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Precisely
1w
281
Image Credit: Precisely
Mainframe Data Meets AI: Reducing Bias and Enhancing Predictive Power
- Mainframes store vast amounts of historical data which is comprehensive and diverse including transactions, demographic and behavioral data that is helpful in AI models to reduce bias in AI.
- Mainframe data can be useful in providing contextual insights, correcting imbalances in real-time data, inclusion of underrepresented groups, and diverse and rich historical data.
- However, legacy systems like mainframe data come with challenges, including data silos, data compatibility, security and compliance concerns, cost, and resource constraints.
- To overcome these challenges, organizations must invest in modernizing their mainframes, use data virtualization tools for accessing and analyzing mainframe data, and APIs to create connections between mainframes and AI platforms.
- Mainframe data, with its rich historical context and diverse demographic representation, will play an increasingly important role in overcoming bias in AI models.
- As AI advances and becomes more embedded in critical decision-making processes, the importance of reducing bias and ensuring fair outcomes will only grow.
- Organizations can unlock the full potential of their mainframe data along with AI systems that result in creating more accurate, equitable, and trustworthy AI systems.
- In conclusion, legacy mainframe data can play a crucial role in delivering successful AI outcomes, keeping up with the demands of the future and eliminating the bias in AI models.
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Dzone
1w
68
Image Credit: Dzone
Data Processing With Python: Choosing Between MPI and Spark
- Message Passing Interface (MPI) and Apache Spark are two popular frameworks used for parallel and distributed computing.
- MPI is a standardized and portable message-passing system designed for parallel computing, while Spark is an open-source analytics engine for processing large amounts of data.
- Spark is more convenient but may compromise on performance, while MPI offers flexibility and maximum performance.
- Spark is interpretive, offers less control over parallelism, and has JVM startup time implications, while MPI provides full control to the programmer and requires manual code implementation.
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Amazon
1w
303
Image Credit: Amazon
An integrated experience for all your data and AI with Amazon SageMaker Unified Studio (preview)
- Amazon SageMaker Unified Studio is an integrated development environment (IDE) for data, analytics, and AI.
- Organizations are building data-driven applications, but they require collaboration across teams and the integration of data, tools, and services.
- With SageMaker Unified Studio, organizations can adopt the best services for their use cases while empowering their data practitioners with a unified development experience.
- Users only need to learn SageMaker Unified Studio tools once and then they can use them across all services.
- The tools are integrated with Amazon Q, can quickly build, refine, and maintain applications with text-to-code capabilities.
- SageMaker Unified Studio tools offer a unified view of an application’s building blocks such as data, code, development artifacts, and compute resources across services to approved users.
- SageMaker Unified Studio automates and simplifies access management for different application blocks.
- You can ingests data to Amazon S3 and create a new table called venue_event_agg.
- SageMaker Unified Studio provides a unified JupyterLab experience across different languages, including SQL, PySpark, and Scala Spark.
- With SageMaker Unified Studio, data practitioners can access all the capabilities of AWS purpose-built analytics, AI/ML, and generative AI services from a single unified development experience.
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Siliconangle
2w
870
Image Credit: Siliconangle
How AWS Q Business aims to unlock data insights for everyone
- AWS has introduced Q Business, which aims to empower non-technical users while complementing developers in accessing and generating data insights.
- Q Business integrates business intelligence and workflow automation, allowing users to consolidate data from multiple sources and derive actionable insights through natural language queries.
- The platform also provides integration with Amazon QuickSight for data storytelling through interactive dashboards.
- AWS' goal is to simplify data utilization for all users and make advanced analytics and AI accessible to non-technical personnel.
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Amazon
2w
236
Image Credit: Amazon
Run Apache Spark Structured Streaming jobs at scale on Amazon EMR Serverless
- Spark Structured Streaming simplifies streaming data processing by providing a high-level API that supports batch-processing-like jobs. Businesses can scale up and down their computing infrastructure as needed with Amazon EMR Serverless to enable Spark Structured Streaming to handle streaming data.
