real-time analytics Archives

Tech Optimizer

June 19, 2025

5 Backend Tools That’ll Cut Your Dev Time in Half (2025 Stack)

Many developers rely on traditional tools like Redis, RabbitMQ, PostgreSQL, and ORMs due to familiarity, but there are newer alternatives that can improve performance and simplify workflows. 1. **Litefs**: A tool that enables SQLite in production with real-time replication across regions, allowing for global scaling with minimal latency, ideal for edge computing scenarios. 2. **Temporal.io**: A workflow engine that guarantees execution for background jobs, eliminating lost orders or stuck payments, and remembers everything even after crashes. 3. **DragonflyDB**: A drop-in replacement for Redis that is four times faster, capable of handling millions of requests per second with built-in horizontal scaling. 4. **sqlc**: A tool that generates type-safe Go/Postgres code directly from SQL, avoiding ORM complexities and runtime SQL errors. 5. **Benthos**: A tool for connecting Kafka, databases, APIs, and WebSockets using simple YAML configuration, supporting over 200 integrations. 6. **Earthly**: A CI/CD tool that combines Docker and Makefile for deterministic builds, ensuring reproducible builds and avoiding common CI pipeline failures.

Tech Optimizer

June 14, 2025

Striim Announces Neon Serverless Postgres Support with Databricks

The integration of real-time data for AI applications is crucial, but traditional ETL processes can introduce latency and complexity. Open-source Postgres is a popular choice for developers, and Neon has developed an AI-driven approach to database creation. Databricks has launched Lakebase, a managed Postgres database, after acquiring Neon. Striim has expanded its Postgres offerings to facilitate high-throughput ingestion from Neon into Databricks for real-time analytics and enables rapid data delivery from legacy systems into Neon. This allows for seamless data flow for AI applications, including support for real-time ingestion, Change Data Capture (CDC), and event streaming from Apache Kafka. Striim's platform supports operational data replication from various traditional systems and upholds data governance with AI-driven PII detection. This expansion enhances partnerships with Databricks and supports real-time SQL Server data access.

Tech Optimizer

June 13, 2025

Postgres and the Lakehouse Are Becoming One System — Here’s What Comes Next

Developers are increasingly using Postgres for application needs alongside lakehouse technologies for analytics and data science. Postgres has evolved into a versatile operational database suitable for various applications, while lakehouse technologies enable organizations to manage and analyze large-scale data efficiently. There is a growing synergy between these systems, although they often operate in silos managed by different teams. An emerging architectural paradigm views Postgres and lakehouses as complementary layers within a modular framework to address both operational and analytical requirements. Historically, OLTP systems were used for transactions and OLAP systems for analysis, but this model is shifting. Scenarios such as financial applications requiring real-time risk reports and SaaS applications calculating usage metrics are becoming standard. Effective communication between product-engineering teams managing operational systems and data teams overseeing lakehouse services is essential for enhancing system resilience. A trend called operational medallion architecture incorporates bronze, silver, and gold layers for real-time and user-facing systems. The bronze layer consists of raw data, the silver layer contains cleaned data in Postgres for real-time analytics, and the gold layer features pre-aggregated data for low-latency experiences. Each layer is queryable, allowing for bidirectional data movement between S3 and Postgres. Key developments facilitating the integration of operational databases and lakehouses include the maturation of Iceberg as a flexible table format, the evolution of Postgres with support for various data types and querying capabilities, and a growing demand for composability among developers. The market is moving towards modular, open, and developer-friendly architectures that integrate operational and analytical layers. Future data infrastructure will focus on interconnected components and coherent modern data architecture that serves both operational and non-operational use cases seamlessly.

