Data observability specialist Monte Carlo on Monday unveiled root cause analysis capabilities aimed at making it faster and easier to identify and resolve data quality incidents.
The vendor’s new root cause analysis tools are targeted specifically at diagnosing changes to Databricks query code and GitLab pull requests. It plans to add similar capabilities for addressing incidents in other platforms as well, according to Lior Gavish, Monte Carlo’s co-founder and CTO.
Root cause analysis is a method of discovering the underlying reasons for code changes that lead to poor data quality.
By identifying the cause of a change as it occurs, root cause analysis enables developers and engineers to address the change before it has consequences, making it a significant part of the data management process, according to Kevin Petrie, an analyst at BARC U.S.
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“A viable data observability program encompasses detection, assessment and remediation,” he said. “Once you detect an issue, you need to find the root cause so you can assess, triage and remediate, for example, by debugging or replacing a bad data pipeline.”
Based in San Francisco, Monte Carlo is a data observability vendor whose platform enables customers to monitor data as it progresses through the pipeline from its ingestion through integration and ultimately analysis. Its purpose is to ensure that the data used to train models and applications, feed dashboards, and inform decisions is accurate and up to date.
A viable data observability program encompasses detection, assessment and remediation. Once you detect an issue, you need to find the root cause so you can assess, triage and remediate. Kevin PetrieAnalyst, BARC U.S.
In 2023, the vendor added data observability for vector databases, which have become a crucial part of retrieval-augmented generation pipelines used to train generative AI tools. Also that year, Monte Carlo launched its own generative AI capabilities, enabling customers to create SQL code using natural language and alerting users to coding problems with suggested fixes.
New capabilities
Data quality is imperative, perhaps more so now than ever as enterprise interest in AI increases and more processes get automated.
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With data being the foundation for analytics and AI, data needs to be accurate for the decisions based on analytics and AI to likewise be accurate. Meanwhile, with data volume increasing exponentially and the complexity of data also rising, it’s impossible for even teams of humans to monitor every data point and data set for quality.
In response, vendors such as Monte Carlo and other data observability specialists including Acceldata, Metaplane and Soda have developed platforms that automatically monitor data for quality and alert users when incidents occur.
Those alerts, however, have to do with the data points and data sets rather than the underlying code. Therefore, to remedy an incident, data engineers and other experts still have to trace the incident back to its source — its root cause — before it can be fixed. That process can take on average 15 hours, according to a survey of more than 200 data professionals by Monte Carlo and Wakefield Research.
That’s nearly two full workdays just to find the source of a single incident and get it remedied. Root cause analysis aims to eliminate much of the time and expense related to discovering and resolving the changes that cause data quality problems.
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Data quality issues can often be traced to one of three causes, according to Gavish: problems with the data itself, something amiss in a system or trouble with code.
Monte Carlo’s new root cause analysis capabilities specifically target issues with Databricks and GitLab code, whether they are simple coding mistakes by developers and engineers or unforeseen consequences of intentional updates.
Developers and engineers get alerts when incidents are detected, including information that correlates the incident with the specific change that caused it. As a result, downtime — the time it takes to resolve data quality problems — is reduced by about 80%, according to Monte Carlo.
“When it comes to resolving data issues, speed is everything,” Gavish said. “Being able to quickly root cause code-related issues leads to faster resolution. When you have visibility into data, systems and code issues all in one platform, it’s much easier to understand the root cause.”
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How to approach root cause analysis.
Specifically, using Monte Carlo’s new root cause analysis capabilities, the data observability vendor’s customers can easily view Databricks query logs and query changes for each table. By doing so, they can see whether there was a query change to that particular table or a table in another part of the data pipeline, and if that change is the cause of the problem.
Similarly, Monte Carlo’s new capabilities enable GitLab users to see which pull requests are linked to which tables. This can help users understand when those requests occurred and new code was merged, and if that new code is causing a data quality issue.
Given the visibility they enable, Monte Carlo’s new root cause analysis capabilities for Databricks query code and GitLab pull requests are significant for the data observability vendor’s customers, according to Petrie.