- Amazon EMR Serverless provides fine-grained scaling to allow for optimal throughput and cost optimization. Fine-Grained Scaling is needed for real-world predicaments where data volumes are unpredictable, and workloads have sudden spikes.
- Enhanced Fan-Out support is available on Amazon Kinesis connector, which is pre-packaged in Amazon EMR Serverless. Enhanced Fan-Out provides each consumer with dedicated throughput of 2 MBps per shard, allowing for faster, more efficient data processing, which boosts the overall performance of streaming jobs on EMR Serverless
- Amazon EMR Serverless ensures resiliency in streaming jobs by leveraging automatic recovery and fault-tolerant architectures. Automatic event retry is also available with EMR Serverless for tackling transient runtime failures.
- EMR Serverless provides robust log management and enhanced monitoring for streaming jobs. The platform is integrated with Amazon Managed Service for Prometheus, enabling detailed engine metrics to be monitored, analyzed, and optimized.
- EMR Serverless supports Kinesis Data Streams, Amazon MSK, and self-managed Apache Kafka clusters as input data sources to keep up with diverse data processing pipelines accurately.
- Using Spark Structured Streaming on EMR Serverless is an efficient and cost-effective solution for real-time data processing. With the ease of integration with AWS services and automated resiliency features, it provides high availability and reliability, minimizing downtime and data loss.
- Anubhav Awasthi, Kshitija Dound, and Paul Min are AWS Solutions Architects who have co-authored this article.
- Organizations may try out Spark Structured Streaming on EMR Serverless and optimize it for their specific needs using the advanced monitoring tools. Comment with questions regarding use cases.
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Amazon
2w
399
Image Credit: Amazon
Federate to Amazon Redshift Query Editor v2 with Microsoft Entra ID
- Amazon Redshift is a fast, petabyte-scale, cloud data warehouse that tens of thousands of customers rely on to power their analytics workloads.
- AWS provides the Amazon Redshift Query Editor V2, a web-based tool that allows you to explore, analyze, and share data using SQL.
- The Query Editor V2 offers a user-friendly interface for connecting to your Redshift clusters, executing queries, and visualizing results.
- Many customers have already implemented identity providers (IdPs) like Microsoft Entra ID (formerly Azure Active Directory) for single sign-on (SSO) access across their applications and services.
- Through this federated setup, users can connect to the Redshift Query Editor using their existing Microsoft Entra ID credentials, allowing you to control permissions for database objects based on business groups defined in your Active Directory.
- In the following sections, we explore the process of federating into AWS using Microsoft Entra ID and AWS Identity and Access Management (IAM), and how to restrict access to datasets based on permissions linked to AD groups.
- You use the federation metadata file to configure the IAM IdP in a later step.
- In IAM, an IdP represents a trusted external authentication service like Microsoft Entra ID that supports SAML 2.0, allowing AWS to recognize user identities authenticated by that service.
- Next, you create an IAM role for SAML-based federation, which will be used to grant access to the Redshift Query Editor and Redshift cluster.
- In this post, we demonstrated how to use Microsoft Entra ID to federate into your AWS account and use the Redshift Query Editor V2 to connect to a Redshift cluster and access the schemas based on the AD groups associated with the user.
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Ubuntu
2w
141
Spark or Hadoop: the best choice for big data teams?
- Apache Spark is an open-source, distributed processing system that allows large amounts of data to be processed efficiently.
- Spark solved several performance problems related to processing large datasets, making it the number one choice in the industry and a direct competitor of Hadoop.
- Spark's strength is distributed computing, making it a champion for operations on large datasets.
- Apache Spark’s architecture consists of three main components: the driver, the executor, and the partitioner. It utilises a manager/worker configuration, where a manager determines the number of worker nodes needed and how they should function.
- Generally, Spark's advantage over Hadoop is speed. Spark is able to perform tasks up to 100 times faster than Hadoop, making it a great solution for low-latency processing use cases, such as machine learning.
