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Amazon
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How FINRA established real-time operational observability for Amazon EMR big data workloads on Amazon EC2 with Prometheus and Grafana
- FINRA has built an observability framework to provide operational metrics insights for big data processing workloads on Amazon EMR on Amazon Elastic Compute Cloud (Amazon EC2) clusters with Prometheus and Grafana.
- Monitoring EMR clusters in real-time is crucial in identifying root causes, minimizing manual actions, and increasing productivity. The challenges faced by organizations while observing cluster performance include scale, dynamic environments, data variety, resource utilization, latency, centralizing observability dashboards, alerting, incident management, and cost management.
- Insights gained from Monitor and Optimize Analytic Workloads on Amazon EMR with Prometheus and Grafana helped FINRA to build its enterprise central monitoring solution using Managed Prometheus and Managed Grafana.
- Managed Prometheus allows for real-time high-volume data collection that scales the ingestion, storage, and querying of operational metrics to mimic Ganglia-like metrics. Additionally, a data ingestion layer for every cluster, configuration for metrics collection, and inefficiencies were included in the solution.
- A mechanism was built to render metrics on Managed Grafana dashboards for task-level, node-level, and cluster-level metrics that can be promoted from lower environments to higher environments.
- The scalable solution significantly reduced the time to resolution and enhanced overall operational stance. The solution empowered the operations and engineering teams with comprehensive insights into various Amazon EMR metrics like OS levels, Spark, JMX, HDFS, and Yarn, all consolidated in one place.
- The solution extends to use cases such as Amazon Elastic Kubernetes Service (Amazon EKS) clusters, including EMR on EKS clusters, and other applications, establishing it as a one-stop system for monitoring metrics across Finra's infrastructure and applications.
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TechBullion
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Secure Your Financial Future: The Benefits of Digital ID Wallets for Document Management
- Digital ID wallets can help manage our identities and documents securely.
- They can streamline processes and enhance security for financial planning.
- Document management is an effective way to plan finances quickly.
- Digital ID wallets store everything in one secure location, reducing clutter.
- Accessibility is another significant advantage of digital ID wallets.
- Digital ID wallets provide user-friendly interfaces and interactions.
- Compatibility with various document types is essential.
- Frequent updates help protect against cybersecurity threats.
- Digital wallets offer integration with blockchain technology, making fraud nearly impossible.
- Investing in a digital ID wallet is a strategic move toward the long-term financial wellness of an individual.
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Cloudera
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Enable Image Analysis with Cloudera’s New Accelerator for Machine Learning Projects Based on Anthropic Claude
- Cloudera introduces a new addition to its Accelerator for Machine Learning Projects (AMPs) called Anthropic Claude, a Large Language Model (LLM).
- Anthropic Claude supports the extraction and manipulation of information from images, offering advanced reasoning capabilities for complex document understanding tasks.
- The AMP enables businesses to digitize, analyze, and extract machine-usable data from unstructured visual inputs, making it a powerful tool for information retrieval and data transformation.
- Possible use cases for this AMP include transcribing typed and handwritten text, extracting data from structured forms, complex document QA, data transformation, and user-defined prompts for niche applications.
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Cloudera
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Empower Your Cyber Defenders with Real-Time Analytics Author: Carolyn Duby, Field CTO
- The Identity Theft Resource Center (ITRC) Annual Data Breach Report shows a 72% increase in data breaches since 2021, highlighting the challenges cybersecurity professionals face today.
- Volume and complexity of existing systems are overwhelming, leaving professionals feeling burned out.
- Too much data, too many tools, and unstructured data not ready for analysis are all challenges facing cyber defenders.
- Cloudera's open data lakehouse, with its combination of data lake storage and data warehouse functionality, tackles these challenges successfully.
- Features of the Cloudera architecture include one unified system for all critical log data, optimized analytics, secure and governed data, and real-time insights through streaming pipelines.
- Cloudera's open data lakehouse enables seamless integration with a wide range of tools and empowers defenders to detect and respond to threats in real-time.
- Iceberg, by Cloudera, optimizes query planning for real-time threat detection, while easing the burden of managing large, constantly evolving tables.
- Cloudera enables AI-driven cybersecurity with generative AI tools that automate routine tasks and build chatbots for incident summaries.
