March 26, 2024: Today, Canonical announced the release of Charmed MongoDB, an enterprise solution for MongoDB® that comes with advanced automation features, multi-cloud capabilities and comprehensive support. 

MongoDB® is one of the most widely used databases worldwide. It provides powerful  capabilities for scaling, consistency and fault tolerance , making it a popular choice for organisations of all sizes and in various industries. Charmed MongoDB is an enterprise drop-in replacement for the MongoDB® Community version with the advanced features organisations need in their production environment.

“As part of our open source data solution portfolio, Charmed MongoDB is designed to meet the demands of modern deployments”, said Cedric Gegout, VP of Product at Canonical. “Organisations can deploy Charmed MongoDB with confidence, knowing they are backed by Canonical’s commitment to performance in any cloud environment, alongside 10 years of support  and security maintenance.”

Hyper-automated MongoDB^®, available on any cloud

The Charmed MongoDB operator deploys and runs MongoDB® on physical, virtual machines (VM) and other cloud and cloud-like environments, including AWS, Azure, OpenStack and VMWare.

The solution comes with automation features that simplify the deployment, scaling, design, and management of MongoDB®, ensuring reliability. In addition to these capabilities, Charmed MongoDB offers enterprise-level features such as high availability, sharding, audit logging, backup and restore, user management, and Transport Layer Security (TLS). 

Secured and supported for 10 years

For organisations looking for fast security patching against Common Vulnerabilities and Exposures (CVEs), Charmed MongoDB offers comprehensive security maintenance. Canonical’s Charmed MongoDB offers a cost-effective, subscription model that includes 10 years of security maintenance and 24/7 support, providing the stability and peace of mind necessary for organisations to run MongoDB® in production. 

Simple pricing per node

Charmed MongoDB is part of Canonical’s data solutions portfolio. Customers purchase 24/7 or weekday enterprise support on a per-node basis through the Ubuntu Pro + Support plan, which covers  all applications within the portfolio, including Charmed Kafka and Charmed Spark as well as solutions for AI offered by Canonical such as Charmed Kubeflow and Charmed MLFlow.

This convenient subscription per node and lack of software licence fees makes Canonical’s offering compelling for organisations looking to run database solutions like MongoDB® with more control over their TCO. Budgeting and financial planning are straightforward and predictable.

Get started with Charmed MongoDB

To get started with Charmed MongoDB, users can refer to the documentation available at Charmhub. For more information about Charmed MongoDB, visit canonical.com/data/mongodb.

Canonical is also delighted to offer Charmed MongoDB training in collaboration with Cloudbase Solutions. This program is designed to help individuals get started with Charmed MongoDB through in-person or virtual training.

Additional resources

Webinar: MongoDB® for Modern Data Management

Whitepaper: MongoDB® Security and Support

Whitepaper: MongoDB® for enterprise data management

Learn more about Charmed MongoDB Managed Service

Learn more about Data Solutions Advisory at Canonical

Trademark Notice

“MongoDB” is a trademark or registered trademark of MongoDB Inc. Other trademarks are property of their respective owners. Charmed MongoDB is not sponsored, endorsed, or affiliated with MongoDB, Inc.

Canonical is delighted to be a technology partner at the Data Innovation Summit (DIS) in 2024. We are proud to showcase our Data and AI solutions through our conference talk and technology in practice sessions. The event will take place in Kistamässan, Stockholm on April 24-25, 2024. Visit us at booth C71 to learn how open source data and AI solutions can help you take your models to production, from edge to cloud.

Data and AI: get first-hand insights from Canonical experts

The modern enterprise can use AI algorithms and models to learn from their treasure troves of big data, and make predictions or decisions based on the data without being explicitly programmed to do so. What’s more, the AI models grow more accurate over time. 

The magic is in the melding of AI and big data. Data of incredible volume, velocity, and variety is fed into the AI engine, making the AI smarter. Over time, less human intervention is needed for the AI to run properly; in time, the AI can deliver deeper insights—and strategic value—from the ever-increasing pools of data, often in real time. 

