Month: June 2023

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By Patrick McFadin, DataStax developer relations and contributor to the Apache Cassandra project.

Netflix tracks every user’s actions to instantly refine its recommendation engine, then uses this data to propose the content users will love. Uber gathers driver, rider, and partner data in the moment and then updates a prediction engine that informs customers about wait times or suggests routes to drivers in real time. FedEx aggregates billions of package events to optimize operations and instantly share visibility with its customers on delivery status.

These leaders succeed with these real-time AI capabilities in large part because of their ability to aggregate massive amounts of real-time data from customers, devices, sensors, or partners as it moves through applications. This data in turn is used to train and serve machine learning models. These companies act on this data in the moment, serving millions of customers in real time. And they all rely on the open-source NoSQL database Apache Cassandra^®.

Let’s take a look at why Cassandra is the database of choice for organizations building enterprise-scale, real-time AI applications.

The challenges posed by real-time AI

Only 12% of AI initiatives succeed in achieving superior growth and business transformation, according to Accenture. Why? In a nutshell, data scientists and developers have been trying to build the most powerful, sophisticated applications for the next generation of business on complex infrastructure built for the demands of yesterday.

Many traditional AI/ML systems, and the outcomes they produce, rely on data warehouses and batch processing. The result: A complex array of technologies, data movements, and transformations are required to “bring” this historical data to ML systems. This alters and slows the flow of data from input to decision to output, resulting in missed opportunities that can open the door for customers to churn or allow recognized cyber security threat patterns to go undetected and unmitigated.

The velocity, type, and volume of data drive the quality of predictions and the impact of the outcomes. Real-time AI demands large amounts of data to train ML models and make accurate predictions or generate new content very quickly. This requires a high-performance database that can bring ML to the data. You’ve created the right architecture to collect and store your data and the best way to keep costs low is to leverage what you have. The solution to a storage cost problem is not adding more storage; it’s finding ways to process your data in place.

Enter Cassandra

There are various databases that can be used to develop a real-time AI application. Relational databases such as MySQL or PostgreSQL may be user-friendly, but they are not capable of managing the vast amounts of data required for web-scale AI applications. Although open-source data stores like Redis are available, they lack the durability necessary to support AI applications that are intended to form the foundation of a business.

For real-time AI to live to its full potential, the database that serves as its foundation must be:

• highly scalable to manage massive amounts of data
• reliable for continuous data access
• fast enough to easily capture big data flows
• flexible enough to deal with various data types.

Cassandra is an open-source NoSQL database that scales with performance and reliability better than any other. Many companies, like those mentioned above, have transformed their businesses and led their industries thanks to real-time AI built on Cassandra. Why?

Horizontal scalability: As AI applications become more sophisticated, they require the ability to handle ever-increasing volumes of data. Cassandra’s distributed architecture is based on consistent hashing, which enables seamless horizontal scaling by evenly distributing data across nodes in the cluster (a collection of nodes). This ensures that your AI applications can handle substantial data growth without compromising performance, a crucial factor from a statistical perspective.

High availability: The decentralized architecture of Cassandra provides high availability and fault tolerance, which ensures that your AI applications remain operational and responsive even during hardware failures or network outages. This feature is especially important for real-time AI applications, as their accuracy and efficiency often rely on continuous access to data for mathematical modeling and analysis.

Low latency: With real-time AI, signals generated by user activities must be captured at a very high rate; the ability to write this data to a database fast is critical. Cassandra’s peer-to-peer architecture and tunable consistency model enable rapid read and write operations, delivering low-latency performance essential for real-time AI applications.

Unlike many other data stores, Cassandra is designed in a way that doesn’t require disk reads or seeks during the write process, so writing data to Cassandra is extremely fast and provides the freedom to capture incoming signals with ease—no matter how fast they arrive.

It ensures that AI algorithms receive the latest data as quickly as possible, allowing for more accurate and timely mathematical computations and decision-making.

Flexible data modeling: Cassandra’s NoSQL data model is schema-free, which means that the methodology for storing data is far more flexible than alternative databases, making it possible to store and query complex and diverse data types common in ML and AI applications. This flexibility enables data scientists to adapt their data models as requirements evolve without having to deal with the constraints of traditional relational databases.

