BOSTON–(BUSINESS WIRE)–August 10, 2023–

Metaplane became the first data observability tool ever to achieve Snowflake Technical Validation, part of Snowflake’s official partner program, created to supply customers reassurance that integration standards between tools are up to par.

Snowflake’s partnership tier certifications are based on the number of joint customers and the quality of the integration. After reaching a particular partnership tier, eligible partners can apply for a Technical Validation certification that guarantees:

• Connection compatibility with all Snowflake configurations
• Detailed documentation on a joint integration
• Meeting stringent security requirements, including secure user access with no data exfiltration
• Functional features that solve for the use cases of the tool category.

To achieve this, Metaplane underwent a months long process including:

• Providing system architecture diagrams
• Demonstrating the product live to verify required functionality
• Providing detailed summaries of capabilities and security certifications of Metaplane

“I’m excited for our expanded partnership with Metaplane to ensure reliable and accurate data as customers continue to push the envelope with what they’re doing with their data in Snowflake,” says Tarik Dwiek, Head of Technology Alliances at Snowflake.

One joint customer, Vivian Health has already been able to Strengthen their customer experiences by using a combination of:

• Python function in Snowpark – to generate training data used in machine learning models for their job recommendation features.
• Metaplane’s continuous data monitoring – to verify the data used for the machine learning model training.

Metaplane’s investment into the stronger partnership has already yielded an upcoming product release, using Data Quality Metric Functions alongside Metaplane to Strengthen data quality. This allows greater flexibility for users to host their known rules within Snowflake while using Metaplane to handle automated continuous monitoring at scale.

Kevin Hu, CEO of Metaplane, says “I couldn’t have imagined that within a year of beginning our partnership with Snowflake that we’d be the first Data Observability solution to help establish their Technical Validation process, and even more excited for the 100s of customers whose data projects we’ll be able to help!”

For more information about Metaplane, visit www.metaplane.dev.

About Metaplane
Metaplane is the Datadog for Data. Data teams at high-growth companies (like Vendr, Drift, and Sigma Computing) use the Metaplane data observability platform to save engineering time and increase trust in data by understanding when things break, what went wrong, and how to fix it – before an executive messages them about a broken dashboard.

View source version on businesswire.com: https://www.businesswire.com/news/home/20230810457396/en/

brandon@metaplane.dev

Most organizations, and managers, believe that ongoing learning and development for their people is important for their growth, improved performance and overall business success. As a result, companies around the world invest significantly in training each year, with over $366 billion spent in 2018. Training budgets increased significantly in 2019, and research suggests that the role of learning and development (L&D) will broaden in 2020.

However, despite this belief, focus and investment, many organizations fail at implementing the type of learning culture and training experience that will truly elevate performance and provide significant returns on investment (ROIs). While most managers say they believe training is important, they typically consider the implementation and execution of L&D in their organization to be less than effective. In fact, HBR states that 75% of managers (registration required) are dissatisfied with their company's L&D function. Why the disconnect? Based on our experience developing learning strategies and cultures with companies, we've found six critical L&D problems that exist in many organizations today.

1. Some L&D teams lack L&D expertise and real-world credibility. We typically find that those responsible for the L&D efforts lack in-depth expertise in developing and/or delivering content. The L&D function is too often another step in an HR generalist's approach to getting their experience in all aspects of the business. Add to this the fact that most of these people designing and delivering content have little to no operations experience, and many have never actually led a team. As a result, we often see a situation where the L&D team will deliver the training they want to deliver, rather than what is actually needed in the operation or by the management team. Therefore, it is not surprising when there is a lack of confidence with in-house training teams.

2. The training offered is not what is needed. As mentioned, we see many L&D teams delivering training that doesn't directly relate to improving individual or organizational performance. They often focus on measuring success by the number of trainings provided, instead of focusing on the quality of what is delivered. We see a lot of training created that is generic and at a high enough level to "cover everybody." This approach is limiting, and it is no wonder that 70% of employees (registration required) report they do not have the mastery to do their jobs. L&D in the future must be more personalized and customized to what people really need — providing specific training to drive habit improvement.

3. L&D becomes a mandatory to-do. Once a lack of confidence in the value of training occurs, management teams become resistant to sending their people to training, and they often do not see the value in their own attendance. As a result, organizations, at the request of their L&D teams, will often shift to make training mandatory, thereby forcing individuals into learning. I can tell you from experience that forced training does not work, especially when the content and delivery is dull and lacks relevance.

