Snowflake Users Gain 100x Faster Insights with Indexima’s Analytics Solution

Snowflake Users Gain 100x Faster Insights with Indexima’s Analytics Solution

Looking to accelerate business intelligence with Snowflake? Indexima, a French company, claims to offer the solution. Founded in 2015 to manage data for Mappy, France's answer to Google Maps, Indexima has evolved significantly. By 2018, after securing €1.3 million in seed funding, the company shifted its focus to managing and analyzing big data stored on Hadoop and other databases, launching its first product, Indexima 1.0. By 2020, it had 15 customers and an annual recurring revenue of €1.2 million. While still supporting Hadoop, Indexima has pivoted to the more promising Snowflake market, releasing Indexima 2.0 this year, with future plans to support Databricks.

Indexima touts its ability to generate fast, actionable business intelligence from Snowflake by creating an “aggregation layer” using its proprietary algorithm, backed by an AI-powered business intelligence assistant.

A Unique Data Software for Analytics

Nicolas Korchia, Indexima's CEO and founder, describes the software as "a unique data solution leveraging ML and AI to address the performance, cost, and agility challenges of analytics."

Indexima employs a columnar data storage format, which organizes data by columns rather than rows—an approach optimized for analytical queries. This structure enables efficient data compression, faster scanning, and the automatic creation of indexes as data is ingested. These indexes are tailored to the data types (e.g., categorical, numerical, textual) and query patterns, enabling sub-millisecond query responses.

Distributed and Scalable Analytics

Built for distributed environments, Indexima supports parallel processing across nodes, with indexes distributed throughout the cluster for scalable querying of large datasets.

For Snowflake users, Indexima functions as a proxy, automatically creating “dynamic tables” that aggregate key information from user queries. This re-routing significantly accelerates query performance, often delivering results up to 100 times faster while reducing costs. Additionally, the system learns and adapts based on the types of queries and datasets used, further improving its performance.

Strategic Partnerships and Market Presence

At the recent IT Press Tour in Valletta, Malta, Korchia highlighted that using Indexima can make Snowflake significantly faster and more cost-efficient, with no development costs required. Snowflake has become a key strategic partner, assisting with lead generation and deal acceleration in its marketplace.

Indexima’s technology partnerships include major players like MicroStrategy, Tableau, Looker, Power BI, Microsoft Azure, AWS, and Cloudera.

Flexible Pricing and Deployment

The cost of Indexima is determined by the number of queries and dynamic tables used per month, with free, regular, business, and enterprise versions available. More insights can be found in Indexima’s blogs, showcasing how the company continues to redefine analytics with its cutting-edge solutions.

79% of Financial Firms Boost Budgets to Overcome Data Management Challenges

Financial firms have long relied on outdated data systems that hinder efficiency and drive up costs. Despite efforts to adopt advanced technologies like artificial intelligence (AI), many institutions face challenges due to fragmented infrastructures.

Inefficiencies in Data Management

Research by Gresham reveals that outdated systems create inefficiencies and elevate regulatory risks. Many financial firms still depend on spreadsheets and legacy tools, leading to a web of data silos and inconsistent quality. This fragmented approach complicates integration and slows decision-making processes.

Cost Implications of Poor Data Management
UK firms, for instance, onboard new data faster than their US counterparts, completing the process in weeks instead of months. However, 44% of organizations struggle to manage data spread across multiple locations, leading to redundancies and inflated costs. The growing volume of data adds to expenses, yet most firms lack systems for real-time cost tracking.

Only 21% of financial firms monitor data consumption and costs in real time. The rest face unexpected expenses, particularly smaller firms that rely on manual tracking methods. These inefficiencies delay reporting and strain budgets.

Additionally, opaque pricing models and fragmented budget allocations exacerbate the issue. Hidden costs tied to data management remain a significant concern for 34% of firms, according to the report.

The Need for Real-Time Data Management

Real-time data management is crucial for financial firms to stay competitive, yet many hesitate to modernize their systems. While 79% plan to increase their budgets for real-time data, many lack the foundational practices needed to implement these systems effectively.

The report also highlights the risks of relying on AI without first addressing data inefficiencies. Poor-quality data can produce errors in AI-driven insights, leading to higher costs and misleading conclusions. Without robust data management practices, AI projects are unlikely to deliver meaningful results.

Recommendations for Improved Data Systems

To address these challenges, the report recommends centralizing budgets and adopting scalable, real-time data systems to reduce redundancies and enhance decision-making. It also advocates for embracing data-as-a-service solutions to lower costs and improve operational efficiency.

A separate report on payment data emphasizes how AI can transform raw data into actionable insights by leveraging unified datasets and advanced analytics. AI can reveal hidden patterns and trends that traditional methods often miss, demonstrating its potential to drive strategic decision-making when combined with effective data management practices.

LIKAT Chemist Honored for Advancing Digital Data Management and AI in Catalysis

For the third consecutive year, the Catalysis Consortium of the National Research Data Infrastructure (NFDI) has honored excellence in implementing the FAIR principles of scientific data management. The 2024 "NFDI4Cat - Digital Chemist Award" was presented to Dr. David Linke of the Leibniz Institute for Catalysis Rostock (LIKAT) during the annual NFDI4Cat meeting in November. The FAIR principles—Findability, Accessibility, Interoperability, and Reusability—establish standards for handling research data.

Over the past four years, Dr. Linke and his team have been developing tools within the NFDI4Cat consortium to enable laboratories to digitize their catalysis research data. This data, accessible to the scientific community, is also used to train AI models. According to Dr. Linke, catalysis researchers typically only publish a fraction of their experimental data. However, the data from failed yet valid experiments—often 10 to 50 times the amount published—is equally valuable for AI training and scientific progress.

The tools created by Dr. Linke help chemists document and prepare research data in machine-readable formats, facilitating unambiguous data exchange. This effort addresses the "I" in FAIR—interoperability—which Dr. Linke describes as the most challenging aspect of their mission. Developing a precise and standardized vocabulary for catalysis research has been a key achievement, as even in exact sciences, technical terms can be interpreted differently.

In the future, AI applications in science could significantly benefit from such efforts, particularly through the use of knowledge graphs. Unlike large language models (LLMs), these graphs serve as precise "scientific experts," integrating the specialized vocabulary developed by Dr. Linke's team to accurately represent complex relationships.

The creation of an AI-compatible research data pool offers immense benefits. Dr. Linke explains that it enhances research efficiency by enabling comprehensive connections to related work, identifying experimental gaps left by other laboratories, and expanding the global pool of accessible knowledge.

The NFDI e.V., established in October 2020 by Germany’s federal and state governments, aims to drive digitization in the research data sector. This initiative is slated to continue until 2030. The NFDI4Cat consortium, one of the association’s first consortia in the field of chemical catalysis, partnered with Chemistry Europe to establish the Digital Chemist Award, which includes a 1,000-euro prize.