The Cassandra community

The Cassandra open-source project is built and maintained by a community of very smart engineers at some of the biggest, most-advanced users of AI (Apple, Netflix, and Uber, to name a few) who are constantly modernizing and extending the capabilities of the database. The upcoming Cassandra 5.0 release, for example, will offer vector search, a critical feature that will be a groundbreaking aid to organizations grappling with the massive datasets that accompany AI efforts.

These advantages make Cassandra a reliable foundation for real-time AI applications that need to handle massive volumes of data while ensuring continuous data access, high performance, and adaptability. If your organization aims to leverage AI to its full potential, choosing the right database is a critical step in your journey.

By adopting a scalable and durable solution like Cassandra, you can ensure the successful execution of your AI initiatives, reduce cost, and optimize processing. It’s time to reconsider your data infrastructure and invest in the right technology to fuel your growth. Remember, the success of your AI strategy doesn’t only lie in the complexity of your algorithms but also in the robustness of your data management system.

Join the growing community of businesses pioneering the future of AI with Cassandra. Seize the opportunity today and equip your business to make the most of real-time AI.

Learn how DataStax makes real-time AI possible here.

About Patrick McFadin

DataStax

Patrick McFadin is the co-author of the O’Reilly book “Managing Cloud Native Data on Kubernetes.” He works at DataStax in developer relations and as a contributor to the Apache Cassandra project. Previously he has worked as an engineering and architecture lead for various internet companies.

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DataStax is partnering with Google to bring vector search to its AstraDB NoSQL database-as-a-service in an attempt to make Apache Cassandra more compatible with AI and large language model (LLM) workloads.

Vector search, or vectorization, especially in the wake of generative AI proliferation, is seen as a key capability by database vendors as it can reduce the time required to train AI models by cutting down the need to structure data — a practice prevalent with current search technologies. In contrast, vector searches can read the required or necessary property attribute of a data point that is being queried.

“Vector search enables developers to search a database by context or meaning rather than keywords or literal values. This is done by using embeddings, for example, Google Cloud’s API for text embedding, which can represent semantic concepts as vectors to search unstructured datasets such as text and images,” DataStax said in a statement.

Embeddings can be seen as powerful tools that enable search in natural language across a large corpus of data, in different formats, and extract the most relevant pieces of data, Datastax said.

Vector databases are seen by analysts as a “hot ticket” item for 2023 as enterprises look for ways to reduce spending while building generative AI based applications.

AstraDB’s vector search accessible via Google-powered NoSQL copilot

Vector search along with other updates will be accessible inside AstraDB via a Google-powered NoSQL copilot that will also help DataStax customers build AI applications, the company said.

Under the hood, the NoSQL copilot combines Cassandra’s vector Search, Google Cloud’s Gen AI Vertex, LangChain, and GCP BigQuery.

“DataStax and GCP co-designed NoSQL copilot as an LLM Memory toolkit that would then plug into LangChain and make it easy to combine the Vertex Gen AI service with Cassandra for caching, vector search, and chat history retrieval. This then makes it easy for enterprises to build their own Copilot for their business applications and use the combination of AI services on their own data sets held in Cassandra,” said Ed Anuff, chief product officer at DataStax.

Plugging into LangChain, an open source framework aimed at simplifying the development of generative AI-powered applications using large language models, is made possible due to an open source library jointly developed by the two companies.

The library, dubbed CassIO, aims to make it easy to add Cassandra-based databases to generative AI software development kits (SDKs) such as LangChain.

Enterprises can use CassIO to build sophisticated AI assistants, semantic caching for generative AI, browse LLM chat history, and manage Cassandra prompt templates, DataStax said.

Other integrations with Google include the ability for enterprises using Google Cloud to import and export data from Cassandra-based databases into Google’s BigQuery data warehouse by using the Google Cloud Console for creating and serving machine learning based features.

A second integration with Google will allow AstraDB subscribers to pipe real-time data to and from Cassandra to Google Cloud services for monitoring generative AI model performance, DataStax said.

DataStax has also partnered with SpringML to help accelerate the development of generative AI applications using SpringML’s data science and AI service offerings.

Availability of vector search for Cassandra

AstraDB, built on Apache Cassandra, will arguably be one of the first to bring vector search to the open source distributed database. Currently, vector search for Cassandra is being planned for its 5.0 release, a post by the database community, where DataStax is a member, showed.

In terms of availability, AstraDB’s vector search presently can be used in non-production workloads and is in public preview, DataStax said, adding that the search will be initially available exclusively on Google Cloud and later extended to other public clouds.