- Overall, Cloudera's open data lakehouse architecture empowers defenders to protect their organizations with real-time and predictive insights, keeping pace with cyber threats.
- The solution is scalable, cost-effective, secure, and analytics-ready, making it one of the best solutions on the market today.
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Amazon
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Your guide to AWS Analytics at AWS re:Invent 2024
- AWS re:Invent 2024 is a comprehensive analytics conference that aims to equip data professionals with the latest advances in the field.
- The event features keynote speeches, breakout sessions, chalk talks, builder sessions, and workshops.
- CEO Matt Garman will speak about AWS's innovation across all aspects of the platform. Swami Sivasubramanian will discuss how data is essential to creating innovative and original solutions for customers.
- Dr. Werner Vogels will share his insights into managing increasingly complex systems based on lessons from running distributed systems at large scales
- Sessions cover a variety of analytic topics: data streaming, serverless analytics, data sharing and management, generative AI, search, data governance, and more.
- Re:Invent breakout sessions are immersive and led by AWS experts, customers, and partners. They are an opportunity to expand your horizons and inspire your next big idea.
- Chalk talks offer a more interactive format for learning. These sessions are highly engaging and offer a unique blend of expert insight and collaborative learning.
- Builder sessions are designed to elevate AWS skills. Each workshop has a maximum audience size of ten attendees with a dedicated AWS expert per table.
- Code talks offer a practical AWS development for developers. Expert speakers guide you through live coding demonstrations showcasing real-world solutions in action.
- The post acts as your go-to resource for navigating the AWS analytics track at re:Invent 2024. Stay in the know and follow AWS Analytics LinkedIn page.
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Amazon
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Ingest telemetry messages in near real time with Amazon API Gateway, Amazon Data Firehose, and Amazon Location Service
- Many organizations use satellite-powered terminal devices for remote monitoring using telemetry and NMEA-0183 formatted messages generated in near real time.
- AWS Cloud-based solution aims to capture, enrich, and ingest telemetry messages as well as deliver alerts in near real time.
- The telemetry message processing begins with an API endpoint created using API Gateway, securing HTTPS transmission over a satellite network.
- Data Firehose batches the messages into 60-second intervals or 1 MB size files, whichever comes first.
- AWS CloudFormation template creates the API Gateway endpoint, Data Firehose delivery stream, Lambda function, Amazon Location index, SNS topic, S3 bucket, and AWS Glue database, table, and crawler.
- Sample JSON formatted telemetry messages simulated the satellite-powered terminal device functionality are delivered to an API Gateway endpoint test interface.
- You should receive an SNS alert at the provided email address.
- Best practices include conducting load testing of the solution using an API simulator tailored to your specific use case and adhering to the least privilege principle for all created users and roles.
- The solution is designed to provide time-critical alerts and actionable insights, for use cases in the space analytics domain.
- Authors of the post provided proof of concept to implement a satellite-based remote alerting and response solution.
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Amazon
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Expand data access through Apache Iceberg using Delta Lake UniForm on AWS
- Delta Lake UniForm is an open table format extension designed to provide a universal data representation that can be efficiently read by different processing engines.
- UniForm allows other table format clients such as Apache Iceberg to access Delta Lake tables.
- There are two options to use UniForm: create a new Delta Lake table with UniForm or enable UniForm on your existing Delta Lake table.
- Delta Lake UniForm expands data access to broader options of analytics engines.
- To create a new Delta Lake table with UniForm enabled, you configure table properties for UniForm in a CREATE TABLE DDL query.
- With Delta Lake UniForm, you can make the Delta Table compatible with the other open table formats such as Apache Iceberg.
- Delta Lake UniForm can be a solution to meet the data access requirement for cloud-based data warehouses such as Amazon Redshift for flexible analytics use cases.
- Enabling UniForm on a Delta Lake table doesn’t affect your current operations on the table, making it suitable if you have Delta Lake tables in your environment.
- UniForm generates multiple table format metadata without duplicating the actual data files.
- Delta Lake UniForm on AWS expands data access capabilities, allowing organizations to use a broader range of analytics engines and data warehouses such as Amazon Redshift.
