes high availability features, multiple database topologies, better read and write scaling, and an architectural cost advantage in terms of lower IOPS. Moreover, by becoming a MariaDB customer you support the continued development of open source database technology.

In this presentation, you will learn:
• The basics of how Aurora works, and what is included and not included
• Aurora's limitations
• SkySQL's features and offerings
• Exactly how MariaDB SkySQL and Amazon Aurora compare in terms of features, price and capabilities

Amazon Aurora vs. MariaDB SkySQL: Best AWS-Hosted DBaaS

Distributed SQL databases are among the fastest-growing, most exciting new data and cloud computing technologies. For years IT organizations have relied on expensive, proprietary client-server relational databases based on 40-year-old technology. While client-server databases have been dependable, they were not designed for cloud computing or modern scale and availability requirements. 
 
However, not all distributed SQL databases are equal. Vendor claims often do not match the production experience. How do you evaluate whether a distributed SQL database is the right solution and sort out which one suits your needs?

In this presentation, you will learn:
• What differentiates distributed SQL databases from other database types
• The application types and use cases that are appropriate for distributed SQL
• Features and characteristics to consider in evaluating a distributed SQL database
• How to design a solution evaluation and proof of concept (POC)

How to Evaluate a Distributed SQL Database

The pandemic forced everyone to shop almost exclusively online. For Whitebox, an e-commerce technology company, they saw more and more people turn to their customers and their platform to deliver essential goods. Due to the company's rapid growth and growing data needs, Whitebox needed a new cloud database to help them reach new heights.

After considering several cloud database options, Whitebox chose to migrate to MariaDB SkySQL on Google Cloud Platform for its scalability and access to the most knowledgeable DBAs and database engineers in the industry. With SkySQL and SkyDBAs, Whitebox saw its brands achieve record sales over the holidays, ending the year with a 700% increase in orders processed year over year. 

Join this webinar to hear from Sean Clark, Whitebox CTO, who drove the evaluation and migration personally, and Manjot Singh, Head of SkyServices at MariaDB, where they will cover:

• Technical improvements needed for Whitebox to scale as fast as possible to meet a fast-growing customer base with increasing data needs
• How Whitebox migrated to SkySQL, why they decided to take a crazy jump right before Black Friday, and how the MariaDB SkyDBA team helped
• What’s next for Whitebox as they move forward with MariaDB

e-Commerce and Cloud Migration: Responding to Data Growth

Applications are not strictly transactional or analytical, OLTP or OLAP. And today’s data is not strictly structured or unstructured, but often a combination of both. Everyday applications need to do a lot of short reads and writes but also answer questions like, “How many of each widget type did we sell?” So, how can you write an application that handles mixed workloads without a messy, cumbersome architecture?

In this webinar, we will look at:
• The current state of the art in database architecture
• Techniques to deal with “long queries”
• How to handle JSON data
• Data consistency factors
• Up-and-coming technology

How to Handle Mixed Workloads with Fewer Databases

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How to Handle Mixed Workloads with Fewer Databases

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