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Big Data News
TechBullion
1w
47
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Analytics Insight Predicts Explosive Growth in India’s Data Science Education Market, Projects US$4.40 Billion by 2030
- Analytics Insight projects explosive growth in India's data science education market, estimating it to reach US$4.40 billion by 2030.
- The report emphasizes how Big Data is transforming the academic world, enhancing system efficiency, addressing learning gaps, and supporting personalized education.
- By 2030, the global Data Science Education market is expected to reach US$67.51 billion, with India contributing approximately 10% of this market.
- The rising popularity of specialized degree programs and certifications, along with the increase in job postings requiring data science skills, is fueling the growth of data science education.
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Precisely
2w
231
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Building a Foundation of Trust: How to Improve the Quality of Your Critical Data
- Inaccurate data undermines analytics, drives up costs, and damages customer trust.
- Implement core processes like validation, enrichment, entity resolution, and reconciliation to reduce risk and improve operational efficiency.
- For long-term success, build a data quality strategy that combines the right tools with clear goals, ownership, and metrics.
- High-quality data improves operations, enables smarter decisions, better customer experiences, and lower operational costs.
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TechBullion
2w
380
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18 Surprising Ways Data Analytics Uncovered Hidden Revenue Opportunities
- Data analytics can uncover hidden revenue opportunities through unexpected insights and analysis across various industries.
- For example, analyzing delivery data and weather patterns led to a 43% increase in online orders for a seafood business during monsoon season.
- Identifying and addressing issues like incomplete checkout processes for specific regions can lead to significant revenue growth by tapping into untapped markets.
- Small changes in strategies, such as personalized email offers for repeat customers, can result in a 20% increase in repeat purchases and overall revenue.
- Analyzing customer behavior data can reveal unexpected growth opportunities, such as targeting specific user segments with personalized discounts.
- Data analysis can uncover hidden revenue streams, like replacing underperforming assets with better alternatives leading to a 13% boost in select portfolios.
- Unearthing trends like midnight purchases driven by unique content can result in new revenue streams, like the 'pajama conversion' audience.
- Optimizing regional messaging and checkout experiences based on data insights can lead to an 18% increase in conversions through partner/referral funnels.
- Analyzing user engagement data can reveal valuable insights, such as the impact of billing feature adoption on platform retention and revenue growth.
- Reallocating resources based on integrated reporting can lead to a 15-25% increase in revenue by leveraging mid-funnel campaigns.
- Uncovering anomalies and underutilized features can drive revenue growth and improve customer engagement, emphasizing the importance of leveraging data insights.
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Amazon
2w
142
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How LaunchDarkly migrated to Amazon MWAA to achieve efficiency and scale
- LaunchDarkly migrated to Amazon MWAA to scale their internal analytics platform, handling up to 14,000 tasks per day with minimal cost increase.
- Challenges faced included difficulties with time to integrate new AWS services, data locality issues, and non-centralized orchestration across engineering teams.
- The solution involved a central Amazon MWAA environment for orchestration, ECS cluster for running tasks, CloudWatch and Datadog for monitoring, and more.
- Migration involved transitioning Airflow code from 1.12 to 2.5.1, faced issues with custom operator dependencies, and improved isolation by moving tasks to ECS on Fargate.
- Upgrade to Airflow 2 was successful, with minimal cost increase due to ECS usage, delivering improved monitoring and observability with Datadog and CloudWatch.
- Scaling beyond internal analytics, LaunchDarkly used Amazon MWAA as a generic orchestrator for various services, enhancing scalability and onboarding speed.
- Lessons learned included the importance of isolation for reliability, monitoring, and observability, leading to successful scaling to 14,000 tasks per day.
- Future plans involve further integration with AWS services like Lambda and SQS to automate data workflows, supporting greater scalability as the company expands.
- By adopting managed services like Amazon MWAA and best practices, LaunchDarkly accelerated innovation, mitigated risks, and improved time-to-value of product offerings.
- Organizations looking to modernize data pipelines should assess current setups, explore Amazon MWAA capabilities, and leverage containerization for improved workflows.