In today’s competitive business environment, your AI and data strategies need to be more interconnected than ever. According to an MIT Technology Review survey, 78% of CIOs say that scaling AI to create business value is the top priority of their enterprise data strategy, and 96% of AI leaders agree. Nearly three out of four CIOs also say that data challenges are the biggest factor jeopardising AI success.

The Data Innovation Summit is a significant event in the field of Data and AI, especially in the Nordics. It brings together professionals, enterprise practitioners, technology providers, start-up innovators, and academics working with data and AI. We at Canonical are delighted to announce that we will be participating in this event and sharing our expertise in Data and AI.

Canonical is a well-known publisher of Ubuntu, which is the preferred operating system (OS) for data scientists. In addition to the OS, Canonical offers an integrated data and AI stack. We provide the most cost-effective options to help you gain control over your Total Cost of Ownership (TCO), and ensure reliable security maintenance, allowing you to innovate at a faster pace.

Canonical DIS talk: open source DataOps and MLOps

Canonical data and AI Product Managers, and Andreea Munteanu and Michelle Anne Tabirao will be speaking about open source for your DataOps and MLOps.

Talk description

Open source data and AI tools enable organisations to create a comprehensive solution that covers all stages of the data and machine learning lifecycle. This includes correlating data from various sources, regardless of their collection engine, and serving the model in production. Together, DataOps and MLOps drive the collaboration, communication, and integration that great data and AI teams need, making them essential to the model lifecycle. DataOps is an approach to data management that focuses on collaboration, communication, and integration among data engineers, data scientists, and other data-related roles to improve the efficiency and effectiveness of data processes. MLOps is a set of practices that combines machine learning, software development, and operations to enable the deployment, monitoring, and maintenance of machine learning models in production environments.

In this talk, we will explore how to build an end-to-end solution for DataOps and MLOps using open-source solutions like databases, ML and analytics tools such as OpenSearch, Kubeflow, and MLFlow. Professionals can focus on building ML models without spending time on the tooling operational work. We will highlight some use cases, e.g. in the telco sector, where they use MLOps and DataOPs to optimise the telco network infrastructure and reduce power consumption.

Attendees will learn about the critical factors to consider when selecting tools and best practices needed for building a robust, production-grade ML project.

Come and meet us at DIS 2024

If you are interested in building or scaling your data and AI projects with open source solutions, we are here to help you. Visit our Data and AI offerings to explore our solutions.

Learn more about our Data and AI solutions

• Ubuntu AI podcast: dive into data and AI on the go
• Big data and AI WP: build a smarter enterprise with a secure, integrated open source stack
• MongoDB for enterprise data management WP: MongoDB benefits for modern enterprise data management, use cases for financial, telecommunications and automotive industries
• MLOps toolkit: from hardware to applications, discover the key factors to consider when building your machine learning toolkit
• Using PostgreSQL to power your AI applications: learn what PostgreSQL has to offer for your AI projects
• Canonical Data: enterprise data solutions for rapid innovation at any scale
• Canonical AI: take your models to production with open source AI

27 February 2024: Today, Canonical announced the release of Charmed Kafka – an advanced solution for Apache Kafka^® that provides everything users need to run Apache Kafka at scale.  

Apache Kafka is an event store that supports a range of contemporary applications including microservices architectures, streaming analytics and AI/ML use cases. Canonical Charmed Kafka simplifies deployment and operation of Kafka across public clouds and private data centres alike. The free, open source solution is offered with comprehensive support and security maintenance, so teams can work with complete peace of mind.

“With Charmed Kafka, enterprise operations teams benefit from an advanced automation and management solution that goes far beyond Kafka cluster deployment and configuration”, said Jon Seager, Vice President of Engineering at Canonical. “Charmed Kafka is one of a series of comprehensive, Canonical open source data solutions that has been engineered for reliable operation, delivered with up to ten years of support and maintenance.”

