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Big Data News
Amazon
5h
271
Image Credit: Amazon
Access Amazon S3 Iceberg tables from Databricks using AWS Glue Iceberg Rest Catalog in Amazon SageMaker Lakehouse
- Amazon SageMaker Lakehouse enables a unified, open, and secure lakehouse platform on your existing data lakes and warehouses.
- SageMaker Lakehouse enables interoperability by providing open source Apache Iceberg REST APIs to access data in the lakehouse.
- SageMaker Lakehouse now provides secure and fine-grained access controls on data in both data warehouses and data lakes.
- In this post, we show how tables cataloged in Data Catalog and stored on Amazon S3 can be consumed from Databricks compute using Glue Iceberg REST Catalog with data access secured using Lake Formation.
- To follow along with the solution presented in this post, you need the following AWS prerequisites.
- Create a cluster and configure it to connect with a Glue Iceberg REST Catalog endpoint.
- We have showed you how to manage a dataset centrally in AWS Glue Data Catalog and make it accessible to Databricks compute using the Iceberg REST Catalog API.
- The solution also enables you to use Databricks to use existing access control mechanisms with Lake Formation.
- Srividya Parthasarathy is a Senior Big Data Architect on the AWS Lake Formation team.
- Venkatavaradhan (Venkat) Viswanathan is a Global Partner Solutions Architect at Amazon Web Services.
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Cloudera
8h
289
Image Credit: Cloudera
The Art of Getting Stuff Done with NVIDIA’s Kari Briski
- For the inaugural episode of Women Leaders in Technology, we welcomed Kari Briski – Vice President AI Software Product Management at NVIDIA.
- When you hit a roadblock, A) don’t get discouraged, B) step back and look at the picture, and then C) take small steps along the way to go around it.
- It’s easier to say, but it just seems devilishly hard to do. It’s really hard to do.
- I have one piece of advice I’ve always said, and I will continue to say it. I say that it doesn’t get any easier. It will never get any easier. It just gets harder, but you get better.
- Find the qualities in the people that you admire, and then apply it to yourself. Number one, it takes time.
- Get comfortable feeling uncomfortable.
- Are you passionate enough about the work that you’re doing to be uncomfortable, to grow, to push boundaries, to challenge people? And if you feel like you’re constantly getting those roadblocks, then make a change. Someone else will value you more.
- You’ll learn more through the process.
- To learn more about Cloudera’s Women Leaders in Technology, join the LinkedIn community and visit the home page.
- Like and subscribe to The AI Forecast to stay up to date on the latest episodes.
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Cloudera
1d
331
Image Credit: Cloudera
AI-Driven SOC Transformation with Cloudera: Enhancing Security Operations with Agentic AI
- Artificial Intelligence(AI) is revolutionizing Security Operations Centers(SOCs) by enabling proactive threat mitigation, automating repetitive tasks and providing actionable insights
- Agentic AI's autonomous and adaptive systems allow SOC teams to actively monitor and respond to threats in real-time and reduce the cognitive load on SOC analysts.
- Tines, a cybersecurity company found that 64% of SOC professionals planned to quit their roles due to stress and burnout issues.
- Also, handling sensitive network data requires robust security measures to prevent data breaches and ensure compliance.
- Agentic AI builds on GenAI capabilities by introducing a layer of autonomy and proactivity in SOC systems.
- By deploying private GenAI models tailored to enterprise needs and incorporating the capabilities of Agentic AI, SOC teams can enhance SOC effectiveness while maintaining data security and compliance.
- Agentic AI leverages predictive analytics to foresee potential attack vectors and suggests mitigating strategies before a threat fully manifests.
- GenAI can process and condense large amounts of data, providing SOC teams with concise summaries of incidents, allowing for faster decision-making.
- AI-powered assistants can recommend remediation steps based on the analysis of past incidents and best practices empowered with actionable insights.
