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Big Data News
Siliconangle
2w
362
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Vast Data adds vector search and serverless functions to its scalable data platform
- Vast Data has announced enhancements to its scalable data platform, allowing support for structured and unstructured data in a single DataSpace with linear and secure scaling.
- The new capabilities include real-time vector search, fine-grained security, and event-driven processing in a platform called Vast InsightEngine.
- The enhancements aim to enable the building of AI applications, agentic workflows, and high-speed inferencing pipelines more easily.
- Vast Data's platform is the first and only vector database that supports trillion-vector scale, allowing indexing of all data and enabling real-time retrieval for agentic workflows.
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Amazon
3w
235
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Enhancing Adobe Marketo Engage Data Analysis with AWS Glue Integration
- Adobe Marketo Engage offers a comprehensive marketing hub with AI-driven personalization, automation, and real-time analytics for B2B marketers.
- AWS introduced SaaS connectivity for Marketo Engage through AWS Glue, simplifying the analytics pipeline and enabling data-driven decisions.
- The integration of AWS Glue with Marketo Engage enhances marketing capabilities, allowing businesses to extract greater value from marketing data.
- Benefits include marketing-sales alignment, enhanced analytics, data integrity, improved lead quality, and a unified customer view.
- The solution involves using AWS Glue to extract Marketo Engage data for processing and enrichment on AWS, enabling data-driven marketing workflows.
- Key steps include creating AWS resources, setting up AWS Glue connections, creating ETL jobs using AWS Glue Studio, and analyzing the data.
- Prerequisites for the integration include a Marketo Engage account and an AWS Glue database.
- The integration process involves creating an S3 bucket, setting up a Marketo Engage connection, and creating ETL jobs for data processing.
- To analyze the data, users can run queries using tools like Amazon Athena to gain insights into customer behavior and campaign performance.
- Upon completion, users can clean up resources to avoid charges and leverage the streamlined data integration benefits for informed decision-making.
- The AWS Glue connector for Marketo Engage enhances data synchronization and advanced analytics, driving better business outcomes for marketers.
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Hackaday
3w
161
Image Credit: Hackaday
Satellite Imagery You Can Play With
- Satellogic operates a series of CubeSats with Earth imaging payloads.
- They offer an open dataset of satellite imagery.
- A script is used to recover the locations of satellites.
- The imagery can be explored using the provided tools.
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Amazon
3w
240
Image Credit: Amazon
Unlock the power of optimization in Amazon Redshift Serverless
- Amazon Redshift Serverless introduces AI-driven scaling and optimization, measuring compute capacity in Redshift Processing Units (RPUs) and considering query complexity and data volume for efficient resource allocation.
- The AI-driven scaling feature prevents over-provisioning of resources and under-provisioning, crucial for workloads with fluctuating demands based on daily or monthly cycles.
- Users can configure workgroups in Amazon Redshift Serverless by setting base RPUs or opting for a price-performance target, offering enhanced flexibility in resource allocation.
- Intelligent resource management in Amazon Redshift Serverless adjusts resources during query execution for optimal performance, particularly for workloads requiring 32 to 512 base RPUs.
- Five optimization profiles ranging from cost-focused to performance-focused allow users to balance price and performance goals, catering to various workload requirements.
- The AI-driven scaling and optimization in Amazon Redshift Serverless benefit analytical workloads with high variability by learning workload patterns and optimizing resources for improved price-performance.
- Measurement of current price-performance using sys_query_history and sys_serverless_usage helps in evaluating the effectiveness of the AI-driven scaling and optimization in Amazon Redshift Serverless.
- In benchmark tests using TPCDS 3TB dataset, different optimization profiles (Optimized for Cost, Balanced, Optimized for Performance) demonstrated varied performance and cost trade-offs.
- Results showed that the Balanced configuration delivered better performance at a slightly higher cost compared to Optimized for Cost, while the Optimized for Performance configuration achieved fastest query times with increased costs.
- The optimization for cost configuration limits resources to save money, the balanced configuration provides moderate resource allocation, and the performance-focused configuration maximizes resource usage for faster query delivery.
- Amazon Redshift Serverless AI-driven scaling and optimization provides optimal resource allocation for various workload requirements, helping organizations achieve a balance between cost efficiency and performance improvements.
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Amazon
3w
229
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Express brokers for Amazon MSK: Turbo-charged Kafka scaling with up to 20 times faster performance
- Amazon Managed Streaming for Apache Kafka (Amazon MSK) Express brokers simplify Kafka deployment and scaling, offering exceptional performance and operational simplicity.
- MSK Express brokers provide up to 3 times more throughput per broker, sustaining impressive data streaming performance on m7g.16xl instances.