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While EPS of ($0.27) and revenue of $41 million (up 18% year-over-year) came in above the consensus estimates of ($0.32) and $39.77M, respectively, guidance missed the expectations.

Subscription revenue grew 21% year-over-year to $38.5M in Q1. Total ARR as of April 30, 2023, was $172.2M, representing an increase of 23% year-over-year.

“We delivered a solid start to the fiscal year and are pleased that our results exceeded our guidance on all metrics,” said Matt Cain, Chair, President, and CEO of Couchbase.

For Q2/24, the company expects revenue in the range of $41.2M-41.8M, missing the consensus estimate of $43.34M.

For the full year, the company expects revenue in the range of $171.7M-174.7M, compared to the consensus of $173.3M. BASE hiked its total annual recurring revenue forecast to $193.5M from the prior $192M forecast.

Stifel analysts hiked the price target on BASE shares to $22 on “solid Capella activity.”

“The ongoing shift in database spend towards next-generation NoSQL technologies and Capella momentum, should allow Couchbase to sustain mid-teens ARR growth, while posting increasing levels of profitability,” the analysts said.

Guggenheim analysts also hiked the price target, raising it by $3 to $23 per share on the Buy-rated BASE stock.

“We don’t believe investors will find many companies with this business momentum in this environment at this recurring rev multiple,” the analysts said.

Additional reporting by Senad Karaahmetovic

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New Jersey, USA- The study examines the impact of these key trends in detail and outlines the growth opportunities in various segments on the basis of how these trends will shape the NoSQL Software Market Market going forward.

This report provides comprehensive data on emerging trends, market drivers, growth opportunities, and restraints that can change the dynamics of this NoSQL Software Market market. The report evaluates the market size of the Global NoSQL Software Market Market and studies the strategy patterns adopted by prominent international players. Also, the report evaluates the size of the market in terms of revenue for the forecast period. All the data figures like percentage shares split and breakdowns are determined using secondary sources and verified through primary sources.

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The report highlights the key players and manufacturers and the latest strategies including new product launches, partnerships, joint ventures, technology, segmentation in terms of region and industry competition, profit and loss ratio, and investment ideas. A precise evaluation of effective manufacturing techniques, advertisement techniques, market share size, growth rate, size, revenue, sales, and value chain analysis.

Key Competitors of the Global NoSQL Software Market Market are:

• MongoDB
• OrientDB
• Amazon
• Azure Cosmos DB
• Couchbase
• ArangoDB
• CouchDB
• MarkLogic
• SQL-RD
• RethinkDB
• RavenDB
• Microsoft
• Redis

The ‘Global NoSQL Software Market Market Research Report’ is a comprehensive and informative study on the current state of the Global NoSQL Software Market Market industry with an emphasis on the global industry. The report presents key statistics on the market status of the global NoSQL Software Market market manufacturers and is a valuable source of guidance and direction for companies and individuals interested in the industry.

Major Product Types covered are:

• Cloud Based
• Web Based

The Application Coverage in the Market is:

• E-Commerce
• Social Networking
• Data Analytics
• Data Storage
• Others

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Regional NoSQL Software Market Market (Regional Output, Demand & Forecast by Countries):-

• North America (United States, Canada, Mexico)
• South America ( Brazil, Argentina, Ecuador, Chile)
• Asia Pacific (China, Japan, India, Korea)
• Europe (Germany, UK, France, Italy)
• Middle East Africa (Egypt, Turkey, Saudi Arabia, Iran) And More.

The research report studies the past, present, and future performance of the global market. The report further analyzes the present competitive scenario, prevalent business models, and the likely advancements in offerings by significant players in the coming years.

Important Features of the Report:

📌– Detailed analysis of the Global NoSQL Software Market market

📌–Fluctuating market dynamics of the industry

📌–Detailed market segmentation

📌– Historical, current, and projected market size in terms of volume and value

📌– Recent industry trends and developments

📌– Competitive landscape of the Global NoSQL Software Market Market

📌– Strategies of key players and product offerings

📌– Potential and niche segments/regions exhibiting promising growth

📌– A neutral perspective towards Global NoSQL Software Market market performance

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Reasons to Purchase Global NoSQL Software Market Market Report:

1. Current and future of Global NoSQL Software Market market outlook in the developed and emerging markets.
2. Analysis of various perspectives of the market with the help of Porter’s five forces analysis.
3. The segment that is expected to dominate is the Global NoSQL Software Market market.
4. Regions are expected to witness the fastest growth during the forecast period.
5. Identify the latest developments, Global NoSQL Software Market market shares, and strategies employed by the major market players.