- Cloudera's unified approach to data management, advanced analytics, machine learning and AI can help transform SOC operations and enhance efficiency.
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Siliconangle
2d
50
Image Credit: Siliconangle
Predictions 2025: Data renaissance, systems of agency, LAMs, SAMs and security threats
- The rise of open table formats, shifting control points, open-source governance catalogs and a heightened focus on artificial intelligence are creating both challenges and opportunities for enterprises and the tech providers who serve them.
- Machine learning and AI are experiencing high spending momentum within most sectors.
- The big three cloud firms are bunched along with Databricks Inc. as a stand out on the vertical axis with some of the traditional AI companies showing strong momentum.
- Enterprises are seeking new ways to provide richer context for their AI initiatives, prompting a surge of interest in knowledge graph technologies.
- Organizations are shifting from proof-of-concept generative AI pilots to full-scale production systems, introducing new data governance imperatives, more sophisticated retrieval and action models, and the rise of personal agents that promise to handle everyday tasks autonomously.
- A new perspective on AI centers around “large action models,” where systems predict the next action — instead of just the next word — by analyzing sequences of function calls. This evolution builds upon large language models but expands their scope to orchestrate decisions across multiple enterprise applications.
- Rising enthusiasm for AI is introducing new and often poorly understood security threats.
- Knowledge graphs offer a compelling strategy for unifying siloed data and adding rich context to AI workloads.
- Success in 2025 will hinge on recognizing AI as a multifaceted ecosystem, one that demands cohesive planning, deep domain expertise, and rigorous oversight to unlock next-level business value.
- 2025 stands poised to be the year enterprises shift from basic AI adoption to comprehensive, integrated strategies.
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Amazon
2d
31
Image Credit: Amazon
Generate vector embeddings for your data using AWS Lambda as a processor for Amazon OpenSearch Ingestion
- Amazon OpenSearch Ingestion now offers AWS Lambda support for processors that enrich and transform log, metric, and trace data in the pipeline.
- With Lambda processors, you can batch events for a single payload before invoking Lambda to optimize the pipeline for performance and cost.
- This enables you to run code without managing servers, integrating events, or maintaining runtimes.
- Integrating OpenSearch Ingestion, Lambda, and OpenSearch Serverless creates a fully serverless pipeline for embedding generation and search.
- You can easily generate embeddings by using OpenSearch's Lambda processor to dynamically generate them.
- The Lambda function invokes Amazon Bedrock's text embeddings model, enabling efficient and scalable embedding creation.
- This serverless approach allows you to focus on developing search and analytics solutions rather than managing infrastructure.
- The Lambda function receives data from OpenSearch Ingestion, invokes Amazon Bedrock to generate an embedding, and adds it to the source record.
- Using Lambda processors to enrich and transform data provides flexibility and the ability to customize event data to meet your needs.
- Amazon OpenSearch Ingestion gives you the option of using Lambda functions as processors along with built-in native processors when transforming data.
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TechBullion
3d
33
Image Credit: TechBullion
How to Recover Data from a Crashed MySQL Table – A Complete Guide
- MySQL tables can crash due to disk failures, power blackouts, software bugs, human error, and resource overloads
- Before recovery, it is crucial to take a complete backup of the database and to check the MySQL error logs for crash details
- Data recovery can be manual or automatic using tools, but both have their own merits and demerits
- There are several methods to recover a crashed MySQL table, including using modern backup systems, Stellar Repair for MySQL, and MySQL utilities such as mysqldump and mysqlcheck
- PHPMyAdmin can also be used to restore MySQL tables using the import feature
- If binary logging is enabled, MySQL tables can be recovered using the mysqlbinlog utility
- For a streamlined recovery, consider using specialized tools like Stellar Repair for MySQL
- After recovery, it is important to test the performance of the recovered table and monitor MySQL logs for early detection of potential issues
- To safeguard your database, set up regular backups and recovery strategies and follow key tips for ensuring the health of MySQL tables
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Siliconangle
7d
186
Image Credit: Siliconangle
Onehouse says its runtime accelerator can speed data lakehouse operations up to 30-fold
- Onehouse Inc. has released a runtime engine that can speed up workloads across open data lake table formats.