- Using Apache Spark on Kubernetes offers numerous advantages over other cluster resource managers, such as Apache YARN, including simplified deployment, management, and authentication.
- Spark offers four main built-in libraries: Spark SQL, Spark Streaming, MLlib and GraphX, providing a large set of functionalities for different operations, such as data streaming, dataset handling, and machine learning.
- Common use cases for Spark include processing large volumes of data, complex operations, scalability requirements, performance improvements for large datasets, and machine learning.
- It is not always the case that Apache Spark and Hadoop are competing solutions and they can be used together depending on business needs.
- Canonical’s Charmed Apache Spark on Kubernetes simplifies the deployment and management process, offering greater flexibility, performance, and ease of use, ensuring quick, reliable, and scalable data processing.
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Amazon
2w
361
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Build Write-Audit-Publish pattern with Apache Iceberg branching and AWS Glue Data Quality
- Managing high quality data while vetting reliability of continuously incoming data is a challenge for data-driven organizations. AWS Glue helps manage data quality via Data Quality Definition Language (DQDL) and Apache Iceberg is table format for data lake management. DLQ and WAP strategy used for vetting data quality in streaming environment, each with their unique advantages. DLQ focuses on efficiently segregating high-quality data where problematic entries are redirected to a separate DLQ. On the other hand, WAP pattern uses Iceberg branch feature to segregate problematic entries from high-quality data so that only clean data is published in the main branch. The multistep process of WAP pattern results in data latency for downstream consumers. It is implementation dependent and thus requires more sophisticated orchestration compared to DLQ approach.
- WAP pattern implements a three-stage process: write, audit and publish where data is initially written to a staging branch, data quality checks are performed on staging branch and validated data is merged into main branch for consumption. Iceberg's branching feature is particularly useful for efficiently implementing WAP pattern. Each branch can be referred to and updated separately and ACID transactions and schema evolution provide useful ways for handling multiple concurrent writers in data with varying schemas.
- An example use case to demonstrate ice-berg branching and AWS glue data quality is given where home monitoring system tracks room temperature and humidity. Incoming data for temperature and humidity is evaluated for quality before being visualized so that only qualified room data is used for further data analysis. The alert system focuses on quality checks using AWS Glue Data Quality and the room data is evaluated against a rule set that contains the normal temperature range of -10 to 50 degrees. A new branch, namely the audit branch containing only valid room data is created. Finally, the publish phase fast-forwards the validated data to the main branch, after which the valid data is now ready for use by downstream applications.
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Amazon
2w
73
Image Credit: Amazon
Implement historical record lookup and Slowly Changing Dimensions Type-2 using Apache Iceberg
- Tracking and analyzing changes over time has become essential in today’s data-driven world. Apache Iceberg provides a feature known as the change log view that enables us to track insertions, updates, and deletions, giving us a complete picture of how our data has evolved.
- Slowly Changing Dimensions (SCD) Type-2 creates new rows for changed data instead of overwriting existing records, allowing for comprehensive tracking of changes over time.
- With Iceberg, you can create a dedicated view of SCD Type-2 on top of the change log view, eliminating the need to implement specific handling to make changes on SCD Type-2 tables. This approach combines the power of Iceberg’s efficient data management with the historical tracking capabilities of SCD Type-2.
- SCD Type-2 requires additional fields such as effective_start_date, effective_end_date, and current_flag to manage historical records. In traditional implementations, SCD Type-2 requires specific handling in all INSERT, UPDATE, and DELETE operations that affect those additional columns.
- Using Iceberg’s change log view, you can obtain the history of a given record directly from the Iceberg table’s history, without needing to create a separate table for managing record history. This streamlined method not only makes the implementation of SCD Type-2 more straightforward, but also offers improved performance and scalability for handling large volumes of historical data in CDC scenarios.