Reduced operational burden and up to 10 years of support

Modern cloud infrastructure brings tremendous flexibility and agility, but deploying, securing and operating open source software on the cloud remains challenging. Charmed Kafka includes a comprehensive management solution that covers the full Kafka cluster lifecycle from initial deployment through to ongoing operational support and monitoring, and includes a distribution of Apache Kafka maintained by Canonical. The entire solution is backed by the Ubuntu Pro + Support subscription – with up to 10 years of support available for select stable tracks.

Simple per node pricing

The release of Charmed Kafka marks one of a series of integrated data solutions in Canonical’s data solutions portfolio . Customers purchase 24/7 or weekday enterprise support on a per-node basis through the Ubuntu Pro + Support plan, which covers  all applications within the portfolio, including Charmed Kafka and Charmed Spark as well as additional solutions for AI offered by Canonical including Charmed Kubeflow and Charmed MLFlow.

Cloud-native portability for Kafka

Charmed Kafka can be deployed on cloud virtual machines (VMs) as well as on Kubernetes, which brings true cloud-native portability across clouds and on-premise data centres. The solution can be deployed to cloud infrastructure such as AWS, Azure, Google Cloud and OpenStack, as well as VMWare, CNCF-conformant Kubernetes and directly on physical systems via Canonical MAAS.

The initial release offers robust features for automated deployment, configuration, scaling, observability, automated upgrades and for securing the cluster.
The Charmed Kafka software operator solution enables administrators to rapidly deploy, configure and operate Kafka clusters on cloud infrastructure or Kubernetes using Juju – Canonical’s open source orchestration engine – either directly or through Terraform.

Try Charmed Kafka today

Users can get started with Charmed Kafka by following the documentation at ubuntu.com/data/docs. Learn more at canonical.com/data/kafka.

About Canonical

Canonical, the publisher of Ubuntu, provides open source security, support and services. Our portfolio covers critical systems, from the smallest devices to the largest clouds, from the kernel to containers, from databases to AI. With customers that include top tech brands, emerging startups, governments and home users, Canonical delivers trusted open source for everyone.

Learn more at https://canonical.com/ 

In the early 2010s, Apache Hadoop captured the imagination of the tech community. A free and powerful open source platform, it gave users a way to process unimaginably large quantities of data, and offered a dazzling variety of tooling to suit nearly every use case – MapReduce for odd jobs like processing of text, audio or video; Hive for SQL based data warehousing; Pig, an unusual language with a similar data warehousing goal; Hbase, Oozie, Sqoop, Flume and a whole parade of other tools for processing massive datasets at scale.

With the surge in interest in Hadoop, a number of startups and established software companies jumped to provide commercial offerings of it. Cloudera was one of the first into the game with CDH – a Hadoop distribution. Initially offered as Deb packages and RPMs, Cloudera quickly introduced Cloudera Manager, a sophisticated, web-based management system to deploy, maintain, and operate Hadoop clusters. With the introduction of Cloudera Manager, Cloudera established themselves as the market leader. Over time, they consolidated their position, merging with competitor HortonWorks.

A lot has changed in the nearly two decades since Hadoop’s release

Hadoop was designed and built as a hyperscale system to be deployed on premise in the data centre, well before public cloud computing had established its dominance and become arguably the most popular way to deliver IT services.

Certain design assumptions taken with Hadoop, like the paradigm “bring the compute to the data”, made sense in the context of a millennial data centre, with 1GbE networking and a relatively low cost per GB for direct attached storage media. But many of those design assumptions make little sense on the cloud, where local block storage devices are costly versus remote, highly durable object storage – which is offered with a far lower cost per GB.

Then there were parts of Hadoop’s critical architecture – for instance YARN and Kerberos –  which over time proved to be difficult to work with. YARN is a complex cluster job scheduler with a narrow focus on data processing, whilst the Kerberos security protocol used by Hadoop has long been a bugbear for many administrators.

