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Big Data News
Siliconangle
7h
182
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Snowflake and Databricks cross the Rubicon into a new competitive domain
- The data industry is experiencing a shift towards evolving from data platforms into true Systems of Intelligence, merging analytic data platforms like Snowflake and Databricks with operational applications.
- The race is on to transition from static dashboards to living systems of intelligence that continuously sense, predict, and optimize business performance, enabling autonomous agents to operate with context and guardrails.
- Key players like Snowflake, Databricks, AWS, and Salesforce are moving towards a new competitive domain, aiming to become leaders in platform strategy rather than engaging in feature wars.
- Enterprises are evolving towards a full-fledged System of Intelligence, reframing the competitive landscape by embedding operational semantics into data layers.
- Snowflake is making strides by building a metric store that could facilitate its transition from an analytic powerhouse to an operational nerve center, while Databricks is establishing its Unity Catalog as a lakehouse control plane for agentic AI.
- AWS is integrating Redshift, Athena, EMR, and Iceberg into SageMaker to offer a cohesive framework, while Salesforce is monetizing feedback loops to transform customer events into autonomous actions.
- The market is witnessing a shift towards outcome-based billing models, with vendors like Salesforce and ServiceNow pricing the control loop itself, potentially expanding the market.
- The journey to a full 4-D business model will take time, with companies vying to control the metric tree, orchestrate agents, and steer enterprise operations.
- As Snowflake Summit and Databricks Data+AI Summit approach, vendors will showcase their green-layer ambitions and roadmap milestones towards owning the system of intelligence.
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Amazon
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PackScan: Building real-time sort center analytics with AWS Services
- PackScan is a cloud-based platform developed by Amazon for real-time analytics in sort centers, enabling immediate access to operational data for optimization and decision-making.
- It provides real-time visibility into package movements, container statuses, and associate productivity critical for logistics operations in high-volume sort centers.
- Amazon Middle Mile team designed PackScan to reduce data latency, allowing proactive decision-making to monitor package flow, optimize shipments, track productivity, and enhance efficiency in real time.
- The solution deploys multiple AWS services like Amazon SNS, Amazon SQS, AWS Lambda, Amazon Data Firehose, and Amazon OpenSearch Service to deliver real-time insights across the network.
- PackScan's data flow involves capturing events at inbound stations through barcode scanners, processing through Lambda, enriching via Data Firehose, and indexing in OpenSearch Service for real-time analytics.
- The architecture is serverless, event-driven, dynamically scaling based on data ingestion volumes to provide low-latency analytics and high scalability.
- The implementation of PackScan led to reduced data latency, proactive operational management, enhanced workforce productivity, and significant improvements in operational efficiency within Amazon's sort centers.
- Lessons learned from the deployment emphasize the efficiency of cloud architecture, real-time visibility's impact on decision-making, and the importance of continuous monitoring for informed decisions.
- Monthly cost estimates for key AWS services used in PackScan implementation showcase the cost-effectiveness and scalability of cloud-based solutions.
- Future enhancements for PackScan include integrating predictive analytics to optimize resource allocation and scaling the solution for additional operational scenarios.
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Siliconangle
2d
192
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ClickHouse reels in $350M for its high-speed columnar database
- ClickHouse Inc. has secured $350 million in a Series C funding round led by Khosla Ventures, bringing its total funding to over $650 million.
- ClickHouse's database, based on a columnar architecture, optimizes data retrieval by storing columns adjacent to each other for faster query processing.
- The database includes performance optimizations like vectorized query execution, range query simplification, and the ability to handle massive scales of data.
- ClickHouse plans to use the new capital to accelerate product development and expand its global presence, serving over 2,000 customers including major tech firms like Sony Corp. and Anthropic PBC.
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Amazon
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How Airties achieved scalability and cost-efficiency by moving from Kafka to Amazon Kinesis Data Streams
- Airties, a wireless networking company, transitioned from Kafka to Amazon Kinesis Data Streams to enhance scalability and cost-efficiency.
- Airties focuses on AI-driven solutions for home connectivity, offering proactive customer care and network optimization through Airties Home.
- By leveraging AWS managed services, Airties improved Wi-Fi experiences, reduced help desk calls, and cut operational costs.
- The migration to Kinesis Data Streams allowed for serverless operations, automatic scaling, and cost reduction.
- Challenges with the previous Kafka-based architecture included operational overhead, scaling complexities, and cluster capacity issues.
- Kinesis Data Streams brought auto-scaling capabilities, stream isolation, dynamic shard management, and Application Auto Scaling for Airties.
- Airties implemented a decoupled architecture for historical data access, utilizing Kinesis Data Streams, Amazon S3, and Amazon DynamoDB.
- The new architecture provided scalability, performance, reliability, operational efficiency, and cost optimization benefits for Airties.
- The transition resulted in a 33% reduction in infrastructure costs, enhanced system reliability, and reduced environmental impact.
- The move to Kinesis Data Streams showcases Airties' commitment to operational excellence, sustainability, and efficient operations on AWS.