Besides, the market study affirms the leading players worldwide in the Global NoSQL Software Market market. Their key marketing strategies and advertising techniques have been highlighted to offer a clear understanding of the Global NoSQL Software Market market.

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Market Research Intellect provides syndicated and customized research reports to clients from various industries and organizations with the aim of delivering functional expertise. We provide reports for all industries including Energy, Technology, Manufacturing and Construction, Chemicals and Materials, Food and Beverage, and more. These reports deliver an in-depth study of the market with industry analysis, the market value for regions and countries, and trends that are pertinent to the industry.

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Nosql Market

The NoSQL database industry is driven by an increase in demand for e-commerce, online applications, unstructured data and data analytics.

PORTLAND, PORTLAND, OR, UNITED STATE, June 6, 2023/EINPresswire.com/ — Allied Market Research published a new report, titled, ” The NoSQL Market Reach to USD 22,087 Billion by 2026 | Top Players Such as – Aerospike, MarkLogic and Neo Technology.” The report offers an extensive analysis of key growth strategies, drivers, opportunities, key segment, Porter’s Five Forces analysis, and competitive landscape. This study is a helpful source of information for market players, investors, VPs, stakeholders, and new entrants to gain thorough understanding of the industry and determine steps to be taken to gain competitive advantage.

The NoSQL market size was valued at USD 2,410.5 million in 2018, and is projected to reach USD 22,087 million by 2026, growing at a CAGR of 31.4% from 2019 to 2026.

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Increase in unstructured data, demand for data analytics, and surge in application development activities across the globe propel the growth of the global NoSQL market. North America accounted for the highest market share in 2018, and will maintain its leadership status during the forecast period. Demand for online gaming and content consumption from OTT platforms increased significantly. So, the demand for NoSQL increased for handling huge amount of data.

The NoSQL market is segmented on the basis of type, application, industry vertical, and region. By type, it is categorized into key-value store, document database, column-based store, and graph database. On the basis of application, it is divided into data storage, mobile apps, data analytics, web apps, and others. Further the data storage segment is sub-segmented into distributed data depository, cache memory, and metadata store. Depending on industry vertical, it is categorized into retail, gaming, IT, and others. By region, the NoSQL market is analyzed across North America, Europe, Asia-Pacific, and LAMEA.

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Based on application, the web apps segment held the largest share, with more than one-fourth of the total share in 2018, and is estimated to dominate during the forecast period. However, the mobile apps segment is projected to manifest the highest CAGR of 33.5% from 2019 to 2026.

Based on type, the key value store segment accounted for more than two-fifths of the total market share in 2018, and is estimated to maintain its lead position by 2026. Contrarily, the graph based segment is expected to grow at the fastest CAGR of 34.2% during the forecast period.

Based on vertical, the IT sector contributed to the highest market share in 2018, accounting for more than two-fifths of the total market share, and is estimated to maintain its highest contribution during the forecast period. However, the gaming segment is expected to grow at the highest CAGR of 34.8% from 2019 to 2026.

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Based on region, North America accounted for the highest market share in 2018, contributing to more than two-fifths of the global NoSQL market share, and will maintain its leadership status during the forecast period. On the other hand, Asia-Pacific is expected to witness the highest CAGR of 35.5% from 2019 to 2026.

Leading players of the global NoSQL market analyzed in the research include Aerospike, Inc., DataStax, Inc., Amazon Web Services, Inc., Couchbase, Inc., Microsoft Corporation, MarkLogic Corporation, Google LLC, Neo Technology, Inc., MongoDB, Inc., and Objectivity, Inc.

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Covid-19 Scenario:

● With lockdown imposed by governments of many countries, demand for online gaming, content consumption from OTT platforms, and activity on social media increased significantly. So, the demand for NoSQL increased for handling huge amount of data.

● With organizations adopting “work from home” strategy to ensure continuity of business processes, NoSQL databases would be needed to store and retrieve data.

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Lastly, this report provides market intelligence most comprehensively. The report structure has been kept such that it offers maximum business value. It provides critical insights into the market dynamics and will enable strategic decision-making for the existing market players as well as those willing to enter the market.