- Key features include fast scaling (up to 20 times faster than standard brokers), 90% faster recovery, and built-in three-way replication for reliability.
- Express brokers eliminate storage management responsibility, offer unlimited storage without pre-provisioning, and adhere to Kafka APIs.
- Traditional Kafka deployment faces limitations like extended recovery times, suboptimal load distribution, and complex scaling operations.
- MSK Express brokers decouple compute and storage, ensuring faster and more reliable broker recovery, efficient load balancing, and faster scaling.
- A scaling use case example with MSK Express brokers demonstrated rapid, safe scaling without disruption, completing in just 28 minutes.
- Best practices for adopting MSK Express brokers include starting with larger instance types for high-throughput workloads and following best practices.
- MSK Express brokers offer simplified operations, superior performance, and rapid scaling capabilities, making them an attractive option for Kafka deployments.
- With advantages like higher throughput, faster scaling, and quicker recovery times, MSK Express brokers cater to organizations' real-time data processing needs.
- Masudur Rahaman Sayem, a Streaming Data Architect at AWS, provides expertise in designing large-scale distributed systems for optimal performance and scalability.
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Mysql
3w
110
Image Credit: Mysql
Oracle Technology Roundtable for Digital Natives – Let’s have a look at AI, Cloud and HeatWave
- The Oracle Technology Roundtable for Digital Natives took place in Zurich, focusing on AI, Cloud, and HeatWave, with features like generative AI, machine learning, vector processing, analytics, and transaction processing across data in Data Lake and MySQL databases.
- Key sessions included discussions on Oracle AI adoption stages, HeatWave's benefits in data processing, and building next-gen applications with Generative AI and Vector Store.
- AI's effectiveness depends on the quality of data managed securely. HeatWave offers a single platform for various workloads, including data analytics and machine learning.
- HeatWave's advantages include unchanged SQL syntax, automatic data propagation, query performance, efficient Data Lake processing, and multi-cloud availability.
- Oracle Cloud for Digital Natives was highlighted for developer-first approach, advanced Data & AI services, technical reach, security features, and cost efficiency.
- HeatWave's role in Data Lakehouse enables near real-time querying of data, simplifying complex data management processes and improving overall performance.
- The event emphasized the importance of not overlooking critical factors like security, reliability, availability, and best practices amidst evolving technologies like AI and machine learning.
- Oracle HeatWave was recommended for mixed workloads, AI projects, and performance enhancements, with a free trial option available.
- The event concluded with a focus on embracing innovations in AI and addressing crucial aspects like security and best practices with the support of dbi services and Sequotech.
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Amazon
3w
388
Image Credit: Amazon
Build a secure data visualization application using the Amazon Redshift Data API with AWS IAM Identity Center
- The Amazon Redshift Data API simplifies access to Amazon Redshift, providing better price-performance and throughput for data analytics at scale.
- The support for single sign-on and trusted identity propagation in Amazon Redshift Data API enables building secure data visualization applications with role-based access control.
- By using IAM Identity Center and trusted identity propagation, users can authenticate with corporate credentials and manage application-level access control efficiently.
- The example scenario of a global sports gear company illustrates restricting data access based on user roles and regions for data visualization.
- Components like IAM Identity Center, Okta as an external IdP, Amazon Redshift Data API, and RBAC in Amazon Redshift power the data visualization application.
- Streamlit is used to create a user-friendly interface for accessing and analyzing sales data securely based on user roles and permissions.
- The solution architecture involves a workflow where users authenticate through Okta, obtain temporary IAM session credentials, and access Amazon Redshift for data.
- The setup includes provisioning resources for IAM Identity Center, Amazon Redshift, and Okta, configuring Redshift RBAC for row-level security, and creating a Streamlit application.
- Prerequisites include an AWS account, IAM Identity Center enabled, an external IdP like Okta set up, and a Python virtual environment for development.
- The process involves creating user groups in Okta, setting up IAM Identity Center with Okta, creating and configuring Amazon Redshift IAM Identity Center connection application, and provisioning a Redshift Serverless workgroup.
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Precisely
3w
269
Image Credit: Precisely
5 Reasons to Use APIs to Unleash Your Data
- Data quality and contextual depth are crucial for effective data-driven decision-making in the modern global economy, with data enrichment playing a vital role.
- APIs enhance data quality by linking internal data to external sources, ensuring trustworthy and updated information for decision-making.
- Using APIs for data enrichment leads to time and cost efficiencies, as real-time updates free up resources for strategic initiatives.
- APIs allow for customization and scalability, enabling companies to adapt data enrichment to their specific needs and integrate it into core business applications.
- Accessing diverse data sources through APIs provides comprehensive insights for informed decision-making, particularly crucial in sectors like finance and healthcare.