“Data teams frequently revise transformation code to meet changing business requirements, adjust formats, filter columns and so on,” he said. “While they try to minimize errors by branching and testing pipeline code, some problems inevitably get into production and break data quality. Monte Carlo helps data engineers spot those errors faster by autodetecting anomalous logs.”
Given its potential impact for developers and engineers, Monte Carlo has plans to expand its data observability platform to include root cause analysis capabilities beyond Databricks and GitLab.
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Databricks and GitLab are each popular environments for developers, with Databricks aggressively building an environment for developing generative AI, traditional AI and machine learning models during the past couple of years. However, many developers and engineers prefer other platforms for building data and AI models and applications.
To meet their needs, Monte Carlo plans to expand its root cause analysis capabilities beyond Databricks and GitLab, according to Gavish, though he did not specify which platforms the company plans to target next.
“We are constantly exploring and building new and stronger ways to enhance Monte Carlo’s resolution capabilities,” he said. “We believe strongly in empowering our customers to resolve data issues right where they start.”
Future plans
With root cause analysis for Databricks query code changes and GitLab pull requests now available — and plans already in place to add more root cause analysis capabilities — Monte Carlo’s product development roadmap is focused on three main themes, according to Gavish:
Expanding data observability to cover the entire data management process from ingestion through analysis.
Applying data observability to AI applications.
More root cause analysis addresses expediting incident resolution. Recent integrations with Informatica and Microsoft’s Azure Data Factory aim to expand Monte Carlo’s data observability capabilities to more of the data management process. And integrations with vendors such as vector database company Pinecone are geared toward applying data observability to AI development.
“It’s our vision to continue evolving Monte Carlo into a platform that can not only detect, but resolve and ultimately prevent issues from wherever they derive in our customers’ data stacks,” Gavish said.
Petrie, meanwhile, suggested that Monte Carlo expand its data observability capabilities beyond monitoring for data quality.
Data observability does not have to be limited to the data itself, he noted. It can extend to monitoring the performance of the processes that make up a data pipeline and prepare data from the time it’s first ingested to the points when it’s ready to inform analysis.
“Monte Carlo traditionally focuses on data quality observability,” Petrie said. “I’d be interested to see them expand in adjacent spaces such as data pipeline observability, which focuses more on the performance of underlying infrastructure.”
Eric Avidon is a senior news writer for TechTarget Editorial and a journalist with more than 25 years of experience. He covers analytics and data management.
“We have heard your distress calls loud and clear,” wrote Al Hope, creative director at Creative Assembly. “I’m delighted to confirm, on behalf of the team, that a sequel to Alien: Isolation is in early development.”
Alien: Isolation released in 2014 to rave reviews, marking it as one of the best video game adaptations from the Alien franchise. It stars Amanda Ripley, daughter of Ellen “get away from her, you bitch” Ripley as she attempts to discover what happened to her mother 15 years after the events of the first Alien movie. Part of the reason for the game’s success is because it taps into the anxiety of fear. Rather than confront the xenomorphs directly, Alien: Isolation requires that Amanda complete her objectives while trying to avoid and escape them, arming her with franchise staple tools like motion trackers and flamethrowers.
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Makers rejoice! This mini PC has a rare expansion connector that no rival offers — 9-pin port is located on the front of Geekom device and could be useful for some great DIY projects
Mini PC makers are becoming increasingly inventive as they cater to the growing demand for compact yet powerful computing solutions. With consumers and businesses alike seeking more space-efficient devices without compromising performance, manufacturers are pushing boundaries in design and functionality.
Geekom makes some of our favorite mini devices, like the AX8 Pro, and its latest offering, the Mini Air12 Lite, is another great addition to its portfolio. As you can guess from the name, this is a scaled-down – and cheaper – version of Geekom’s Mini Air12.