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Siliconangle
3d
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Hex raises $70M to expand AI-powered data analytics platform
- Hex Technologies Inc. raises $70 million in new funding to expand operations and enhance its AI-powered data analytics platform.
- Founded in 2020 by former Palantir Inc. employees, Hex offers a collaborative data workspace aimed at improving data utilization in organizations.
- Hex's platform features a notebook-based interface supporting SQL, Python, and no-code inputs, along with an App Builder for creating interactive data applications without additional coding.
- The new funding, totaling $70 million, brings Hex's total raised amount to $172 million, with notable customers including Reddit, Cisco, NBA, and others.
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Amazon
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Unlock self-serve streaming SQL with Amazon Managed Service for Apache Flink
- Riskified, an ecommerce fraud prevention platform, utilizes AI/ML for real-time transaction data analysis to prevent fraud and optimize approval rates.
- Riskified uses streaming technologies like Apache Flink, Spark, and Kafka Streams for instant decision-making and enhancing customer experiences.
- Riskified introduced self-serve streaming SQL pipelines to enhance analytical precision in detecting fraudulent transactions.
- SQL streaming jobs enable real-time data processing with minimal complexity, enhancing fraud detection strategies like velocity checks.
- Riskified faced limitations with ksqlDB and transitioned to Apache Flink for better schema evolution management and pipeline efficiency.
- Managed Service for Apache Flink offers benefits such as job isolation, automatic scaling, and reduced resource contention for efficient streaming.
- Riskified implemented Flink's DataStream API to overcome serialization limitations, optimizing fraud detection strategies.
- CI/CD processes with Managed Service for Apache Flink ensure efficient build and deployment, supporting gradual deployment to different environments.
- Riskified developed a self-service interface for submitting SQL jobs using AWS SDK, enabling flexible Flink application configuration.
- The transition to Flink enhanced schema evolution, resource management, observability, and recovery mechanisms for improved fraud detection and streaming efficiency.
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Silicon
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168
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Salesforce Acquires Informatica For $8 Billion
- Salesforce has announced the acquisition of Informatica for $8 billion in equity value, net of Salesforce’s current investment in Informatica.
- Informatica, a leader in enterprise AI-powered cloud data management, will be acquired by Salesforce to strengthen its data management and integration capabilities.
- Informatica shareholders will receive $25 per share, a premium of about 11 percent from the closing price of $22.55.
- The acquisition aims to enhance Salesforce's data foundation and establish a unified architecture for agentic AI across the enterprise.
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Siliconangle
3d
160
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Monte Carlo turns its gaze to unstructured data quality issues
- Monte Carlo Data Inc. introduces new capability to monitor unstructured information, including text, images, video, and audio files for data quality.
- Up to 90% of data stored on an enterprise's servers is unstructured information, leading to reliability issues due to lack of easy monitoring for data quality.
- Monte Carlo specializes in data observability, offering tools to ensure dataset quality and applying machine learning algorithms to detect abnormal behavior in data streams.
- The company now focuses on unstructured data, crucial for AI applications, allowing customizable checks for data reliability, consistency, and accuracy.
- Monte Carlo aims to enable companies to trust unstructured data by validating AI model outputs and detecting sensitive information in texts.
- Analyst Michael Ni sees a trend toward consolidation in AI and data observability markets, emphasizing the importance of observing unstructured data for AI trust.
- The move towards data observability for unstructured data signifies a shift in trust in AI and marks the beginning of consolidated decision observability.
- Monte Carlo's new tool integrates with platforms like Snowflake, Databricks, and Google BigQuery, enhancing data reliability for AI-driven insights.
- The company's mission includes providing visibility across the full data and AI application lifecycle, with a focus on ensuring the reliability of foundational data.
- Monte Carlo's advancement in unstructured data monitoring is seen as a significant step in enhancing AI trust and data observability in enterprise environments.
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Amazon
4d
234
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Unify streaming and analytical data with Amazon Data Firehose and Amazon SageMaker Lakehouse
- To bridge the gap between streaming data and analytical workloads, organizations can leverage Amazon Data Firehose and Amazon SageMaker Lakehouse.
- Streaming data enables real-time insights and dynamic responses crucial for applications needing immediate, adaptable responses.
- Amazon Data Firehose simplifies streaming data delivery to various data platforms with automatic scaling and real-time delivery.
- Amazon SageMaker Lakehouse unifies data sources, providing flexibility and access to Iceberg-compatible tools for analytics.
- By using SageMaker Lakehouse, organizations can combine Iceberg's capabilities with cloud scalability for improved analytics workflows.
- The integration removes barriers between data storage and ML processes, allowing direct work with Iceberg tables.
- The solution demonstrates creating Iceberg tables in SageMaker Unified Studio and streaming data via Firehose for collaboration across teams.
- AWS CloudFormation templates help with setting up resources for Firehose to deliver streaming data to Iceberg tables.
- Prerequisites include an AWS account, SageMaker Unified Studio domain, and creation of a demo project for this walkthrough.
- Steps involve creating Iceberg tables, deploying necessary resources, setting up a Firehose stream, and generating and querying streaming data.
