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10 months to innovation: Definity's leap to data agility with BigQuery and Vertex AI

Definity, a Canadian P&C insurer, underwent a critical mission to modernize its data infrastructure for innovation and scalability.
Legacy limitations, including scalability issues, data silos, and rising costs, prompted Definity to migrate its data ecosystem.
Definity chose Google Cloud's BigQuery and Vertex AI to build its new Strategic Data Platform (SDP) for advanced analytics.
The migration involved collaboration with Google Cloud and Quantiphi, completing the process in just ten months, 50% faster than the industry average.
The migration resulted in exceptional user satisfaction, cost savings, and significant improvements in performance and agility for Definity.
Lessons learned from the migration journey include fostering collaboration, bold decision-making, and transparent communication.
Definity's future plans include leveraging BigQuery and Vertex AI for further innovation and the development of new AI use cases.
By migrating to BigQuery and Vertex AI, Definity aims to continue leading innovation in the insurance industry with a scalable and AI-ready data foundation.
The partnership with Google Cloud is expected to play a vital role in helping Definity achieve its data transformation goals and expand its AI capabilities.
Definity's migration experience highlights the importance of teamwork, leadership trust, adaptability, and incremental delivery for successful data platform modernization.

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Dev

187

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Why Every Data Analyst Needs To Know SQL: A Beginner's Guide

SQL (Structured Query Language) is crucial for data analysts as it enables data access, manipulation, and efficiency in handling large datasets.
Data analysts work with various data types stored in relational databases like MySQL and PostgreSQL, using SQL to extract insights for business decision-making.
SQL helps in identifying trends, analyzing performance, and optimizing operational efficiency in real-world business scenarios.
Key SQL concepts for beginners include SELECT, FROM, WHERE, JOIN, GROUP BY, and ORDER BY, forming a solid foundation for query building.
SQL integrates with data analysis tools like Excel, Power BI, Python, and R, aiding in data visualization and advanced processing.
Common challenges for SQL beginners include inefficient queries and JOIN operations, which can be overcome through practice and understanding data structures.
To improve SQL skills, practicing on online platforms, working on projects, and engaging with data analytics communities are recommended.
Transitioning from other fields to data analytics brings valuable skills like analytical thinking, problem-solving, and logical reasoning, enhancing one's perspective in the field.
Learning SQL can significantly impact a data analyst's career, opening doors to complex problem-solving, cross-functional projects, and increased employability.
Mastering SQL is a standout way to showcase one's capability in data analytics, leading to career growth and valuable contributions in the job market.

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Amazon

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Multiple database support on Amazon RDS for Db2 DB instance

Organizations can benefit from running multiple databases on a physical server or virtual machine to optimize resource utilization in IBM Db2 environments.
Amazon RDS for Db2 now supports multiple databases within a single instance, offering resource optimization and cost reduction.
Db2's multiple database architecture allows for strong isolation between applications while sharing instance-level resources like CPU and memory.
Key components in Db2's multiple database architecture include separate logs, catalog tables, tablespaces, buffer pools, and instance-level resources.
Amazon RDS for Db2 multiple database feature provides resource optimization, cost savings, ease of management, and flexible database control.
Logical isolation and security are maintained within each database, with support for AWS KMS encryption at rest and SSL/TLS encryption in transit.
Scaling databases and instances is made easier with Amazon RDS for Db2 multiple database feature, without additional charges for enabling it.
Best practices for managing multiple databases on Db2 instances include monitoring resource usage, memory allocation, CPU utilization, I/O throughput, and connections.
The cleanup process after testing the multiple database feature involves dropping test databases, removing catalog entries, and node configurations.
Amazon RDS for Db2's multiple database feature streamlines database management and helps reduce costs by consolidating databases within a single instance.