4. The focus is on training events, not a learning culture. Some organizations seem more focused on investing and delivering training events, one-off training sessions that are not always connected, relevant nor delivered when they are needed most. It is no wonder that many managers loathe the training process. When the focus is just on "ticking the box" with a certain number of events rather than evolving an attitude throughout the organization on the benefits and need for learning, the training investment is often wasted. Companies must establish an attitude and infrastructure that offers employees and managers the time and opportunity for continuous learning on a variety of subjects in a variety of mediums at any time.

5. The top of the organization has stopped learning. Another issue limiting a learning culture is when L&D is not truly supported from the top of the organization. While every executive we meet says they support training, they often do not engage in any learning and development themselves. Whenever we see true learning cultures, it is usually because there is real participation and support from the very top of the organization. Not only do these executives stop leading when they stop learning, but they also send a message that training is not as important as they might be telling us.

6. The training overlooks the biological realities of learning and retention. Simply put, many training sessions we review are too long, try to cram too much information into a single session or are just boring. As attention spans become shorter, we must evolve the learning experience to be more interactive, built around games and/or discussions and involve participants being hands-on. We need to build training that fits our employees rather than trying to make our employees fit our training. We must also be more considerate of how retention works, meaning that training strategies must be built around opportunities with time to practice skills learned and less time on introducing new concepts. On-the-job-training is still the best learning experience, so more emphasis must be placed on allowing time to practice in the operation rather than on a computer or in a classroom.

Learning cultures are essential for the modern organization. In the 2020s, companies must move beyond just offering learning events or a curriculum of e-learning modules and focus on developing a true learning culture, one that inspires, open minds, supports change and growth, encourages creativity, delivers innovation and develops the next level of leaders. It is time to consider whether your business is maximizing its investment in training and if your company is approaching L&D correctly. Consider whether any of these issues apply to you, and be willing to rethink your L&D to maximize your ROI.

Azure Synapse Analytics and Snowflake are two commonly recommended ETL tools for businesses that need to process large amounts of data. Choosing between the two will depend on the unique strengths of these services and your company’s needs. These are the key differences between Synapse and Snowflake, including their features and where they excel.

Jump to:

What is Azure Synapse Analytics?

Azure Synapse Analytics (formerly known as Azure SQL Data Warehouse) is a data analytics service from Microsoft. It’s part of the Azure platform, which includes products like Azure Databricks, Cosmos DB and Power BI.

Microsoft describes it as offering a “… unified experience to ingest, explore, prepare, transform, manage, and serve data for immediate BI and machine learning needs.” The service is one of the most popular tools available for information warehousing and the management of big data systems.

Key features of Azure Synapse Analytics include:

• End-to-end cloud data warehousing.
• Built-in governance tools.
• Massively parallel processing.
• Seamless integration with other Azure products.

What is Snowflake?

Snowflake is another popular big data platform, developed by a company of the same name. It’s a fully managed platform as a service used for various applications — including data warehousing, lake management, data science and secure sharing of real-time information.

A Snowflake data warehouse is built on either the Amazon Web Services or Microsoft Azure cloud infrastructure. Cloud storage and compute power can scale independently.

Like most available data platforms, Snowflake is built with key trends in business intelligence automation in mind, including automation, segmentation of intelligence workflows and growing use of anything as a service tools.

The major competitors of Snowflake include Dremio, Firebolt, and Palantir.

Key features of Snowflake’s platform include:

• Scalable computing.
• Data sharing.
• Data cloning.
• Integration with third-party tools, including many Azure products.

SEE: For more information, explore our overview of Snowflake.

Azure Synapse Analytics vs. Snowflake: Comparison table

Azure Synapse Analytics and Snowflake pricing

Azure Synapse Analytics pricing

Azure Snapase offers different pricing tiers and categories based on region, type of service, storage, unit of time and other factors. The prepurchase plans are available in six tiers starting with 5,000 Synapse Commit Units for $4,750. The higher tier is priced at $259,200 for 260,000 SCUs.

The pricing for data integration capabilities offered by Azure Synapse Analytics is based on data pipeline activities, integration runtime hours, operation charges, and data flow cluster size and execution. Each activity has separate charges. For example, Basis Data Flows are charged at $0.257 per vCore-hour, while Standard Data Flows are charged at $0.325 per vCore-hour.

Snowflake pricing

The pricing for Snowflake is divided into four tiers, the pricing of which depends on the preferred platform and region. For example, if you prefer the Microsoft Azure platform and are located in the U.S. West region, you will pay the following:

• Standard: $2 per credit.
• Enterprise: $3 per credit.
• Business Critical: $4 per credit.
• AVS: Customized pricing.