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New Jersey, USA- This section discusses various aspects of the Nosql Databases Software Market sector, including its size, trends, and revenue forecasts. The Nosql Databases Software Market Research literature also presents sections exclusive to assessing and concluding the revenue prospects for each market sector. The Nosql Databases Software Market report concludes with a detailed assessment of this industry, highlighting the growth drivers and lucrative prospects that are likely to affect the global Nosql Databases Software Market over the forecast period.

This section discusses various aspects of this sector, including its size, trends, and revenue forecasts. The Nosql Databases Software Market is segmented by product type, end-user industry, and geography.

The Nosql Databases Software Market report provides an in-depth analysis of the current state of the industry, including its technological trends, competitive landscape, key players, and revenue forecasts for global, regional, and country levels. It also provides comprehensive coverage of major industry drivers, restraints, and their impact on market growth during the forecast period. For the purpose of research, The Report has segmented the global Nosql Databases Software Market on the basis of types, technology, and region.

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Key Competitors of the Global Nosql Databases Software Market are:

• 3M
• ATS Acoustics Putty Pads
• STI Firestop
• ROCKWOOL
• Hilti
• Nullifire
• Pyroplex
• Minerallac
• FSI Limited
• EverBuild (Firespan)
• Metacaulk
• BIOSTOP
• Remo
• Knauf Group

Major Product Types covered are:

• Elastometric Type
• Intumescent Type

The Application Coverage in the Market is:

• Electric Power
• Communication
• Others

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Regional Nosql Databases Software Market (Regional Output, Demand & Forecast by Regions):-

• North America (United States, Canada, Mexico)
• South America ( Brazil, Argentina, Ecuador, Chile)
• Asia Pacific (China, Japan, India, Korea)
• Europe (Germany, UK, France, Italy)
• Middle East Africa (Egypt, Turkey, Saudi Arabia, Iran) And More.

Major Points Covered in Table of Contents:

Nosql Databases Software Market Overview

Market Competition by Manufacturers

Production Picture of Nosql Databases Software Market and Global Nosql Databases Software Market: Classification

Overall Nosql Databases Software Market Regional Demand

Market Breakdown and Data Triangulation Approach

Business, Regional, Product Type, Sales Channel – Trends

Nosql Databases Software Market Dynamics: Restraints, Opportunities, Industry Value Chain, Porter’s Analysis, and Others

Covid-19 impact on Global Nosql Databases Software Market Demand

Market Analysis Forecast by Segments

Competitive Analysis

Market Research Findings & Conclusion The research report studies the past, present, and future performance of the global market. The report further analyzes the present competitive scenario, prevalent business models, and the likely advancements in offerings by significant players in the coming years.

Key Questions Answered by the Report

What are the top strategies that players are expected to adopt in the coming years?

What are the trends in this Nosql Databases Software Market?

How will the competitive landscape change in the future?

What are the challenges for this Nosql Databases Software Market?

What are the market opportunities and market overview of the Nosql Databases Software Market?

What are the key drivers and challenges of the global Nosql Databases Software Market?

How is the global Nosql Databases Software Market segmented by product type?

What will be the growth rate of the Global Nosql Databases Software Market 2023 for the forecast period 2023 to 2030?

What will be the market size during this estimated period?

What are the opportunities business owners can rely upon to earn more profits and stay competitive during the estimated period?

Potential and niche segments/regions exhibiting promising growth

A neutral perspective toward Global Nosql Databases Software Market performance

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Market Research Intellect provides syndicated and customized research reports to clients from various industries and organizations with the aim of delivering functional expertise. We provide reports for all industries including Energy, Technology, Manufacturing and Construction, Chemicals and Materials, Food and Beverage, and more. These reports deliver an in-depth study of the market with industry analysis, the market value for regions and countries, and trends that are pertinent to the industry. 

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Mr. Edwyne Fernandes
Market Research Intellect
New Jersey (USA)
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Toll-Free: +1 (800)-782-1768
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New Jersey (United States) – NoSQL Market research is an intelligence report with meticulous efforts undertaken to study the right and valuable information. The data which has been looked upon is done considering both, the existing top players and the upcoming competitors. Business strategies of the key players and the new entering market industries are studied in detail. Well explained SWOT analysis, revenue share and contact information are shared in this report analysis.