- APIs simplify compliance with data privacy laws when enriching data, reducing the risks associated with breaches and regulatory actions.
- Precisely's Data Integrity Suite offers APIs that automate data preparation and enriching activities, providing accurate, contextual data for enhanced decision-making.
- Precisely's Geo Addressing APIs enable capturing, cleansing, and enriching addresses for more informed decisions, while the Data Graph API facilitates access to various datasets for enrichment.
- Precisely APIs streamline the process of data cleansing, consolidation, and enrichment, offering organizations a unified framework for driving competitive advantage through data-driven decisions.
- By leveraging APIs for data enrichment, organizations can optimize their data processes, enhance decision-making, and stay competitive in the evolving technological landscape.
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Designveloper
3w
172
Image Credit: Designveloper
What Is Big Data Analytics and How It Useful for Business?
- Big Data analytics involves the systematic use of computers to analyze large amounts of data from various sources to spot hidden patterns or trends.
- Big Data is characterized by the three Vs: volume, velocity, and variety, with additional Vs like Variability, Veracity, and Value.
- Big Data Analytics comprises nine stages, starting from business case evaluation to using analysis results to make informed decisions.
- There are four types of Big Data Analytics: Descriptive, Diagnostic, Predictive, and Prescriptive Analytics.
- Businesses utilize Big Data Analytics to increase customer satisfaction, enhance work efficiency, and reduce costs.
- Companies like Shopee and Google use Big Data Analytics to optimize services, make tailored suggestions, and understand user behavior.
- The growing importance of Big Data Analytics has led to an estimated market value of $90 billion by 2025.
- The adoption of AI/ML in Big Data Analytics helps automate data manipulation processes and analyze both structured and unstructured data.
- Data-related jobs like data analysts and data scientists are in demand worldwide, offering attractive average salaries.
- Understanding Big Data Analytics and honing relevant skills are crucial for businesses to leverage this gold mine of information effectively.
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Amazon
3w
397
Image Credit: Amazon
Cross-account data collaboration with Amazon DataZone and AWS analytical tools
- Data sharing is crucial for innovation, growth, and collaboration, with organizations promoting it outperforming peers as per a Gartner study.
- Amazon DataZone enables cataloging, discovering, and sharing data across AWS, solving challenges like managing permissions and data discovery across accounts.
- The solution allows cross-account data collaboration, utilizing AWS analytical tools like Amazon Athena and Redshift query editor.
- Data administrators, data publishers, and data subscribers play key roles in creating, publishing, and consuming data assets in this setup.
- Prerequisites for setting up cross-account access include two AWS accounts, Amazon Redshift cluster, and AWS Secrets Manager for storing credentials.
- Amazon DataZone leverages AWS Resource Access Manager for domain associations, facilitating automatic association for accounts in the same organization.
- Steps involve setting up DataZone domain, requesting domain association, creating projects for Glue and Redshift, and subscribing to data assets.
- Creating AWS Glue and Redshift environments, publishing data assets, setting up environment profiles, and subscribing to tables are integral parts of the process.
- The solution aims to simplify cross-account data sharing, ensuring reliable access, consistent governance, and utilizing AWS Glue and Amazon Redshift for insights and decision-making.
- Key authors involved in the post are Arun Pradeep Selvaraj, Piyush Mattoo, and Mani Yamaraja, who specialize in solutions architecture and customer-centric technology solutions at AWS.
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Atlan
3w
376
Image Credit: Atlan
Data Governance in the AI Era: 3 Big Problems and How to Solve Them
- The Great Data Debate focused on the challenges of data governance in the AI era.
- Key experts including Tiankai Feng, Sunil Soares, Sonali Basak, Bojan Simic, and Brian Ames discussed evolving governance needs.
- Three major data governance problems were highlighted during the debate.
- The first problem discussed was data governance being treated as an afterthought, rather than a proactive approach from the start.
- The panel emphasized the need to integrate governance into processes early on and tie it to business outcomes to shift from a reactive to proactive approach.
- AI's introduction has made governance more challenging by amplifying flaws in data and creating new risks like data bias and lack of explainability.
- To govern AI effectively, organizations need proactive AI governance strategies, automation, and clear policies defining AI boundaries.
- Another key issue highlighted was the resistance to traditional governance methods due to their manual, slow, and disconnected nature.
- To make governance seamless, experts suggested automating processes, integrating governance tools into existing workflows, and leveraging AI to reduce manual efforts.
- The importance of embedding governance into daily workflows, letting AI govern AI, tying governance to business impact, and investing in AI governance was underlined in the debate.
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Siliconangle
3w
106
Image Credit: Siliconangle
TigerGraph adds hybrid search capability to its graph database, releases free edition
- TigerGraph has added hybrid search capability to its graph database.