You can choose to pay an extra $50 per terabyte per month for on-demand storage or $23 per terabyte per month for capacity storage.

Feature comparison: Azure Synapse Analytics vs. Snowflake

The two extract, transfer and load products have a lot in common, but they differ in specific features, strengths, weaknesses and popular use cases.

Use cases and versatility

Synapse Analytics and Snowflake are built for a range of data analysis and storage applications, but Snowflake is a better fit for conventional business intelligence and analytics. It includes near-zero maintenance with features like automatic clustering and performance optimization tools.

Businesses that use Snowflake for storage and analysis may not need a full-time administrator who has deep experience with the platform.

By comparison, native integration with Spark Pool and Delta Lake makes Synapse Analytics an excellent choice for advanced big data applications, including artificial intelligence, machine learning and data streaming. However, the platform will require much more labor and attention from analytics teams.

A Synapse Analytics administrator who is familiar with the platform and knows how to effectively manage the service will likely be necessary for a business to benefit fully. Setup of the Synapse Analytics platform will also likely be more involved, meaning businesses may need to wait longer to see results.

Architecture

Snowflake isn’t built to run on a specific architecture and will run on top of three major cloud platforms: AWS, Microsoft Azure’s Cloud platform and Google Cloud. A layer of abstraction separates the Snowflake storage and compute credits from the actual cloud resources from a business’s provider of choice.

Each virtual Snowflake warehouse has its own independent compute cluster. They don’t share resources, so the performance of one warehouse shouldn’t impact the performance of another.

Comparatively, Azure Synapse Analytics is built specifically for Azure Cloud. It’s designed from the ground up for integration with other Azure services. Snowflake will also integrate with many of these services, but it lacks some of the capabilities that make Synapse Analytics’ integration with Azure so seamless.

Scalability

Snowflake has built-in auto-scaling capabilities and an auto-suspend feature that will allow administrators to dynamically manage warehouse resources as their needs change. It uses a per-second billing model, and being able to quickly scale storage and compute up or down can provide immediate cost savings.

The zero-copy cloning feature from Snowflake allows administrators to create a copy of tables, schemas and warehouses without duplicating the actual data. This allows for even greater scalability.

Azure offers strong scalability but lacks some of the features that make Snowflake so flexible. Serverless SQL Pools and Spark Pools in Azure have automatic scaling by default. However, Dedicated SQL Pools require manual scaling.

SEE: Compare features of top time tracking software.

Azure Synapse Analytics pros and cons

Pros of Azure Synapse Analytics

• Deep integration with the Azure ecosystem.
• Unified platform for data warehousing and analytics.
• Advanced analytics capabilities.

Cons of Azure Synapse Analytics

• Steep learning curve for beginners.
• Serverless capabilities are limited to newer Azure services.

Snowflake pros and cons

Pros of Snowflake

• Cloud-native.
• Automatic performance tuning.
• User-friendly interface.

Cons of Snowflake

• Limited control over the infrastructure.
• Reliant on cloud service for availability.

Review methodology

To review Azure Synapse Analytics and Snowflake, we analyzed various factors, including the core functionality, scalability, ease of use, integration capabilities, security tools and customer support. We also analyzed the pricing structure of each solution, including its licensing costs and any extra charges for add-on services.

Should your organization use Azure Synapse Analytics or Snowflake?

A company deciding between Synapse and Snowflake is in a good position. Both platforms are excellent data storage and analysis services, with features necessary for many business intelligence and analysis workflows.

However, the two do differ when it comes to specific strengths and ideal use cases. Snowflake excels for companies that want to perform more traditional business intelligence analytics and will benefit from excellent scalability.

With Snowflake, you get a more user-friendly interface but are dependent on cloud service availability. As Snowflake is cloud-native, you also have limited direct control over the infrastructure. Businesses that need granular control over their infrastructure optimization will find this a key disadvantage of Snowflake.

Azure Synapse Analytics has a steeper learning curve than Snowflake, and scalability may be more challenging, depending on the type of pool a business uses. However, it’s an excellent choice for companies working with AI, ML and data streaming and will likely perform better than Snowflake for these applications.

SAN FRANCISCO, Aug. 17, 2023 — DynamoFL, Inc. announced that it has closed a $15.1 million Series A funding round to meet demand for its privacy- and compliance-focused generative AI solutions. Coming off a $4.2M seed round, the company has raised $19.3M to date. DynamoFL’s flagship technology, which allows customers to safely train Large Language Models (LLM) on sensitive internal data, is already in use by Fortune 500 companies in finance, electronics, insurance and automotive sectors.