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Some of the Top companies Influencing this Market include:

IBM Corporation, Aerospike Inc, MarkLogic Corporation, Hibernate, MariaDB, Oracle Database, Neo technology, MongoDB, Basho Technologies, Couchbase, PostgreSQL

Various factors are responsible for the market’s growth trajectory, which are studied at length in the report. In addition, the report lists down the restraints that are posing threat to the global NoSQL .This report is a consolidation of primary and secondary research, which provides market size, share, dynamics, and forecast for various segments and sub-segments considering the macro and micro environmental factors. It also gauges the bargaining power of suppliers and buyers, threat from new entrants and product substitutes, and the degree of competition prevailing in the market.

NoSQL Market Types:

Key-Value Store, Document Databases, Column Based Stores, Graph Database.

NoSQL Market Applications:

Retail, Online Game Development, IT, Social Network Development, Web Applications Management, Others

The report studies the key segments in the global NoSQL industry, their growth in the past few years, profiles and market sizes of individual segments, and gives a detailed overview of the profiles of various segments. The report also presents key products and various other products in the global NoSQL industry along with its market size and growth in the study period. The major demand drivers for the global NoSQL industry products and services are outlined in the report. The NoSQL report details some major success factors and risk factors of investing in certain segments.

The report provides insights on the following pointers:

Market Penetration: Comprehensive data on the product portfolios of the top players in the NoSQL market.

Product Development/Innovation: Detailed information about upcoming technologies, R&D activities, and market product debuts.

Competitive Assessment: An in-depth analysis of the market’s top companies’ market strategies, as well as their geographic and business segments.

Market Development: Information on developing markets in its entirety. This study examines the market in several geographies for various segments.

Market Diversification: Extensive data on new goods, untapped geographies, recent advancements, and investment opportunities in the NoSQL market.

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The cost analysis of the Global NoSQL Market has been performed while keeping in view manufacturing expenses, labor cost, and raw materials and their market concentration rate, suppliers, and price trend. Other factors such as Supply chain, downstream buyers, and sourcing strategy have been assessed to provide a complete and in-depth view of the market. Buyers of the report will also be exposed to a study on market positioning with factors such as target client, brand strategy, and price strategy taken into consideration.

Key questions covered in this report?

1. A complete overview of different geographic distributions and common product categories in the NoSQL Market.
2. When you have information on the cost of production, the cost of products, and the cost of production for future years, you can fix up the developing databases for your industry.
3. Thorough break-in analysis for new enterprises seeking to enter the NoSQL Market.
4. Exactly how do the most essential corporations and mid-level companies create cash within the Market?
5. Conduct a thorough study on the overall state of the NoSQL Market to aid in the selection of product launches and revisions.

Table of Contents

Global NoSQL Market Research Report 2022 – 2029

Chapter 1 NoSQL Market Overview

Chapter 2 Global Economic Impact on Industry

Chapter 3 Global Market Competition by Manufacturers

Chapter 4 Global Production, Revenue (Value) by Region

Chapter 5 Global Supply (Production), Consumption, Export, Import by Regions

Chapter 6 Global Production, Revenue (Value), Price Trend by Type

Chapter 7 Global Market Analysis by Application

Chapter 8 Manufacturing Cost Analysis

Chapter 9 Industrial Chain, Sourcing Strategy and Downstream Buyers

Chapter 10 Marketing Strategy Analysis, Distributors/Traders

Chapter 11 Market Effect Factors Analysis

Chapter 12 Global NoSQL Market Forecast

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Google DeepMind recently announced PaLM 2, a large language model (LLM) powering Bard and over 25 other product features. PaLM 2 significantly outperforms the previous version of PaLM on a wide range of benchmarks, while being smaller and cheaper to run.

Google CEO Sundar Pichai announced the model at Google I/O ’23. PaLM 2 performs well on a variety of tasks including code generation, reasoning, and multilingual processing, and it is available in four different model sizes, including a lightweight version called Gecko that is intended for use on mobile devices. When evaluated on NLP benchmarks, PaLM 2 showed performance improvements over PaLM, and achieved new state-of-the-art levels in many tasks, especially on the BIG-bench benchmark. Besides powering Bard, the new model is also a foundation for many other products, including Med-PaLM 2, a LLM fine-tuned for the medical domain, and Sec-PaLM, a model for cybersecurity. According to Google,

PaLM 2 shows us the impact of highly capable models of various sizes and speeds—and that versatile AI models reap real benefits for everyone. Yet just as we’re committed to releasing the most helpful and responsible AI tools today, we’re also working to create the best foundation models yet for Google.