- The feature combines graph-based tools for finding connections between data points with a vector search capability.
- This upgrade enables AI applications powered by TigerGraph's database to retrieve information more reliably.
- The company also released a free edition of its database, named TigerGraph DB Community Edition.
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Dzone
4w
8
Image Credit: Dzone
AI Agents for Data Warehousing
- Data warehousing involves storing data from various sources within an organization for reporting, decision-making, and analytics.
- Traditional data warehousing faces challenges like high costs, slow processing, and scalability issues.
- DW Agent AI, powered by artificial intelligence, is revolutionizing data management by automating and optimizing data warehousing processes.
- AI agents enhance ETL/ELT automation, query optimization, and advanced analytics in data warehousing.
- Google Cloud offers advanced data warehousing capabilities using AI services like BigQuery and Cloud Dataflow.
- AI agents assist in ETL automation by detecting schema changes, data transformation, incremental load optimization, and data quality assurance.
- Data analytics with AI agents involve predictive modeling, anomaly detection, and real-time reporting for improved decision-making.
- Practical implementation of AI agents includes optimizing queries, natural language data interaction, and system optimization for efficient data processing.
- Benefits of AI agents in data warehousing include reduced manual effort, improved accuracy, scalability, and cost-effectiveness.
- The future of AI in data warehousing holds promise for more advanced automation, improved self-optimization, and enhanced decision-making capabilities.
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Amazon
4w
173
Image Credit: Amazon
Amazon OpenSearch Service vector database capabilities revisited
- Amazon OpenSearch Service has evolved since 2023, with improved performance, cost-effectiveness, and new features for hybrid search methods using dense and sparse vectors.
- In 2024, there was a shift towards production use of Retrieval Augmented Generation (RAG) applications and semantic search workloads to enhance relevance.
- 2025 brings support for OpenSearch 2.17, featuring enhancements focused on lowering costs, reducing latency, and improving search accuracy.
- OpenSearch Service offers a vector database supporting FAISS, NMSLIB, and Lucene engines for exact and approximate nearest-neighbor matching with various distance metrics.
- Builders are adopting a hybrid search approach combining lexical and semantic retrieval methods to cater to diverse user queries effectively.
- OpenSearch improved hybrid search capabilities in 2024 through conditional scoring logic, optimized structures, and parallel query processing, reducing latency and post-filtering for refined results.
- Sparse vector search simplifies the integration of lexical and semantic information, enhancing query processing latency in 2024.
- OpenSearch introduced strategies in 2024 to reduce costs for production workloads, including scalar and binary quantization, in-memory handling optimizations, and support for JDK21 and SIMD instruction sets.
- Innovations like k-NN query updates, chunking strategies, and reduced RAM consumption methods contribute to improved accuracy and efficiency in 2024.
- OpenSearch's focus on dense vector handling, cost reduction through quantization, and support for AI-native pipelines highlights a commitment to advancing search AI use cases and integrations.
- Overall, OpenSearch continues to enhance its capabilities for semantic search and vector databases, offering builders powerful, scalable solutions for AI-driven applications.
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Precisely
4w
204
Image Credit: Precisely
Data and Process Automation Adoption: Challenges, Maturity, and Business Impact
- Data and process automation adoption is essential for businesses running on SAP®, with challenges such as complexity, integration, and stakeholder alignment needing to be navigated for success.
- The value of automation increases with maturity, providing benefits like time and cost savings initially, and enhancing agility, resilience, and competitiveness in advanced stages.
- Data quality and governance play a crucial role in automation, impacting business outcomes and AI initiatives if not appropriately maintained.
- The top challenges for data and process automation adoption include complexity of business processes and data, integration with existing systems, misalignment between stakeholders, building a business case, and ensuring data quality and governance.
- The 2024 survey conducted in partnership with ASUG showed that only 5% of companies have achieved high automation adoption levels, with many relying on a mix of automated and manual SAP processes.
- Challenges like complexity, data-intensive processes, and stakeholder alignment hinder automation efforts, while the need for establishing clear business rules is vital for success.
- Integration with existing systems poses a challenge, especially when dealing with multiple systems of record like Salesforce, ServiceNow, and MDM alongside SAP system data.
- Misalignment between technical and business stakeholders, building a compelling business case for automation, and maintaining data quality and governance are key hurdles to successful automation implementation.
- Benefits of task and process automation include increased efficiency, cost savings, improved data quality and compliance, enhanced business agility, and better decision-making with real-time insights.
- Levels of automation maturity progress from individual role automation to organizational optimization, with a focus on business impact, increased agility, and optimized outcomes as automation adoption grows.
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