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Amazon

239

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Build resilient Oracle Database workloads on Amazon EC2

Resiliency in database workloads involves withstanding and recovering from failures, which is essential for critical business applications.
High availability and disaster recovery are vital for a resilient database architecture, with metrics like RPO and RTO guiding the design.
AWS offers options like Amazon RDS or self-managed Amazon EC2 for running Oracle Database workloads.
Different architecture patterns and options are available for the compute and storage layers when configuring self-managed Oracle databases on Amazon EC2.
Considerations for Oracle Database AMIs and Amazon EBS storage are crucial for designing resilient and performant database environments.
Multiple architecture patterns are discussed, including configurations with Data Guard, Active Data Guard, Amazon EBS, and Oracle ASM for volume management.
Cross-Region disaster recovery using Data Guard can provide high availability and data protection for critical workloads, with multiple standby databases.
Backup considerations include using Oracle native tools, AWS Backup, and RMAN to meet data protection, disaster recovery, and compliance needs.
Start with RPO and RTO requirements, choose appropriate HA and DR options, and consider Amazon EBS and AWS Backup when running Oracle databases on Amazon EC2.
The authors are specialists from Amazon Web Services, focusing on database projects, migrations to AWS Cloud, and building resilient architectures.

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Global Fintech Series

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Oracle Brings AI Agents to the Fight Against Financial Crime

Oracle Financial Services is enhancing its Investigation Hub Cloud Service with new agentic AI capabilities to automate investigative processes in fighting financial crime.
The AI agents use generative AI-driven narratives to supplement investigators' analysis, saving time and resources by automating traditionally manual tasks.
These AI agents are designed to surface key insights, collect evidence, recommend decisions, and generate comprehensive alert narratives, making financial investigations more efficient.
The new capabilities are available globally for financial institutions of all sizes using Oracle's Investigation Hub crime and case management solution.

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Insider

100

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From Amazon to Pinterest: The 40 tech companies that file the most H-1B immigrant work visas

Tech companies, especially giants like Amazon and Microsoft, file for thousands of H-1B visas annually for skilled foreign workers.
President Trump's immigration policies have caused a stir around the future of H-1B visas, sparking debate within the tech industry.
Key figures in Trump's base have expressed opposition to the H-1B program, while tech leaders like Elon Musk acknowledge the need for improvement.
A public analysis revealed that tech companies like Amazon, Microsoft, Google, Meta, and Apple are among the heaviest users of H-1B visas.
These companies utilize the program to fill various roles such as software engineers, research positions, and data science roles.
In the 2024 fiscal year, Amazon filed the most H-1B requests with a total of 14,783, followed by Microsoft, Alphabet, Meta, and Apple.
The top 40 tech companies sponsoring H-1B visas also include IBM, Intel, Oracle, Tesla, Salesforce, Nvidia, Cisco, and Qualcomm, among others.
The future of the H-1B program remains uncertain, and any changes could significantly impact America's largest technology companies.
While the number of filings does not directly translate to hires, these companies rely on H-1B visas to access global talent for critical positions.
The analysis focused on tech product companies like Amazon, Microsoft, and Google, excluding IT consulting firms traditionally large users of H-1B visas.

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Dev

244

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Search for the Closest Matching Record within the Group — From SQL to SPL #9

The task is to find the record closest to ConfirmationStarted among all the Closed records before ConfirmationStarted in each ID.
In SQL, this problem can be solved using multi-level subqueries and window functions to generate sequence numbers and filter records.
In SPL (Structured Process Language), which provides natural sequence numbers and rich position-related calculations, this problem can be solved more efficiently.
esProc SPL is now available as a free download.

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Towards Data Science

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7 Powerful DBeaver Tips and Tricks to Improve Your SQL Workflow

DBeaver has a powerful Command Palette feature, accessible through hotkeys or the search option.
DBeaver allows setting up a different SQL formatter, such as pg_formatter, for customized formatting preferences.
The Content Assist feature in DBeaver helps expand SELECT * queries into explicit column names with the CTRL + Space hotkey.
DBeaver provides useful features like the Calc tab for analyzing query results and the Groupings tab for creating group-by queries.