In 2022, InfoQ covered the original release of Pathways Language Model (PaLM), a 540-billion-parameter large language model (LLM). PaLM achieved state-of-the-art performance on several reasoning benchmarks and also exhibited capabilities on two novel reasoning tasks: logical inference and explaining a joke.

For PaLM 2, Google implemented several changes to improve model performance. First, they studied model scaling laws to determine the optimal combination of training compute, model size, and data size. They found that, for a given compute budget, data and model size should be scaled “roughly 1:1,” whereas previous researchers had scaled model size 3x the data size.

The team improved PaLM 2’s multilingual capabilities by including more languages in the training dataset and updating the model training objective. The original dataset was “dominated” by English; the new dataset pulls from a more diverse set of languages and domains. Instead of using only a language modeling objective, PaLM 2 was trained using a “tuned mixture” of several objectives.

Google evaluated PaLM 2 on six broad classes of NLP benchmark, including: reasoning, coding, translation, question answering, classification, and natural language generation. The focus of the evaluation was to compare its performance to the original PaLM. On BIG-bench, PaLM 2 showed “large improvements,” and on classification and question answering even the smallest PaLM 2 model achieved performance “competitive” with much the larger PaLM model. On reasoning tasks, PaLM 2 was also “competitive” with GPT-4; it outperformed GPT-4 on the GSM8K mathematical reasoning benchmark.

In a Reddit discussion about the model, several users commented that although its output wasn’t as good as that from GPT-4, PaLM 2 was noticeably better. One user said:

They probably want it to be scalable so they can implement it for free/low cost with their products. Also so it can accompany search results without taking forever (I use GPT 4 all the time and love it, but it is pretty slow.)…I just used the new Bard (which is based on PaLM 2) and it’s a good amount faster than even GPT 3.5 turbo.

The PaLM 2 tech report page on Papers with Code lists the model’s performance on several NLP benchmarks.

Article originally posted on InfoQ. Visit InfoQ

Avalonia UI is an open-source and cross-platform UI framework for .NET developers, designed to facilitate the development of desktop applications that can run on Windows, macOS, Linux, iOS, Android, and WebAssembly. InfoQ interviewed Mike James, CEO of Avalonia UI, in order to understand more about this UI framework and its features.

InfoQ: How did Avalonia UI emerge within the ecosystem?

Mike James: The project has its roots in OSS, being community-led from its inception rather than being built by an established business and later shared. The core contributors established a company a few years back to provide support and development services to enterprises.

We recently announced our first foray into products with Avalonia XPF, our cross-platform WPF, which enables WPF apps to run on macOS and Linux with little to no code changes.

InfoQ: What are the key features and benefits of using Avalonia UI for developing desktop applications?

Mike: Using Skia, we enable “pixel-perfect” applications that look the same across every platform. You don’t need to learn or dig into platform-specific APIs to tweak your app’s UI or create custom controls! We’ve found that this level of customisation is essential to many of our users.

The other key benefit of Avalonia UI is that we have the broadest range of supported platforms while not compromising performance. You can develop apps for powerful desktops, low-powered embedded devices, phones, tablets, and WebAssembly.

Our API is designed to be cross-platform, and we take time to ensure that added features make sense in the context of cross-platform UI app development. While WPF and WinUI influence our APIs, we don’t simply copy them; we change things where we can improve. We’ve always regarded blindly copying Windows APIs as a flawed strategy.

InfoQ: What is the current state of Avalonia UI in the .NET User Interface landscape?

Mike: When I started exploring cross-platform UIs for .NET in 2012, the only option was Xamarin. Today the landscape is very different; we have many more options, all with their strengths and weaknesses. Our strength is our ability to provide a high-performance, consistent user experience across platforms while providing developers with a familiar, stable and proven SDK.

We’re often compared to MAUI, which interests me as we don’t view MAUI as a competitor. When I worked at Xamarin, the significant benefit was that Xamarin apps were native apps; that is, they used the native UI toolkits. In 2013, this was extremely important, but it’s no longer such a crucial component when picking a UI technology. If we look at Flutter or the growing popularity of web technologies, we see that it’s possible to deliver high-quality experiences without using native UI toolkits.

We have a ton of respect for MAUI, but we see Flutter and Qt as the main competitors going forward. That’s not to disrespect them, but we believe that technologies outside the .NET ecosystem are doing some exciting things.