- SCD Type-2 enables point-in-time analysis, provides detailed audit trails, aids in data quality management, and helps meet compliance requirements by preserving historical data. It is particularly relevant to Change Data Capture (CDC) scenarios, where capturing all data changes over time is crucial.
- This tutorial demonstrates how to implement historical record management and SCD Type-2 using Apache Iceberg, focusing on a typical CDC architecture. It also showcases how a change log view aids historical analysis, improving possibilities for advanced time-based analytics, auditing, and data governance.
- The change log view does not lose any historical record changes even when undergoing compaction. However, the change log view loses historical record changes corresponded to snapshots deleted with expire_snapshots and Glue Data Catalog automatic snapshot deletion. It is not supported in MoR tables.
- Implementing Iceberg’s change log view and SCD Type-2 enables you to manage the history of records and tables without extra effort. It shows how this approach can be implemented, showcasing the efficiency and flexibility it brings to historical data analysis and CDC processes.
- Apache Iceberg makes the implementation of SCD Type-2 more manageable and offers improved performance and scalability for handling large volumes of historical data in CDC scenarios.
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Siliconangle
2w
292
Image Credit: Siliconangle
AWS leads the charge in sustainable data centers with AI-ready innovations
- Amazon Web Services Inc. is focused on creating highly efficient and reliable data centers with innovative solutions.
- AWS is embracing liquid cooling techniques and scalable infrastructure to save more energy.
- They have seen a 46% improvement in efficiency during peak cooling times by using computational fluid dynamics.
- AWS aims to decrease costs, carbon output, and improve efficiency in training AI models.
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Cloudera
2w
133
Image Credit: Cloudera
Introducing Accelerator for Machine Learning (ML) Projects: Summarization with Gemini from Vertex AI
- Cloudera has released a new Accelerator for Machine Learning (ML) Projects (AMP) called 'Summarization with Gemini from Vertex AI'.
- The AMP is a pre-built MVP for AI use cases that can be deployed in a single-click from Cloudera AI (CAI).
- The aim of the AMP is to provide an AI application prototype for document summarization and showcase the ease of building AI applications using Cloudera AI and Google's Vertex AI Model Garden.
- The Gemini Pro Models used in the AMP offer superior speed and competitive pricing for text summarization applications.
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Cloudera
2w
366
Image Credit: Cloudera
Scaling AI Solutions with Cloudera: A Deep Dive into AI Inference and Solution Patterns
- Cloudera is offering AI Inference, a production-grade environment to deploy artificial intelligence (AI) models at scale.
- The architecture of AI Inference ensures low-latency, high-availability deployments, ideal for enterprise-grade applications.
- The service supports a wide range of models, from traditional predictive models to advanced generative AI, such as large language models and embedding models.
- With support for Open Inference Protocol and OpenAI API standards, Cloudera AI Inference can deploy models for different AI tasks, such as language generation and predictive analytics.
- Cloudera AI Inference supports canary deployments for smoother rollouts where a new model version can be tested on a subset of traffic before full rollout.
- Cloudera's Professional Services provide a blueprint of best-practice frameworks for scaling AI by encompassing all aspects of the AI lifecycle from data engineering to real-time inference and monitoring.
- Cloudera's platform provides a strong foundation for GenAI applications, supporting everything from secure hosting to end-to-end AI workflows.
- Cloudera DataFlow, powered by NiFi, enables seamless data ingestion from Amazon S3 to Pinecone, creating a robust knowledge base, allowing fast, searchable insights in Retrieval-Augmented Generation applications.
- Cloudera provides pre-built accelerators (AMPs) and ReadyFlows to speed up AI application deployment.
- Cloudera's Professional Services team brings expertise in tailored AI deployments, from pilot projects to full-scale production, ensuring AI implementations align with business objectives.
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Precisely
2w
81
Image Credit: Precisely
2025 Planning Insights: Data Governance Adoption Has Risen Dramatically
- 71% of organizations have a data governance program, compared to 60% in 2023.
- Improved quality of data analytics and insights, improved data quality, and increased collaboration are the top reported benefits of data governance programs.