Powered by a 12th Gen Intel Alder Lake N100 processor, it comes with up to 16GB of DDR4-3200 RAM and up to 1TB of M.2 2280 PCIe Gen 3 x4 SSD storage. You can preorder it on the Geekom US site with 8GB RAM and a 256GB SSD for $199 (down from the usual MSRP of $249). It’s expected to ship in October.
Plenty of ports
The Mini Air12 Lite provides a decent array of ports. On the back, you’ll find two USB 3.2 Gen 2 and two USB 2.0 ports, as well as DisplayPort 1.4 and HDMI 2.0 outputs, supporting dual-monitor setups, along with an RJ45 Ethernet port. Wireless connectivity is provided via WiFi 5 and Bluetooth 5.1.
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The front panel features two USB 3.2 Gen 1 ports and separate 3.5mm jacks for a microphone and headphones. Digital audio is also an option via HDMI and DP.
What sets the Mini Air12 Lite apart is the 9-pin expansion header on the front. This provides pins for connecting external components or additional functionalities, including a 5V DC power pin, power switch pins, and pins for power and HDD activity LEDs. Since the device already features a power button and built-in power LED, we’d guess the aim is to offer users more customization options, possibly configuring these pins for extra controls or indicators, but we’ll have to wait until it’s released to know for certain.
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Hackers can eavesdrop on conversations near smartphones by measuring sound vibrations with the handset’s built-in motion sensors.
Experiments had previously shown that the gyroscope and accelerometers in smartphones, collectively known as an inertial measurement unit (IMU), could detect sound vibrations in the air and listen in on conversations. This means an app that doesn’t have permission to use the microphone could get around this by using the IMU as a makeshift sound sensor.
The Halo franchise is going to look a bit different going forward, starting with developer 343 Industries itself. The company announced Sunday that it’s rebranding to Halo Studios to mark a whole “new approach” to development, along with multiple new Halo projects.
This is the second time in history that the Halo series will be developed under a different name. The first three Halo games was developed by Bungie, followed by 343 Industries, which was formed inside Microsoft after Bungie opted to go independent.
“If you really break Halo down, there have been two very distinct chapters. Chapter 1 – Bungie. Chapter 2 – 343 Industries,” studio head Pierre Hintze said in an Xbox Wire post. “Now, I think we have an audience which is hungry for more. So we’re not just going to try improve the efficiency of development, but change the recipe of how we make Halo games.”
The first big change is to transition from using the proprietary Slipspace engine to Unreal Engine 5. Halo Infinite, the franchise’s last new mainline game, was made on the engine, but it required a lot of internal upkeep. It’s made from decades-old Bungie code, and according to Bloomberg, that partially led to Infinite‘s long development cycle, along with a dependance on contract workers, and a switch to remote work because of the COVID-19 pandemic.
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Bloomberg reported early last year that the company would be switching over to Unreal after a series of disappointments. While Infinite had a positive launch, the edges started to show soon after. The multiplayer’s post-launch releases were received negatively by the fan base that cited overpriced cosmetics, slow progression, and thin updates, but game modes were also delayeddue to problems with Slipspace. Basically, the switch to Unreal is a long time coming, and will allow the team to work on more projects.
“Respectfully, some components of Slipspace are almost 25 years old,” art director Chris Matthews said in the post. “Although 343 were developing it continuously, there are aspects of Unreal that Epic has been developing for some time, which are unavailable to us in Slipspace — and would have taken huge amounts of time and resources to try and replicate.”
“We had a disproportionate focus on trying to create the conditions to be successful in servicing Halo Infinite,” Hintze said in the Halo Studios announcement. “[But switching to Unreal] allows us to put all the focus on making multiple new experiences at the highest quality possible.”
Halo Studios has been working in Unreal with Project Foundry, which isn’t a new game but a demonstration of how the Epic Games engine can be used with the Halo series. The company showed off some example clips at the 2024 Halo World Championship on Sunday. As for the new Halo games in the works, we don’t have any information on them just yet.
Petrie, meanwhile, suggested that Monte Carlo expand its data observability capabilities beyond monitoring for data quality.
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