In terms of our current state, we provide the most performant cross-platform UI toolkit for .NET developers. If developers prioritise performance, then it makes sense for them to use Avalonia UI. We achieve this by managing every pixel displayed within the application. We have very minimal dependencies, on Windows only using Win32 for the Window.

We previously had a version of Avalonia UI that depended on MonoMac, the precursor to Xamarin.Mac, but found its performance wasn’t acceptable, and thus created our own binding approach called MicroCOM.

On Linux, we only need X11, and iOS and Android just require NET.iOS and NET.Android. Once we have a native Window, we’re good to start pushing pixels and receiving user input events.

InfoQ: How is Avalonia UI positioned in comparison with Microsoft’s recent efforts?

Mike: We tend not to compare ourselves to MAUI, which provides respectable support for mobile but falters on other platforms. From a technical perspective, Avalonia’s approach is drastically different from that of MAUI. MAUI is less a UI toolkit and more a UI abstraction. It relies on the underlying native UI toolkit to work, while we draw the entire UI using ourselves. Most of our users are using SkiaSharp to achieve this, but our architecture makes the renderer pluggable and we’re looking at alternatives as SkiaSharp risks becoming a liability with its lack of maintenance from Microsoft.

The drawn approach yields considerable benefits, especially regarding performance, ease of development and platform support. What really illustrates this is our ability to easily support any platform. Our only requirement is the ability to push pixels and receive interaction events. To give a concrete example, you only need to look at our VNC support, which works with only ~200 LOC. It’d be impossible for MAUI to add new platforms at the speed that we can, simply due to their architecture and dependency on abstracting native UI toolkits.

Our ultimate aim is to become the first choice for every new project that requires a client app, regardless of the target platform. We deliver the best experience for desktop apps already, but we must continue improving our mobile offering to compete with Flutter. We’re going to achieve this by continuing to make investments in growing the team developing Avalonia UI.

InfoQ: What is the roadmap for future development of Avalonia UI, and what new features can we expect to see in upcoming releases?

Mike: In the short term, our roadmap focuses on releasing v11 and overhauling the documentation portal.

v11 provides vast improvements, including a new compositional renderer, accessibility, additional platform support and much more.

Past the release of v11, we’ve some plans that will help us continue to drive adoption and improve the developer experience. We’re going to improve our Visual Studio extension and investigate other renderers to remove our dependency on SkiaSharp.

Interested readers can learn more about Avalonia UI and get started using the documentation and Avalonia GitHub repository.

Posted on nosqlgooglealerts. Visit nosqlgooglealerts

The NoSQL market research delivers comprehensive research on the present stage of the market, covers market size with respect to assessment as sales volume, and provides a precise forecast of the market scenario over the estimated period. Also focuses on the product, application, manufacturers, suppliers, and regional segments of the market. NoSQL market report research highlights market driving factors, an overview of the market growth, industry size, and market share. Subsequently NoSQL market report depicts the constantly evolving needs of clients, vendors, and purchasers in different regions, it becomes simple to target specific market and generate large revenues in the global industry.

According to the latest research study, the demand of global NoSQL market size & share was valued at approximately USD 85.4 Billion in 2022 and is expected to reach USD 96.2 billion in 2023 and is expected to reach a value of around USD 170 Billion by 2030, at a compound annual growth rate (CAGR) of about 12%during the forecast period 2023 to 2030.”

Some of these key players include: PostgreSQL, Oracle Database, Objectivity, Neo, MySQL, MongoLab, MongoDB, Microsoft SQL Server, MarkLogic, IBM, Hypertable, DynamoDB, Couchbase, CloudDB, Cisco, Basho Technologies, Aerospike

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Global NoSQL Market by Type:

Key-Value Store, Document Databases, Column Based Stores, Graph Database

Global NoSQL Market by Application:

Data Storage, Metadata Store, Cache Memory, Distributed Data Depository, e-Commerce, Mobile Apps, Web Applications, Data Analytics, Social Networking

Regional Assessment:

Geographically, the global NoSQL market is classified into four major regions including North America (the US and Canada), Europe (UK, Germany, France, Italy, Spain, and Rest of Europe), Asia-Pacific (India, China, Japan, and Rest of Asia-Pacific), and Rest of the World (Latin America and the Middle East and Africa (MEA)).

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– To project the consumption of NoSQL submarkets, with respect to key regions (along with their respective key countries).

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Strategic Points Covered in Table of Content of Global NoSQL Market:

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Chapter 5: Displaying the by Type, End User and Region/Country 2017-2021
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