- Data governance is a top data integrity challenge, cited by 54% of organizations.
- 62% of respondents report data governance as a top data challenge to artificial intelligence (AI) initiatives.
- The demand for data governance is also driven by data privacy and security, which are in the top three priorities for improving data integrity in 2024 (45%).
- Data mesh and data fabric moved forward as trends influencing data programs, jumping five percentage points from 13% in 2023 to 18% in 2024.
- 45% of respondents report that regulatory compliance is a goal of their governance program.
- 54% of respondents report data governance as a top data integrity challenge.
- Data governance is seeing remarkable growth – propelled by evolving business needs and advancing technical trends.
- Effective implementation and maintenance of a robust data governance program is necessary to ensure its effectiveness and sustainability.
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TechBullion
2w
212
Image Credit: TechBullion
How to Achieve Personalized Customer Experiences Through Big Data
- Personalized experiences have become a vital differentiator for businesses. To achieve personalized customer experiences effectively businesses need to make use of Big Data. Twenty tips collected from Founders, CEOs and a Senior Data Scientist reveal how data-driven insights can create value on both ends of the recruitment equation. By analyzing data from buying patterns, they can find common combinations that customers might find appealing and create tailor-made shopping experiences, resulting in increased customer satisfaction and boosted sales. Analyzing customer behavior can help businesses create personalized recommendations that are efficiently managed with an AI-driven recommendation engine.
- It has been noted that Big Data can transform how customers experience personalized support. Those with unique skin sensitivities or conditions can benefit from the strategic use of data, which has enabled the creation of custom skincare devices. By using predictive analytics to anticipate clients' needs, big data can provide customized loan options that align with each client’s financial goals in the mortgage industry. Big Data strategies have led to impressive results within the fitness industry through targeted promotions at critical moments in customers’ journeys.
- Using this platform, companies can curate personalized marketing campaigns and provide accurate buying insights to increase conversions, shorten sales cycle times and send timely follow-ups based on real-time data triggers. Another success story is the integration of Big Data into user behavior, which has wowed customers with personalized usage recommendations, provided personalized tutorials, and helped create deeper connections with clients that foster customer satisfaction and trust.
- Big Data can also facilitate the creation of highly personalized customer experiences in smaller local businesses by providing custom daily spreadsheets of actionable insights like recent market trends, updated customer demographics, local competitor activity, and real-time changes in key industry metrics. This information offers businesses a deeper understanding of how their customers feel about their services throughout the customer journey, incorporating valuable feedback, and sentiment analysis.
- Big data allows businesses to anticipate customer needs and address concerns before they escalate, ensuring customer experience remains positive and enhancing VFM. By continually refining their processes, businesses are able to remain competitive while building lasting relationships with clients. Big Data aides in making customers understood and supported, leading to stronger relationships and higher lifetime value.
- It has been noted that the whole key to delivering a personalized experience at scale lies in the pairing of Big Data and generative AI to provide tailored solutions for every industry vertical. The automation of the content and demo generation process has allowed hyper-personalized experiences at scale making generative AI an excellent tool to achieve personalization with digits.
- The key to unlocking the potential of personalizing a user experience lies within Big Data's ability to provide thousands of data points daily. Raw data is processed and transformed into a format suitable for ingestion into predictive algorithms to forecast key metrics like customer churn and segmentation. Leveraging a scoring matrix that triggers alerts when thresholds are breached in specific areas or regions empowers businesses to ease targeted action plans effectively.
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Cloudera
2w
347
Image Credit: Cloudera
The Struggle Between Data Dark Ages and LLM Accuracy
- The AI Forecast: Data and AI in the Cloud Era is a podcast that explores the impact of AI on business and industry.
- LLM precision, especially in areas like supply chain and finance, is crucial for accuracy.
- Obtaining a higher level of precision in LLMs requires capturing context and metadata.
- As data availability decreases, companies will rely on data collectives and value chains to share information.
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