- Authors include Asena Uyar, Dean Verhey, and Daniel Lopes, discussing their journey of orchestrating data pipelines at scale with Amazon MWAA and Amazon ECS.
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Amazon
2w
160
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Simplify enterprise data access using the Amazon Redshift integration with Amazon S3 Access Grants
- Organizations face challenges in managing fragmented access rights across different AWS services like Amazon S3 and Amazon Redshift, leading to overhead in permission management and collaboration between security and data owners.
- The Amazon S3 Access Grants integration with Amazon Redshift offers centralized user authentication through AWS IAM Identity Center, simplifying access management with grants for specific users or groups.
- The solution involves enabling AWS Organizations, configuring IAM Identity Center, using multiple member accounts for Redshift and S3, and establishing secure data access based on IAM Identity Center users and groups.
- Steps include enabling S3 Access Grants, updating IAM role permissions, creating S3 bucket and IAM policy, setting up S3 Access Grants, allowing cross-account resource access, and creating Redshift tables.
- Integration testing involves unloading data from Amazon Redshift to Amazon S3 and loading it back, ensuring access control through S3 Access Grants while simplifying permissions management.
- The solution aims to streamline data access for organizations managing large amounts of data across multiple business units, with a focus on a simpler ETL process and centralized access management.
- IAM Identity Center users can run queries in Amazon Redshift with temporary S3 access credentials provided by S3 Access Grants, maintaining security and simplifying permissions for data operations.
- CloudTrail logs capture IAM Identity Center related operations for audit purposes, ensuring visibility into user access and permissions management in the integrated environment.
- Cleanup steps include deleting IdP applications, IAM Identity Center configurations, Redshift resources, IAM roles, S3 bucket, and associated S3 Access Grants instance.
- The integration of Amazon Redshift with S3 Access Grants using IAM Identity Center offers a robust, secure analytics environment with simplified data access for business users across different accounts.
- Additional resources and documentation are provided for further guidance on integrating IAM Identity Center with various services for streamlined authentication and data access.
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Amazon
2w
277
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Access Amazon Redshift Managed Storage tables through Apache Spark on AWS Glue and Amazon EMR using Amazon SageMaker Lakehouse
- Data-driven organizations are adopting lakehouse solutions for simplified data management and access to data from various engines.
- Amazon SageMaker Lakehouse unifies data from Amazon S3 and Amazon Redshift to power analytics and AI/ML applications.
- SageMaker Lakehouse allows in-place data access across third-party sources through Amazon Athena.
- Access Amazon Redshift Managed Storage (RMS) tables via Iceberg APIs using AWS Glue Data Catalog.
- Integration steps for Apache Spark on AWS Glue and Amazon EMR to access RMS tables are described.
- Configurations for accessing RMS databases using Spark session catalog configurations are outlined.
- Create a Lakehouse catalog for RMS and manage data access through SageMaker Unified Studio and AWS Glue.
- Demonstration on querying RMS tables via SageMaker Unified Studio using Spark and Iceberg REST catalog.
- Instructions for creating, accessing, and querying tables in RMS Lakehouse catalog are provided.
- Cleaning up resources post demo implementation to prevent future charges is highlighted.
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Precisely
2w
429
Image Credit: Precisely
ExxonMobil, Weber Metals, and Celanese Share Their SAP® Automation Successes: An Inspiration Days Recap
- ExxonMobil, Weber Metals, and Celanese shared their SAP automation successes at an Inspiration Days event by Precisely.
- At the event, attendees networked, learned, and exchanged real-world results and insights, along with exclusive presentations and Q&A sessions with Precisely engineers.
- ExxonMobil transformed their inventory data entry process, saving $5 million, by leveraging Automate Evolve for data standardization.
- Weber Metals streamlined data migration using Automate Studio, appreciating its user-friendliness and time-saving capabilities.
- Celanese automated processes like bill of material and inspection plans with Automate Evolve, reducing cycle time and maintaining efficiency even after a significant data volume increase.