Tech Optimizer

June 12, 2025

Striim Announces Neon Serverless Postgres Support to Broaden Agentic AI Use Cases with Databricks

The demand for real-time data in artificial intelligence is increasing, but integrating data from legacy systems poses challenges due to traditional ETL pipelines that can introduce latency. Open-source Postgres is favored for operational needs, and Neon offers a new approach to database creation using AI agents. Databricks has launched Lakebase, a managed Postgres database for AI applications, after acquiring Neon. Striim has expanded its Postgres offerings to enable high-throughput data ingestion from Neon into Databricks for real-time analytics and supports rapid data delivery from legacy systems into Neon. Striim's platform allows real-time replication of operational data from various traditional systems to Neon, real-time ingestion into Databricks, and enhances generative AI applications with inline data enrichment. Alok Pareek from Striim highlighted the importance of this expansion for Postgres-native teams to build real-time AI architectures. Striim also supports Databricks Delta Lake and SQL2Fabric-X for real-time SQL Server data access. Striim's platform processes over 100 billion daily events with sub-second latency, aiding proactive decision-making.

Tech Optimizer

May 21, 2025

Achieve up to 1.7 times higher write throughput and 1.38 times better price performance with Amazon Aurora PostgreSQL on AWS Graviton4-based R8g instances

Upgrading to Graviton4-based R8g instances with Aurora PostgreSQL-Compatible 17.4 in an Aurora I/O-Optimized cluster configuration results in significant performance improvements. The new instances provide up to 1.7 times higher write throughput, 1.38 times better price-performance, and reduce commit latency by up to 46% on r8g.16xlarge instances and 38% on r8g.2xlarge instances compared to Graviton2-based R6g instances. The Amazon Aurora PostgreSQL-Compatible Edition now supports AWS Graviton4-based R8g instances and PostgreSQL 17.4, which introduces performance enhancements for I/O-Optimized configurations, optimizing write operations and batch processing. R8g instances offer up to 192 vCPUs and 1.5 TB of memory, supporting larger configurations and providing up to 50 Gbps of network bandwidth. PostgreSQL 17 includes vacuum improvements, eliminates the need to drop logical replication slots during upgrades, and expands SQL/JSON standards. Aurora PostgreSQL-Compatible separates compute from storage, enabling independent scaling and maintaining six-way replication for durability, while processing changes as log records to reduce I/O operations. Performance benchmarks using HammerDB show improvements in throughput and commit latency across various workloads. For small workloads on 2xlarge instance size, throughput increased by 50.25% and commit latency improved by 33.87%. For medium workloads on 16xlarge instance size, throughput increased by 30% and commit latency improved by 17.44%. The most significant performance benefits arise from combining hardware upgrades from Graviton2 to Graviton4 with database engine upgrades from PostgreSQL 15.10 to 17.4. For small workloads, throughput increased by 70% and commit latency improved by 38.71%. For medium workloads, throughput increased by 70% and commit latency improved by 46.67%. Cost efficiency is also enhanced, with a 38% improvement in price performance and a 61.26% improvement in price-performance ratio when comparing Graviton2 and Graviton4 instances. Reserved Instances for Graviton4-based R8g instances offer additional cost-optimization opportunities.

Winsage

May 21, 2025

Announcing new Microsoft Dataverse capabilities for multi-agent operations

Microsoft Dataverse is a secure and scalable platform that integrates enterprise data with agent functionalities, serving as the backbone for organizations to manage business and operational data. It powers Microsoft Copilot Studio, enabling developers to create agents that execute adaptive tasks while ensuring human oversight. Key features include AI-powered search, prompt columns for embedding generative AI, and the Dataverse Model Context Protocol (MCP) server, which transforms structured data into interactive knowledge for agents. The MCP server offers capabilities such as querying data, engaging with knowledge sources, creating/updating records, and executing custom prompts. Dataverse knowledge is integrated into Copilot Studio, connecting structured and unstructured data from various sources to create a unified knowledge network. Data in Dataverse is pre-indexed for near-real-time analytics, and integration with Microsoft Fabric allows for easy exploration of this data. Dynamics 365 data is now accessible within Microsoft 365 Copilot, streamlining workflows. New knowledge sources and connectors have been introduced, including Snowflake, SAP, and Confluence, enhancing agent capabilities. The Power Platform connector SDK simplifies the integration of external structured data into Power Apps and Dataverse. A centralized Tools hub in Copilot Studio allows for the management of reusable functionalities across agents. Additionally, three new managed agents are available in preview, designed to automate document workflows, generate executive briefs, and process inbound leads, facilitating quick implementation and scalability for organizations.