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Inside

266

Oracle’s Java Platform Extension for Visual Studio Code Passes 2.5 Million Downloads!

Oracle’s Java Platform extension for Visual Studio Code(JPEVSC) brings comprehensive development capabilities for Java to Visual Studio Code.
JPEVSC is a self-contained plugin, packed with all the essential features for programming in Java that doesn’t require any additional extension. It supports build tools like Maven and Gradle, as well as projects without dependency management tools. Furthermore, it provides a range of development features, including code refactoring, application debugging, and running unit tests.
Key features of the JPEVSC extension include Auto-Complete and hints for specific APIs, JDK downloader capability, and support for configuring projects with a different JDK version. The extension also has localization for Japanese and Simplified Chinese languages.
Contributions to the development of the JPEVSC extension are welcomed from the community. Users can provide feedback, automate tests, identify bugs, fix issues, and add enhancements. Code contributions require signing the Oracle Contributor Agreement (OCA).

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Cloudblog

144

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ScaNN for AlloyDB: The first PostgreSQL vector search index that works well from millions to billion of vectors

ScaNN for AlloyDB is the first Postgres-based vector search extension designed to handle vector indexes of various sizes efficiently, offering fast index builds, transactional updates, small memory usage, and speedy and precise search capabilities.
AlloyDB users engage in complex semantic search and AI tasks with 100 million to over 1 billion vectors, often turning to the pgvector HNSW graph algorithm for large vector search indexes.
Although pgvector HNSW excels in small dataset query performance, it faces challenges with speed, cost, and performance for very large datasets, leading to the development of ScaNN for AlloyDB.
ScaNN for AlloyDB, integrating Google's ScaNN vector search technology, offers a cost-effective solution with smaller memory usage, improved latency, and compatibility with pgvector.
In benchmarks, ScaNN for AlloyDB demonstrated significantly lower index build costs and faster latency compared to other PostgreSQL systems, especially for large datasets that do not fit in main memory.
The comparison between ScaNN for AlloyDB and pgvector HNSW underlined the superior performance and lower memory footprint of ScaNN, providing faster search and insert latencies even for extensive datasets.
Key differences lie in the data organization and algorithms, where ScaNN's tree-based structure offers cache-friendly, sequential access, outperforming HNSW's random access for larger datasets.
ScaNN for AlloyDB's market-leading features make it a competitive option for vector search applications, showcasing better search performance, low memory usage, and cost-effectiveness.
The ScaNN for AlloyDB extension is available in AlloyDB, empowering users to leverage efficient vector search capabilities for diverse applications, whether for large or small datasets.
To delve deeper into the ScaNN for AlloyDB index, users can refer to the introduction and whitepaper provided, enabling them to explore and implement the cutting-edge ScaNN algorithm in their projects.

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Mongodb

100

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ORiGAMi: A Machine Learning Architecture for the Document Model

ORiGAMi is a Transformer-based architecture designed for supervised learning on semi-structured data like JSON in a document model database.
It addresses the challenges faced by the ML community in working with semi-structured formats compared to traditional tabular data.
The architecture tokenizes documents into key-value pairs and structural tokens, making prediction directly from semi-structured documents possible.
By training on datasets with as few as 200 labeled samples, ORiGAMi combines data efficiency with Transformer model flexibility.
The model's token sequences serve as input for predicting the next token, ensuring valid document generation.
ORiGAMi reformulates classification to predict any field within a document, eliminating the need for separate models or pipelines.
Example use case includes user segmentation based on user profiles containing nested structures like device history and subscription details.
With ORiGAMi, users can make predictions on raw documents, preserving nested structures and updating predictions as user behavior changes.
The architecture is open-sourced on GitHub, with command-line interfaces for training models and making predictions seamlessly.
ORiGAMi provides a way for document-native machine learning, inviting users to explore, contribute, and apply it to real-world problems.