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Precisely
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2025 Trends in SAP® Processes and Automation: New strategic insights
- Automation is becoming a key driver of digital transformation strategies. As companies continue their digital journeys, 58% are adopting automation as a way to improve agility, speed and data quality. While 50% are choosing to apply automation on a use-case-by-use-case basis, the number of businesses with no automated processes has dropped from 13% to 4% in the past year. However, companies face challenges surrounding integration and complexity. Integration is the top automation challenge driven by the growing use-case-by-use-case approach. Process complexity remains a significant barrier to automation's potential.
- The shift to SAP® S/4HANA is gaining momentum with half of the respondents having already migrated to it. The move creates a renewed emphasis on process and data interdependence across business functions. Adoption of SAP® BTP services is increasing, driven by integration capabilities. Adoption grew from 31% in 2023 to 41% in 2024. The growing interest in BTP is consistent with the 'Clean Core' strategy that aims to reduce customisations and clutter from legacy software systems.
- SAP® Fiori Adoption remains steady but comes with challenges, such as limitations in using the platform for automated solutions due to its complexities compared to traditional SAP® GUI transactions and other manual data entry requirements. Adoption in finance and supply chain management is at 61% and 50% but future plans are lower, with 84% and 64% respectively. Meanwhile, there's increasing importance placed on citizen developers as organisations look for automation responses to support growing business needs.
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Cloudera
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Introducing Cloudera Fine Tuning Studio for Training, Evaluating, and Deploying LLMs with Cloudera AI
- Cloudera Fine Tuning Studio, a one-stop-shop studio application that covers the entire workflow and lifecycle of fine tuning, evaluating, and deploying fine-tuned LLMs in Cloudera’s AI Workbench.
- Large Language Models (LLMs) can optimize costs, save time, and increase productivity for both internal and external use cases in enterprises.
- Several LLMs are publicly available through APIs from OpenAI, Anthropic, AWS, and others, but enterprises often can’t use them because of private data sources, company-specific formatting, and hosting costs.
- Fine tuning can solve these issues to provide specific coding formats and standards, reduce training time, and achieve cost benefits with smaller models customized to specific tasks or use cases.
- Cloudera's Fine Tuning Studio enables users to track all resources for fine tuning and evaluating LLMs, build and test prompts, train new adapters for LLMs, evaluate trained LLMs, and deploy them to production environments.
- Fine Tuning Studio comes with MLFlow experiments integration, several ways to test the performance of trained models and compare their performance, and deep integrations with Cloudera’s AI suite of tools to deploy, host, and monitor LLMs.
- Fine Tuning Studio ships with a convenient Python client that makes calls to the Fine Tuning Studio’s core server, enabling data scientists to build and develop their own training scripts.
- Cloudera’s Fine Tuning Studio is available to Cloudera AI customers as an Accelerator for Machine Learning Projects (AMP).
- The team behind Fine Tuning Studio aims to provide customers with a streamlined approach to fine tune any model, on any data, for any enterprise application.
- Fine Tuning Studio can be utilized to fine tune smaller, cost-effective models to perform specific tasks, outperforming larger, more generalized models.
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Precisely
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Understanding Master Data Management (MDM) and Its Role in Data Integrity
- Master data management (MDM) ensures accuracy, consistency, and uniformity of a company's data.
- MDM is important for breaking down data silos, avoiding discrepancies, and informed decision-making.
- It acts as a single source of truth and offers a holistic and up-to-date view of business data.
- MDM synchronizes data changes across all systems in case of customer updates thereby avoiding costly errors.
- MDM ensures accuracy, consistency, and integrity of master data throughout its entire life cycle, including validation and deprecation of records.
- Challenges in implementing MDM include data literacy, governance, ownership, and data protection.
- MDM's cross-domain management ability can benefit all industries.
- A system of record is a centralized repository where critical business data is stored and managed, and is closely tied to MDM.
- Master data consists of key business entities amidst various data domains including party, reference, location, financial, and revenue.
- Precisely offers EnterWorks multi-domain MDM and its Data Integrity Suite including Data Observability, Data Governance, and Data Quality to address MDM challenges.
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SiliconCanals
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Germany’s big xyt secures €10M funding led by Amsterdam’s Finch Capital
- Frankfurt-based big xyt, a data analytics company for financial markets, has secured €10M funding led by Amsterdam's Finch Capital.
- The funds will be used to expand into key markets such as Europe, the US, and Asia-Pacific.
- The investment will support hiring and strengthen big xyt's position in AI-based data analytics for financial markets.