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Siliconangle
4d
170
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Heatseeker AI raises $1.5M in funding for its market research platform
- Heatseeker AI has raised $1.5 million in pre-seed funding to enhance its market research platform.
- The funding was led by Capital F with participation from Euphemia, Even Capital, and East End Ventures.
- Heatseeker's platform relies on online ads to gather user feedback for market research, offering a more reliable source than traditional surveys.
- The company plans to use the funding to launch a tool called Synthetic Experiments, leveraging AI to accelerate market research programs and reduce costs.
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Siliconangle
2d
118
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Exclusive: Anomalo expands data quality insights to unstructured information
- Anomalo Inc. has launched a new Unstructured Data Monitoring tool to monitor unstructured data for enterprises, building on its flagship data quality platform.
- The tool helps in spotting issues within massive unstructured data volumes stored in various locations like text files and images.
- Anomalo's expertise now extends to managing unstructured data in cloud data warehouses and data lakes, ensuring trust in all data types.
- Unstructured data comprises around 80% of records in most companies, making it a significant focus area for data quality and AI.
- The tool, including Anomalo Workflows, automates the identification and correction of quality issues in unstructured data.
- It can analyze up to 100,000 documents in one operation, offering a scalable solution for handling large volumes of unstructured information.
- Anomalo aims to help companies extract insights from unstructured data and convert it into clean datasets for AI model training.
- The tool facilitates analysis of support tickets, call logs, social media comments, and more to derive meaningful insights for businesses.
- Anomalo's tool launch follows a similar move by Monte Carlo Data Inc., signaling a rapid consolidation phase in the AI and data observability markets.
- This advancement in unstructured data monitoring signifies a shift towards enhancing trust and value extraction from unstructured data for enterprises.
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Precisely
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122
4 Steps to Transform Your Address Data in Snowflake with the Precisely Geo Addressing Native App
- Address data is crucial for data-driven decision-making but can be challenging to manage effectively.
- The Precisely Geo Addressing Native App in Snowflake helps standardize and enrich address data.
- Steps include setting up Snowflake, installing the app from the Marketplace, and geocoding addresses.
- The app allows forward and reverse geocoding, address verification, and includes the PreciselyID.
- By leveraging the PreciselyID, users can enrich address data with additional insights like wildfire risk assessment.
- The Precisely Wildfire Risk dataset can be integrated with geocoded addresses for in-depth analysis.
- Using PreciselyID simplifies integrating multiple datasets and enhances data analysis capabilities.
- The app empowers users to make better data-driven decisions by leveraging its geocoding and verification features.
- It enables users to enhance logistics, assess risks accurately, and enrich datasets efficiently.
- Elevate location intelligence within Snowflake by leveraging the app's capabilities for improved decision-making.
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Siliconangle
3d
331
Image Credit: Siliconangle
Memory for the machine: How vector databases power the next generation of AI assistants
- Aquant Inc. used a vector database, Pinecone, to ground its AI models with real-time knowledge for its AI-powered platform.
- Vector databases act as a semantic memory for AI, enabling assistants to understand context and intent, making them critical for modern AI applications.
- They allow for real-time search in vast data collections, aiding in interpreting human conversations and surfacing insights from unstructured data.
- Vector databases have become essential for AI agents by providing situational awareness and on-demand memory for decision-making.
- Their roots lie in similarity search, using multidimensional data to understand subject matter, such as text, images, or audio.
- The vector database market is growing rapidly, projected to reach $10.6 billion by 2032, driven by the demand for AI-driven applications across industries.
- Enterprises are adopting vector databases to support AI assistants, and are exploring their use in indexing images, audio, and video for multimodal AI applications.
- Vector databases are being used to facilitate structured knowledge graphs for intelligent querying, aiding in different industries like finance and document processing.
- They are crucial for AI agents to have access to real-time data for accurate responses, and are evolving to support multi-agent systems for autonomous decision-making.
- Major providers are integrating vector databases with existing technologies, opening up new possibilities for AI applications across various domains.
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Precisely
4d
73
Smart Banking in 2025: The Intelligent Technologies Defining CX and Operations
- Banks focusing on agility and data-driven customer-centricity are thriving in the evolving financial landscape.
- The webinar 'Smart Banking in 2025' discussed the shifts in banking towards digital transformation, compliance, and customer experience.
- Challenges include fragmented systems, siloed data, and lack of system integration hindering omnichannel customer engagement.
- The key solutions highlighted were integration of communication systems, data consolidation for personalized experiences, and centralized customer data for future-ready operations.
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Precisely
4d
381
Smart Banking: The Intelligent Technologies Defining CX and Operations
- In a recent American Banker webinar, the focus was on smart banking and intelligent technologies defining customer experience and operations in the financial sector.
- Banks need to prioritize agility, data-driven customer-centricity, and digital transformation to thrive in an evolving market.
- Integrating communication systems, consolidating data, and focusing on data-driven personalization are key strategies for enhancing customer service and loyalty.
- The session concluded with a call to focus on clean, consolidated data, building empowering engagement systems, and keeping the customer at the center to drive agility and success in the banking industry.
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