- The engine achieves this by understanding common data workloads and implementing specialized optimizations.
- Customers using the Onehouse Compute Runtime experience improved query performance and reduced cloud infrastructure costs.
- The OCR supports various open data lake table formats and integrates with major catalogs in the market.
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Scientificworldinfo
7d
24
Image Credit: Scientificworldinfo
Big Data vs. Small Data: What’s the Difference? Explained
- Big Data refers to datasets that are so large, complex, or fast-moving that traditional data management tools cannot process them efficiently. Distributed Computing, Data Warehousing and Storage Solutions, Machine Learning, Artificial Intelligence, and Data Integration Tools are key technologies for Big Data.
- Small Data refers to datasets that are manageable in size and complexity, making them easier to analyze and interpret using traditional tools like spreadsheets and databases. Customer Feedback Surveys, Sales Data Analysis and Website Analytics are some of the examples of Small Data.
- Big Data is targeted for unlocking macro-level insights for industries like healthcare, finance, and retail, while Small Data shines in localized scenarios such as improving customer satisfaction through survey feedback.
- Big Data provides comprehensive insights, supports scalability, while Small Data is easy to use, quick for decision-making and cost-effective.
- While Big Data and Small Data differ, integrating big data and small data creates a powerful synergy, enabling businesses to align their data strategies with specific business needs, balancing the depth and breadth of insights derived.
- Emerging trends such as Edge Computing, Data Democratization, and AI-Powered Insights are set to shape this dynamic landscape, paving the way for a future where data is not just a byproduct of digital interactions but a central driver of progress.
- By mastering the balance between Big Data and Small Data, organizations can create data strategies that are both effective and efficient, staying competitive in an increasingly complex world.
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1 Like
Amazon
7d
58
Image Credit: Amazon
Automate topic provisioning and configuration using Terraform with Amazon MSK
- Manual management of topic configurations for Amazon MSK clusters can be challenging for organizations, leading to inefficiency and scalability challenges.
- Using Terraform, an infrastructure as code tool by HashiCorp, can automate topic provisioning and configuration for both provisioned and serverless MSK clusters.
- Terraform provides benefits such as automation, consistency and repeatability, scalability, and version control.
- To use Terraform for MSK topic configuration management, you will need AWS account access, AWS Identity and Access Management (IAM) roles and MSK clusters, and Terraform 1.10.x.
- The article also provides a comprehensive guide to install Terraform on your client machine, provision an MSK topic using Terraform, update MSK topic configuration, and delete MSK topic using Terraform.
- Through Terraform, organizations can streamline their operations, minimize errors, and have a robust and scalable Amazon MSK environment.
- The solution offers automation, consistency, scalability and version control benefits enabling provisioning and managing of multiple MSK topics.
- The use of Terraform tool simplifies process, fosters scalability, and enhances flexibility. It automates topic configuration management and facilitates centralized management of MSK environment.
- By adopting Terraform for optimizing Amazon MSK configurations, organizations can achieve operational excellence while streamlining their data pipelines, unlocking efficiency and scalability.
- The solution offers automation, consistency, scalability, version control, and centralized management of MSK topic configurations.
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Pymnts
6h
149
Image Credit: Pymnts
CFOs and Treasurers Embrace Leading Role in Organizational Data Monetization
- To monetise the vast amounts of data that businesses now produce, CFOs and treasurers must play a leading role in data-driven processes and planning, according to PYMNTS. Data value management, data resource life-cycle measurement and executive-level data leadership make up the key pillars of a business culture that acknowledges big data as a valuable strategic asset. Among the three, data value realisation involves the deployment of actionable insights that translate to gains in revenue or customer experience improvements. For maximum efficiency, efforts must be aligned with broader business strategies.