The round, co-led by Canapi Ventures and Nexus Venture Partners, also had participation from Formus Capital, Soma Capital and angel investors Vojtech Jina, Apple’s privacy-preserving machine learning (ML) lead, Tolga Erbay, Head of Governance, Risk and Compliance at Dropbox and Charu Jangid, product leader at Snowflake, to name a few.

The need for AI solutions that preserve compliance and security has never been greater. LLMs present unprecedented privacy and compliance risks for enterprises. It has been widely shown that LLMs can easily memorize sensitive data from its training dataset. Malicious actors can exploit this vulnerability to extract sensitive users’ personally identifiable information and sensitive contract values with carefully designed prompts, posing a major data security risk for the enterprise. The pace of innovation and adoption in the AI sector is punctuated by the rapidly changing global regulatory landscape, many of which require that enterprises detail these data risks, but enterprises today are not equipped to detect and address the risk of data leakage. In the EU, the GDPR and the impending EU AI act, along with similar initiatives in China and India, as well as AI regulation acts in the US, require that enterprises detail these data risks. However, today they are not equipped to detect and address the risk of data leakage.

More clearly needs to be done. As government agencies like the FTC explore concerns around LLM providers’ data security, DynamoFL’s machine learning privacy research team recently showed how personal information – including sensitive details about C-Suite executives, Fortune 500 corporations, and private contract values – could be easily extracted from a fine-tuned GPT-3 model. DynamoFL’s privacy evaluation suite provides out of the box testing for data extraction vulnerabilities and automated documentation to ensure enterprises’ LLMs are secure and compliant.

“We deploy our suite of privacy-preserving training and testing offerings to directly address and document compliance requirements to help enterprises stay on top of regulatory developments, and deploy LLMs in a safe and compliant manner,” said DynamoFL co-founder Christian Lau.

“Privacy and compliance are critical to ensuring the safe deployment of AI across the enterprise. These are foundational pillars of the DynamoFL platform,” said Greg Thome, Principal at Canapi. “By working with DynamoFL, companies can deliver best-in-class AI experiences while mitigating the well-documented data leakage risks. We’re excited to support DynamoFL as they scale the product and expand their team of privacy-focused machine learning engineers.”

The company’s solutions help organizations privately fine-tune LLMs on internal data while identifying and documenting potential privacy risks. Organizations can choose to implement DynamoFL’s end-to-end suite or to implement their Privacy Evaluation Suite, Differential Privacy and/or Federated Learning modules individually.

DynamoFL was founded by two MIT PhDs who have spent the last six years researching the cutting-edge, privacy-focused AI and ML technology forming the basis of the company’s core offerings. The team balances expertise in the latest research in privacy-preserving ML, with researchers and engineers from MIT, Harvard and Cal-Berkeley, and experience in deploying enterprise-grade AI applications for Microsoft, Apple, Meta and Palantir, among other top tech companies.

“This investment validates our philosophy that AI platforms need to be built with a focus on privacy and security from day one in order to scale in enterprise use cases,” said DynamoFL CEO and co-founder Vaikkunth Mugunthan. “It also reflects the growing interest and demand for in-house Generative AI solutions across industries.”

“While AI holds tremendous potential to transform every industry, the need of the hour is to ensure that AI is safe and trustworthy. DynamoFL is set to do just that and enable enterprises to adopt AI while preserving privacy and remaining regulation-compliant,” said Jishnu Bhattacharjee, Managing Director, Nexus Venture Partners.”We are thrilled to have partnered with Vaik, Christian and team in their journey of building an impactful company.”

About DynamoFL, Inc.

DynamoFL is the world’s leading enterprise solution for privacy-preserving Generative AI. At DynamoFL we believe that prioritizing privacy, compliance and data security from day 1 while building Generative AI applications is the only way to responsibly scale AI and use it to augment human potential beyond what was thought possible. Our proprietary technology encapsulates state of the art optimization techniques for training and deploying Generative AI models along with a robust privacy training and evaluation suite incorporating paradigms like Federated Learning and Differential privacy to bring high performing end-to-end plug-and-play Generative AI to global enterprises.

Source: DynamoFL

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MindsDB, a database platform facilitating AI-centric applications, today announced the successful conclusion of a $5 million investment round led by NVentures, an arm of Nvidia, with participation from additional investors. This new funding propels MindsDB’s cumulative seed funding to $46.5 million, fortifying the company’s objective of democratizing access to artificial intelligence (AI) for global enterprises.