In short: Hadoop was never designed with cloud computing in mind.

Between the ageing architecture and the complexity of the platform, many are looking to make a move away from Hadoop and from Cloudera, and seek a state-of-the-art alternative, more aligned with modern cloud-native computing principles and optimised for low cost operation in the contemporary cloud context.

Cloudera migration alternatives

When architects are looking for alternative data hub platforms, they tend to seek a solution that’s free, open source, powerful, and flexible. Like Hadoop, it should be capable of processing extremely large quantities of data, and give them flexibility in features to suit a wide array of use cases. But these days, the solution needs to be cloud ready, capable of auto scaling, and must run efficiently with a low operational cost.

Charmed Spark from Canonical is founded on Apache Spark, a mature and sophisticated data processing framework widely used with Hadoop. Spark supports data engineering, data lakehouse, and data science use cases for AI/ML and has been widely adopted by the Big Data user community. Charmed Spark entirely replaces Hadoop YARN with the more general purpose, extensible cluster resource manager  – Kubernetes. Kubernetes has become by far the most popular cluster resource manager on the market today, with a flexible palette of features and plugins.

How to move legacy workloads off Hadoop

You’re leaving 2010 behind, but you aren’t giving up its data – nor its processing logic. You need to make sure that your chosen migration plan makes it relatively straightforward to bring all of it with you. In our experience, this has been a major concern for clients planning a data hub migration to a modern, cloud-native infrastructure, and it’s why Charmed Spark is designed to offer a straightforward path from legacy Hadoop to a state-of-the-art, cloud-native data platform.

Charmed Spark includes a distribution of Apache Spark – the most popular of the Hadoop data processing frameworks – designed and built to run on Kubernetes. It offers an effective replacement for Hadoop, as its architecture entirely supersedes YARN and abstracts away the data storage tier to cloud object storage. Legacy Hadoop workloads such as Hive-based SQL data processing applications can often be readily migrated to the Spark framework, which has a high degree of compatibility with Hive, simplifying the transition from Hadoop. Charmed Spark is also available as a fully integrated offer for the data centre, including the Ceph object storage system and an advanced Kubernetes distribution from Canonical, easing transition from legacy Hadoop still further.

How to maintain flexibility in your cloud-native data hub design

A modern data hub is nothing if it lacks connectivity to popular object storage systems on the cloud. Whether it’s AWS S3, Azure Data Lake Store, Google Cloud Storage, or API compatible clones, a flexible data hub needs to be able to access and use all of these systems. Charmed Spark has been built with this in mind.

Architects are also concerned with workload consolidation and efficiency and with this in mind, the solution offers support for the Volcano gang scheduler Kubernetes plugin, helping ensure maximum efficiency on the Kubernetes cluster.

Cloud lock-in remains a worry for many, and a key concern that a modern data hub architecture needs to address. Charmed Spark offers platform portability between clouds. The solution can be deployed to many popular cloud Kubernetes platforms, including AWS EKS and Google GKE.

Cloudera migration: how to migrate while keeping costs manageable

One of the biggest pressure points in a migration project is cost, and it’s often the deciding factor in new technology adoption, even beyond functionality. Like Hadoop, Charmed Spark ticks both boxes – cost and capability – as it’s free to deploy and use.

As with any other project, the cost concern is a long-term consideration: how do you keep your data hub secure, up to date, and protected, in a cost-effective manner? Charmed Spark offers long-term support and security maintenance commitments: the Charmed Spark solution is available with up to ten years of support per stable track – which includes security fixes and break/fix support with a choice of 24/7 or weekday SLA.

Users interested in learning more about Charmed Spark can contact our commercial team, access the product page or explore Canonical’s portfolio of data and AI solutions.

Further reading

• Charmed Spark reference architecture guide
• Charmed Spark product page
• Canonical data solutions portfolio page
• Canonical AI solutions portfolio page