/ By Paul Krill / 0 Comments

Microsoft has unveiled a preview of a C++-based vectorized query engine for the Azure Databricks cloud analytics and AI service based on Apache Spark. Azure Databricks, which is delivered in partnership with Databricks, introduced the Photon-powered Delta Engine September 22.

Written in C++ and compatible with Spark APIs, Photon is a vectorized query engine that leverages modern CPU architecture and the Delta Lake open source transactional storage layer to enhance Apache Spark 3.0 performance by as much as 20x. Microsoft said that as organizations embrace data-driven decision-making, it is now imperative for them to have a platform that can quickly analyze massive amounts and types of data.

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/ By David Linthicum / 0 Comments

Our ability to augment technology with artificial intelligence and machine learning does not seem to have limits. We now have AI-powered analytics, smart Internet of Things, AI at the edge, and of course AIops tools.

At their essence, AIops tools do smart automations. These include self-healing, proactive maintenance, even working with security and governance systems to coordinate actions, such as identifying a performance issue as a breach.

We need to consider discovery as well, or the capability of gathering data ongoing and leveraging that data to train the knowledge engine. This allows the knowledgebases to become savvier. Greater knowledge about how the systems under management behave or are likely to behave creates a better capability of predicting issues and being proactive around fixes and reporting. 

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/ By David Linthicum / 0 Comments

Our ability to augment technology with artificial intelligence and machine learning does not seem to have limits. We now have AI-powered analytics, smart Internet of Things, AI at the edge, and of course AIops tools.

At their essence, AIops tools do smart automations. These include self-healing, proactive maintenance, even working with security and governance systems to coordinate actions, such as identifying a performance issue as a breach.

We need to consider discovery as well, or the capability of gathering data ongoing and leveraging that data to train the knowledge engine. This allows the knowledgebases to become savvier. Greater knowledge about how the systems under management behave or are likely to behave creates a better capability of predicting issues and being proactive around fixes and reporting. 

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/ By Shane Johnson / 0 Comments

As information and processing needs have grown, pain points such as performance and resiliency have necessitated new solutions. Databases need to maintain ACID compliance and consistency, provide high availability and high performance, and handle massive workloads without becoming a drain on resources. Sharding has offered a solution, but for many companies sharding has reached its limits, due to its complexity and resource requirements. A better solution is distributed SQL.

In a distributed SQL implementation, the database is distributed across multiple physical systems, delivering transactions at a globally scalable level. MariaDB Platform X5, a major release that includes upgrades to every aspect of MariaDB Platform, provides distributed SQL and massive scalability through the addition of a new smart storage engine called Xpand. With a shared nothing architecture, fully distributed ACID transactions, and strong consistency, Xpand allows you to scale to millions of transactions per second.

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/ By Matt Asay / 0 Comments

The Snowflake IPO was a big deal, and not merely because of the company’s enormous valuation.

In 2013 Cloudera co-founder Mike Olson confidently (and accurately) declared “a stunning and irreversible trend in enterprise infrastructure.” That trend? “No dominant platform-level software infrastructure has emerged in the last 10 years in closed-source, proprietary form.” Snowflake, a cloud-based enterprise data platform, may spell the end of that run. 

Sure, we had Splunk, but Spunk squeaked through the hypothesis police before open source had found its feet, as Lightspeed partner Gaurav Gupta told me. MySQL, Apache Hadoop, MongoDB, Apache Spark... all of them (at least initially) open source.

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/ By David Linthicum / 0 Comments

A few things have changed since the start of the COVID-19 lockdowns. The Global 2000, as well as governments, now understand that traditional data centers are more vulnerable to natural disasters (such as pandemics) than once thought.  

Indeed, the use of cloud computing means that there are no physical data centers and servers to protect, and that we don’t rely on humans for physical operations as much as we did in the past.  

Putting the obvious aside for now, let’s look at the future of cloud computing, specifically at the future of cloud computing architecture. I see a few changes happening now that won’t likely go away after the current crisis ends:

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/ By David Linthicum / 0 Comments

A few things have changed since the start of the COVID-19 lockdowns. The Global 2000, as well as governments, now understand that traditional data centers are more vulnerable to natural disasters (such as pandemics) than once thought.  

Indeed, the use of cloud computing means that there are no physical data centers and servers to protect, and that we don’t rely on humans for physical operations as much as we did in the past.  

Putting the obvious aside for now, let’s look at the future of cloud computing, specifically at the future of cloud computing architecture. I see a few changes happening now that won’t likely go away after the current crisis ends:

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/ By Martin Heller / 0 Comments

For most professional software developers, using application lifecycle management (ALM) is a given. Data scientists, many of whom do not have a software development background, often have not used lifecycle management for their machine learning models. That’s a problem that’s much easier to fix now than it was a few years ago, thanks to the advent of “MLops” environments and frameworks that support machine learning lifecycle management.

What is machine learning lifecycle management?

The easy answer to this question would be that machine learning lifecycle management is the same as ALM, but that would also be wrong. That’s because the lifecycle of a machine learning model is different from the software development lifecycle (SDLC) in a number of ways.

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/ By Martin Heller / 0 Comments

For most professional software developers, using application lifecycle management (ALM) is a given. Data scientists, many of whom do not have a software development background, often have not used lifecycle management for their machine learning models. That’s a problem that’s much easier to fix now than it was a few years ago, thanks to the advent of “MLops” environments and frameworks that support machine learning lifecycle management.

What is machine learning lifecycle management?

The easy answer to this question would be that machine learning lifecycle management is the same as ALM, but that would also be wrong. That’s because the lifecycle of a machine learning model is different from the software development lifecycle (SDLC) in a number of ways.

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/ By Gene Leganza / 0 Comments

In the Forrester/InfoWorld Enterprise Architecture Awards competition, we look for the most dramatic stories of EA’s strategic leadership and concrete business impact. The winners of the 2020 Forrester/InfoWorld Enterprise Architecture Awards show the value of a close relationship with the business, a solid vision for enabling digital transformation, and effective governance practices — not to mention the need for a high-priority response to a global pandemic!

In alphabetical order, the winners this year are the EA teams of:

Congrats to all the winners — each has a compelling story of EA best practices. You can read those stories below.

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