MindsDB stated that this capital infusion will expedite the company’s mission to integrate AI capabilities into products aimed at the expansive cohort of approximately 30 million software developers spanning diverse industries.

The company has highlighted its platform’s array of over 130 AI integrations, allowing developers to oversee AI models originating from diverse advanced machine learning frameworks like OpenAI, Anthropic, Hugging Face, Langchain and Nixtla. 

By facilitating a fusion of these models with data residing within platforms such as Amazon Redshift, Google BigQuery, MySQL, Postgres, MongoDB and Snowflake, the platform assumes a pivotal role in enabling cohesive AI incorporation.

“This new backing from Nvidia signals that the AI revolution will not be limited to companies with fully staffed data science teams and expertise. Every developer worldwide, regardless of their AI knowledge, should be capable of producing, managing and plugging AI models into existing software infrastructure,” Jorge Torres, CEO and cofounder of MindsDB, told VentureBeat. “Our goal is to help solve, enable and inspire the world’s 30 million-plus developers to leverage their data to build AI applications, no matter their data source or [the] machine learning model/framework they want to use.”

Torres claims that AI proficiency is a rarity among current software developers, with fewer than 1% possessing this skill. Most of these few proficient individuals are nestled within the ranks of the largest market leaders. This scarcity, he asserts, erects barriers that hinder burgeoning startups as well as small and medium-sized enterprises from harnessing the advantages of generative AI.

In response to these challenges, Torres elucidated that MindsDB’s mission revolves around democratizing AI development, rendering the journey from prototype to production accessible to all stripes of developers without requiring specialized AI training. 

The platform aims to empower developers to fashion AI applications directly from existing corporate data reservoirs, erasing the barriers to entry and fostering the adoption of an AI-centric paradigm across companies of varying dimensions. 

“Our mission to increase AI accessibility within organizations will only grow in importance as AI fundamentally changes the world,” Torres told VentureBeat. “The new funding will enable us to evolve our product to empower even more developers to build the next generation of AI applications.”

The company announced the availability of MindsDB Pro, a service offering dedicated GPU-equipped instances for experimentation and deployment of AI/ML projects via the cloud. With over 150,000 open-source installations, MindsDB said that notable companies, including Bytes, Dumuso, JourneyFoods, Progressify, Precise Finance, and Rize have already employed its services to streamline their product development and internal operations.

MindsDB’s Torres emphasized data’s pivotal role in AI/ML and underscored developers’ need to access the most pertinent AI models to catalyze transformative business applications. 

Furnishing users with a comprehensive array of ML/AI frameworks, Torres says, eases user success.

“Our partnerships across the database and AI ecosystems enable users to take advantage of MindsDB’s advanced ML from within these platforms, turning their databases into powerful predictive engines,” explained Torres. “We enable integration of all of the different elements of a company’s data stack to be easily input into AI models and then the output of that AI to be put back into a data source. Our platform is the central hub connecting data sources to the most relevant AI models, enabling the creation of useful AI-powered solutions.”

Torres said that the open-source developer community has significantly contributed to advancing the company’s mission of democratizing AI development. Initially, the platform incorporated just a handful of data sources. However, in the past year, the potential has exponentially grown, driven by the power of the open-source community to independently construct integrations.

“We’ve taken a bottoms-up approach because many of our new customers of the managed version of MindsDB — MindsDB Pro — discovered us through our partners or from starting with us through our open-source product,” he said. “Now, we are focused on how to provide reliability and stability when scaling our cloud. For SMBs that often lack dedicated ML engineering teams, our managed services offer a user-friendly interface that allows non-experts to leverage machine learning effectively.”

Financial services platform Domuso, for example, used MindsDB to create and implement a reliable ML model using MindsDB’s AutoML solution, supported by machine learning experts.

Domuso engineered predictions and transitioned them into live operations, accomplishing this with its existing team and technological resources. MindsDB claims that the move resulted in a $95,000 reduction in chargebacks over a span of two months, potentially yielding savings of approximately $500,000 annually.

“MindsDB seeks to bring AI development closer to data, simplifying the generation of AI while bridging the gap between AI and the necessary data to unlock its potential,” Torres told VentureBeat. “We’re dedicated to eliminating the complexities of managing multiple AI frameworks. With our unified platform, organizations can seamlessly execute and automate a variety of AI frameworks through our platform’s extensive array of integrations.”

Lead investor NVentures joins a consortium of existing investors, including Benchmark, Mayfield, Y Combinator, OpenOcean, and Walden Catalyst in this new funding endeavor.