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TechBullion
2w
299
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Databricks Launches Data Intelligence for Marketing
- Databricks has launched Data Intelligence for Marketing, combining the Data Intelligence Platform with out-of-the-box integrations to leading marketing solution providers.
- The platform allows marketers to bring customer and campaign data together in real time to develop more relevant and efficient campaigns at scale.
- Partnerships with companies like Adobe, Amperity, Salesforce, Deloitte, and more enable marketers to access insights, personalize experiences, and run effective marketing campaigns.
- Databricks' Data Intelligence for Marketing empowers organizations to meet customer expectations faster by providing easier access to insights, smarter campaign strategies, and the utilization of AI and marketing partners.
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TechBullion
2w
208
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From Dashboards to Decision-Making: An Interview With Daria Voronova, A Data Visualization Expert Transforming Business Decision-Making
- Daria Voronova, a data visualization expert, focuses on moving businesses from guesswork to informed decisions.
- She emphasizes the importance of starting with the right questions and ensuring decisions are trustworthy.
- Voronova's approach involves building a system starting from clarifying the business problem to designing tools for understanding.
- She aims to shift people's mindset from tool users to strategic partners in data visualization and decision-making.
- Voronova highlights the value of helping stakeholders ask better questions for actionable solutions.
- She stresses on the significance of internal growth and training to enable teams to move towards strategic thinking.
- Voronova's methodology combines energy, analytics, design, and business consulting to build systems executives rely on.
- She emphasizes the need for professionals to focus on critical thinking and solving complex business problems rather than just obtaining certifications.
- Voronova leverages AI to accelerate learning in data visualization, emphasizing solving practical business problems rather than just creating charts.
- She believes AI enhances professionals' roles by automating tasks while emphasizing human strengths like interpretation and creativity.
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Towards Data Science
2w
116
The Geospatial Capabilities of Microsoft Fabric and ESRI GeoAnalytics, Demonstrated
- Geospatial data plays a crucial role in data collected and maintained by governments. Big Data engines need adaptation to efficiently handle geospatial data, with considerations like geographical indexes and partitioning.
- Microsoft Fabric Spark compute engine, integrated with ESRI GeoAnalytics, is showcased for geospatial big data processing.
- GeoAnalytics functions in Fabric support over 150 spatial functions, enabling spatial operations in Python, SQL, or Scala with spatial indexing for efficiency.
- A demonstration using Dutch AHN and BAG datasets illustrates spatial selection and processing capabilities on a large dataset.
- Steps include reading data in geoparquet format, spatial selections, aggregation of lidar points, and spatial regression.
- Notable functions like make_point, srid, AggregatePoints, and GWR are used in the demonstration for data transformation and analysis.
- Visualizations are generated to showcase building data and height differences, emphasizing the importance of geographical data in analytics.
- Challenges of handling geospatial data efficiently in big data systems are discussed, emphasizing the need for adaptation and specialized tools.
- The blog post serves as a demonstration of effective geospatial big data processing using Microsoft Fabric and ESRI GeoAnalytics.
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Javacodegeeks
2w
373
Image Credit: Javacodegeeks
Introduction to Apache Beam Using Java: A Beginner-Friendly Guide to Unified Data Processing
- Apache Beam is an open-source, unified model for defining both batch and streaming data-parallel processing pipelines, allowing developers to write jobs that can run on various execution engines.
- Apache Beam provides a high-level programming model with a unified API for batch and streaming, portability across multiple runners, and support for windowing, event time, triggers, and watermarks.
- Java is the primary SDK for Apache Beam, offering mature API, better performance tuning options, wide usage in enterprise systems, and compatibility with Maven and Gradle for dependency management.
- Apache Beam's unified API allows developers to define pipelines that can be configured for either batch or streaming at runtime, making it versatile for processing both bounded and unbounded datasets.
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TechBullion
2w
134
Image Credit: TechBullion
DiffuseDrive Aims to Solve Data Scarcity Problem for Automotive, Aerospace, Defense, and Robotics Industries with New Round of Seed Funding
- DiffuseDrive, a physical AI company, aims to address data scarcity for Fortune 500 companies by creating synthetic data through its generative AI platform.