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Hackernoon

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Is Our Technique Effective in Finding Bugs for XPath Expression Processors? An Investigation

Our technique, XPress, was evaluated for its effectiveness and efficiency in finding bugs for XPath expression processors.
The evaluation included investigating the effectiveness of XPress in finding new XPath-related bugs in established XML processors.
The query generation approach described in Section 3.2 was analyzed for its bug-finding efficiency in comparison to real-world baselines and a random generation approach.
The evaluation also compared the differential testing test oracle to the state-of-the-art oracle.

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Siliconangle

425

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Oracle misses expectations and guidance was also weak, sending its stock lower

Oracle's stock fell in extended trading after missing analysts' expectations and reporting lower-than-expected financial results for the third quarter of fiscal year 2025.
The company's earnings came in at $1.47 per share, below the forecast of $1.49 per share, with revenue of $14.13 billion missing the analyst target of $14.39 billion.
Despite revenue growth, Oracle also fell short of estimates in key segments like cloud services and license support, with revenue increasing by 10% to $11.01 billion.
Oracle's cloud infrastructure segment saw rapid growth, with revenue up 49% to $2.7 billion, driven by customer demand and the introduction of the Oracle AI Data Platform.
The company announced plans to double its data center capacity this year to meet customer demand and highlighted investments in AI initiatives like the Stargate Project alongside partners including OpenAI.
Oracle's performance obligations saw a significant increase, reaching $130 billion, exceeding the estimated $103.3 billion.
Despite concerns about overspending in cloud infrastructure, Oracle signed cloud agreements with major companies, aiming for a 15% revenue increase in the next fiscal year.
Oracle's guidance for the current quarter fell short of Wall Street's expectations, with revenue growth projected at 8-9% and earnings expected to range from $1.61 to $1.65 per share, lower than analysts' forecasts.
Investors displayed skepticism and Oracle's stock declined by 4% in extended trading following a general market downturn and concerns over economic uncertainty.
Despite challenges, Oracle benefits from its own infrastructure and direct integration of AI models, which shields it from rising compute costs and adoption barriers faced by cloud rivals.

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Amazon

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Demystifying Amazon DynamoDB on-demand capacity mode

AWS Solutions Architects provide insights on common misconceptions about Amazon DynamoDB on-demand capacity mode.
On-demand capacity mode can reduce complexity, but questions about cost, performance, and operation persist.
Misconceptions about on-demand capacity costs include oversimplification of pricing, comparison with provisioned mode, and charges for unused capacity.
Amazon CloudWatch metrics help optimize costs and understand usage patterns in DynamoDB.
Performance myths, such as slower response times and limits on scaling, are clarified, highlighting consistent low latency and customizable throughput limits.
Operational myths are addressed, including control over consumption, pre-warming necessity, and downtime during capacity changes.
On-demand mode's benefits lie in reduced monitoring needs, seamless scaling, and flexibility for variable workloads.
Misconceptions about throttling elimination and applicability of on-demand mode are discussed, emphasizing partition-level limits and broad usage potential.
AWS recommends assessing workload specifics to choose between capacity modes, focusing on operational efficiency and actual usage.
To start with DynamoDB on-demand mode, utilize the Amazon DynamoDB console and Developer Guide for best practices and optimizations.

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Dev

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Get the records after and before the searched one：SQL VS SPL #12

The ProductionLine_Number in a certain table of the Mariadb database is a grouping field, and there are duplicate values in the Cardboard_Number field within the group.
To retrieve records with a specified Cardboard_Number, grouping by ProductionLine_Number and sorting by date_Time within the group can be done.
The SQL solution involves using window functions to assign sequence numbers and then performing interval association using JOIN.
On the other hand, SPL (Structured Processing Language) has grouped subsets and a positional reference mechanism, simplifying the code.

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