- Treasurers have a key role to play in bridging the gap between financial priorities and technological possibilities. Meanwhile, data life-cycle measurement involves measuring the financial impact of data over time, as well as its reuse across different departments and how it contributes to revenue generation and cost savings. By properly tracking and analysing data, businesses can make better informed decisions and allocate resources more effectively.
- Finance teams play a crucial part in data monetisation, providing analytical rigour for measuring the financial and non-financial gains that data initiatives deliver. They also ensure that data projects are prioritised and aligned with a company's broader financial outlook and funding requirements. Smart data management will become even more important as businesses migrate further to the cloud and adopt artificial intelligence technologies.
- At present, 77% of treasurers believe closer collaboration between departments is necessary and to reap the full benefits from data monetisation all participants must share a common approach. CFOs, meanwhile, have a critical role in measuring the potential return on investment in data-oriented initiatives, prioritising the most impactful and setting budgets accordingly.
- Finance teams want to automate their sectors. According to Flashfood CFO Chris Taylor, 'We’re super-focused on automating all the financial flows within the finance function.'
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8 Likes
Precisely
9h
153
Image Credit: Precisely
2025 Planning Insights: Skills and Resource Shortages Impede AI Adoption and Data Program Success
- A recent report highlights that skills and resource shortages are impeding AI adoption and data program success.
- 60% of respondents in the survey cited a lack of skills and resources as a challenge to AI adoption.
- The shortage of professionals skilled in data management and AI implementation presents a significant obstacle for organizations.
- 42% of respondents identified a shortage of skills and staff as the biggest challenge hindering organizations from achieving high data quality.
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9 Likes
Siliconangle
1d
327
Image Credit: Siliconangle
Diesel Labs blends generative AI chat with big-data analysis to create synthetic focus groups for marketing teams
- Diesel Labs Inc. has developed a tool called PanelAI that merges generative AI chat capabilities with its proprietary dataset on consumer attitudes.
- PanelAI provides marketing and media teams with real-time insights from consumers, derived from over 2.2 billion daily signals on various platforms.
- The tool acts as a synthetic focus group, replacing traditional focus groups and surveys to deliver accurate insights into consumer behavior.
- PanelAI combines social media opinions with statistical data analysis, offering a deeper understanding of emerging trends and enabling smarter decisions.
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19 Likes
Siliconangle
2d
270
Image Credit: Siliconangle
TigerGraph revs up its graph database offering with faster setup times and new preconfigurations
- Graph database startup TigerGraph Inc. announces major update to its cloud platform.
- The Savanna release brings six times faster network deployments and numerous new features.
- TigerGraph's graph database enables rapid analysis and queries by storing connections alongside traditional records.
- The Savanna update includes faster provisioning, automated deployments, preconfigured solution kits, cost savings, and improved support for query languages.
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16 Likes
HRKatha
2d
2.9k
Image Credit: HRKatha
69% employers say analytical thinking is the core skill for the workforce in 2025
- Analytical thinking is considered the core skill for the workforce in 2025, according to a survey by the World Economic Forum.
- The report reveals that 69% of employers believe analytical thinking is the top skill needed.
- Other essential skills include resilience, flexibility, agility, leadership, social influence, and creative thinking.
- Technological skills, such as AI, big data, networks, and cybersecurity, are predicted to grow rapidly in importance.
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1 Like
TechBullion
4d
378
Image Credit: TechBullion
Big Data Analytics: Transforming Cybersecurity Practices
- Big data analytics revolutionizes how we address cybersecurity threats and vulnerabilities.
- It provides real-time processing and analysis of vast datasets, enabling proactive threat detection.
- Big data analytics equips security teams with actionable insights for faster incident response and containment strategies.
- The integration of big data analytics with SIEM systems and AI creates a powerful synergy for cybersecurity.
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