- The platform generates photorealistic synthetic data quickly, helping train computer vision models effectively.
- DiffuseDrive secured $3.5 million in seed funding and has been successful in deploying its technology in various industries.
- Founded by Balint Pasztor and Roland Pinter in Hungary in 2023, DiffuseDrive quickly moved to Silicon Valley to tackle data scarcity in AI.
- The robotics and AI market is expected to reach over $124 billion by 2030, providing significant opportunities for innovative solutions like DiffuseDrive.
- With previous funding of $1 million, DiffuseDrive's total seed funding amounts to $4.5 million, crucial for expanding its technology.
- The generative AI platform significantly improves data solutions for companies, offering faster and more realistic data for training models.
- DiffuseDrive's technology streamlines data assessment and generation, surpassing traditional methods of data gathering and simulation.
- Co-founder Roland Pinter emphasizes the importance of data in AI and envisions DiffuseDrive as the industry's gold standard for data solutions.
- DiffuseDrive's innovative technology has the potential to transform various industries, such as automotive, defense, and robotics, through intelligent decision-making.
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Siliconangle
2w
204
Image Credit: Siliconangle
Amplitude adds new tools to unify marketing and product analytics
- Amplitude Inc. has launched new capabilities to unify marketing and product data for optimizing channels and campaigns.
- The new features enable connecting top-of-funnel marketing with in-product behavior, offering insights for conversions and customer value.
- Additions include tailored dashboards for marketing teams, experimentation tools, and integration with ad platforms like Google Ads and Facebook Ads.
- Amplitude's latest tools aim to empower marketers with actionable insights, reduce reliance on data analysts, and improve campaign effectiveness.
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Siliconangle
2w
356
Image Credit: Siliconangle
Informatica’s CLAIRE agents bring AI automation to big data management
- Informatica LLC is introducing a range of AI agents named CLAIRE to automate data management tasks.
- These agents, set to launch in the fall, will assist in data ingestion, quality assurance, and lineage tracking.
- The AI agents catalogue includes Data Quality, Data Discovery, Data Ingestion, ELT, and Modernization agents.
- They aim to streamline data operations across various platforms and address the growing challenges in data management.
- Informatica's Metadata System of Intelligence adds context to each customer's data environment, enhancing data management processes.
- CLAIRE Agents will be compatible with major data platforms and will leverage large language models like Google's Gemini and Databricks' Mosaic AI.
- Informatica CEO Amit Walia sees the agents as a step towards autonomous data management, offering orchestrated and transformative business outcomes.
- An AI Agent Engineering service will also be launched to allow companies to create and manage fleets of multi-agent AI systems.
- Informatica's CLAIRE Copilot, leveraging LLMs like Microsoft's Azure OpenAI GPT, will help developers in optimizing data transformations.
- This automation drive by Informatica aims to simplify data processes and allow organizations to focus more on building innovative features.
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Amazon
2w
357
Image Credit: Amazon
Zero-copy, Coordination-free approach to OpenSearch Snapshots
- Amazon OpenSearch Service provides automated snapshots for data backup and recovery, maintaining data durability and business continuity.
- Snapshot efficiency enhancements in OpenSearch domains aim to avoid operational impact.
- The traditional OpenSearch snapshot mechanism involves significant communication overhead, especially in large deployments.
- OpenSearch optimized instances have improved data durability and efficiency by referencing existing data during snapshots.
- Shallow snapshot v2 reduces data duplication and communication overhead through timestamp-based referencing.
- Shallow snapshot v2 offers substantial performance improvements and scalability benefits with faster snapshot creation.
- The architecture of shallow snapshot v2 simplifies operations and enhances storage efficiency, leading to cost savings.
- Future enhancements may include point in time recovery and further performance optimizations.
- Shallow Snapshot V2 provides a robust solution for modern data backup challenges in Amazon OpenSearch Service domains.
- Authors of the article include Sachin Kale, Bukhtawar Khan, and Gaurav Bafna, who work on various aspects of OpenSearch at AWS.
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