Tech Optimizer

February 7, 2025

What is Managed Endpoint Protection?

Managed Endpoint Protection involves outsourcing endpoint security for devices like laptops, desktops, and servers to specialized providers. These services offer continuous monitoring, automated responses, and compliance checks, utilizing advanced analytics and real-time threat intelligence. Key features include continuous monitoring, automated threat containment, vulnerability management, forensic analysis, compliance tools, and threat hunting. Managed services address various threats such as ransomware, phishing, zero-day exploits, credential theft, insider threats, DDoS attacks, and data exfiltration. Benefits include 24/7 expert coverage, access to specialized threat intelligence, faster incident response, reduced operational complexity, predictable pricing, and improved compliance. Challenges include the rapidly evolving threat landscape, skills shortage, budget constraints, and ensuring real-time visibility. Best practices for implementation involve maintaining asset inventories, defining roles, fine-tuning detection thresholds, conducting regular drills, and integrating with broader security measures. When choosing a provider, factors to consider include technical expertise, service level agreements, integration capabilities, pricing, compliance, and ongoing support. SentinelOne offers an autonomous cybersecurity platform that enhances endpoint security through superior visibility, automated responses, and customizable features.

Tech Optimizer

December 18, 2024

PostgreSQL becomes developers’ choice for AI and real-time apps

PostgreSQL is the preferred database for AI developers, with 78.6% of respondents favoring it for its flexibility in managing diverse data types. There has been a 65% increase in PostgreSQL adoption among developers compared to the previous year, and 55.3% of PostgreSQL developers now incorporate AI tools into their workflows, reflecting a 55% year-over-year increase. Additionally, 60% of developers use PostgreSQL for both personal and professional projects, indicating a 20% growth from last year. PostgreSQL's extensibility and familiar SQL interface make it attractive for AI-focused developers, allowing for the integration of advanced features like vector storage. Its scalability and support for modern workloads have led to its adoption across various industries, including IoT, financial technology, and healthcare.

Tech Optimizer

December 17, 2024

Report Reveals: Developers Turn to PostgreSQL for Strong AI Capabilities

Timescale has released its State of PostgreSQL 2024 Report, highlighting PostgreSQL's increasing importance as the preferred database for AI applications. Key findings include: - 78.6% of developers prefer PostgreSQL for its capability to manage various data types, including vectors. - 65% of developers reported an increase in PostgreSQL usage compared to the previous year, a 14-point rise from 2023. - 55.3% of PostgreSQL developers are using AI tools, reflecting a 55% year-over-year growth. - 60% of developers use PostgreSQL for both personal and professional projects, a 20% increase from last year. PostgreSQL is recognized for its stability, extensibility, and user-friendly SQL interface, making it a strong alternative to specialized databases. Developers value its data integration flexibility (78.6%) and ease of use (56%). The report indicates PostgreSQL's adoption across various industries, including finance, healthcare, and IoT, due to its ability to handle transactional and analytical workloads. Timescale is enhancing PostgreSQL's capabilities for AI with innovations like pgai and pgvectorscale.

Tech Optimizer

November 20, 2024

Timescale Bolsters AI-Ready PostgreSQL with pgai Vectorizer

Timescale has introduced the pgai Vectorizer, which allows developers to create, store, and manage vector embeddings alongside relational data within PostgreSQL, eliminating the need for external tools. This tool is part of the TimescaleDB extension for time-series data, enhancing PostgreSQL's AI integration capabilities. The pgai Vectorizer simplifies AI workflows by integrating them directly into PostgreSQL, enabling the rapid development of AI applications using SQL commands. It automatically creates and synchronizes embeddings for text data stored in the database, building upon the pgvector and pgvectorscale extensions. Currently, the pgai Vectorizer supports only OpenAI models, but there are plans to include additional embedding model providers in the future. A pre-built Docker developer environment is available for experimentation with embeddings.