- big xyt offers a scalable platform for analysing global market data and providing analytics solutions to various clients.
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Siliconangle
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SAS buys synthetic data software from Hazy to streamline AI development
- SAS Institute Inc. acquires intellectual property from Hazy Ltd to boost its artificial intelligence portfolio.
- The acquisition enables SAS to provide customers with tools to create synthetic data for AI workloads.
- Hazy's platform enables companies to use sensitive data to create synthetic information without exposing private or restricted information.
- SAS plans to integrate Hazy's tools with SAS Data Maker platform, providing customers with richer synthetic datasets and accelerating AI projects.
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Siliconangle
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Data mapping provider Lume raises $4.2M in funding
- Lume AI Inc. raises $4.2 million in seed funding to enhance its data mapping technology.
- General Catalyst led the funding round, with participation from Khosla Ventures, Floodgate, Y Combinator, Soma Capital, and angel investors.
- Lume's software platform simplifies the task of creating data mapping pipelines, automatically generating scripts to reformat data between applications.
- The company aims to save developers time and enable easier data movement within organizations, offering support for JSON, CSV, and XML formats.
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TechBullion
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Innovative Database Column Expansion and Automation for Scalable System
- Crafting an optimal innovative solution requires careful assessment of the system’s current state, aligning with evolving business needs, and balancing security, performance, and budget constraints.
- The Database Column Expansion Project was initiated to enhance the brewery system, expanding the Product ID field from 2 bytes to 4 bytes—a change essential for supporting future product introductions.
- Identifying every impacted Product_Id column across 128 applications developed in various programming languages was complex, given inconsistent naming conventions across tables.
- The initiative demanded rigorous planning and efficient execution to handle the high transaction volume that the brewery system handles while ensuring that the system’s performance wasn’t compromised.
- To facilitate this process, a comprehensive impact analysis utility was created using Oracle PL/SQL, leveraging stored procedures and packages, to accurately identify and verify the impacted columns.
- The approved design utilized a more streamlined and efficient approach with views and synonyms to ensure the smooth operation of both remediated and non-remediated applications.
- The development of source code for each database view object, “Instead of” triggers, and other associated components would require several hours per day, prompting the development of a robust automation framework using Oracle PL/SQL and UNIX Shell scripting.
- A comprehensive performance tuning initiative, combining proactive and incremental tuning with continuous monitoring, dramatically optimized data processing speeds.
- This project successfully delivered a groundbreaking solution for database column expansion across multiple systems, enabling seamless scalability for future product launches.
- Through the automation of database component creation and compilation, significant cost savings and enhanced operational efficiency were achieved.
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Amazon
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Streamlining AWS Glue Studio visual jobs: Building an integrated CI/CD pipeline for seamless environment synchronization
- AWS Glue enables organizations to make data-driven business decisions by providing seamless integration throughout the development lifecycle, many customers have integrated their data across multiple sources using AWS Glue.
- AWS Glue Studio visual jobs provide a graphical interface called the visual editor that you can use to author extract, transform, and load (ETL) jobs in AWS Glue visually.
- To address the needs of a streamlined development lifecycle and seamless synchronization between environments, an end-to-end solution is presented, combining the power of the AWS Glue Visual Job API, a custom AWS Glue Resource Sync Utility, and an based continuous integration and continuous deployment (CI/CD) pipeline.
- The AWS Glue Resource Sync Utility is a Python application developed on top of the AWS Glue Visual Job API, designed to synchronize AWS Glue Studio visual jobs across different accounts without losing the visual representation.
- The solution uses three separate AWS accounts. One account is designated for the development environment, another for the production environment, and a third to host the CI/CD infrastructure and pipeline.
- The AWS account responsible for hosting the CI/CD pipeline is composed of three key components: Managing AWS Glue Job updates, Cross-Account Access Management, and Version Control Integration.
- You can create AWS Glue Studio visual jobs using the intuitive visual editor in your development account.
- By serializing AWS Glue Studio visual jobs to JSON files and committing them to a Git repository, you enable version control for your data integration workflows.
- By following this approach you can track changes, collaborate with team members, and easily deploy jobs to other accounts or environments.
- This solution empowers data engineers to focus on building robust data integration pipelines while automating the complexities of managing and deploying AWS Glue Studio visual jobs across multiple environments.
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