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Towards Data Science
2w
276
Image Credit: Towards Data Science
Mastering Hadoop, Part 3: Hadoop Ecosystem: Get the most out of your cluster
- Apache Hive enables querying HDFS data using a SQL-like language without complex MapReduce processes.
- Hive was developed by Facebook for processing structured and semi-structured data, useful for batch analyses.
- Metastore in Hive stores metadata like table definitions and column names to manage large datasets.
- HiveQL queries are converted by the execution engine into tasks for processing by Hadoop.
- Hive performance can be optimized using partitioning for faster searching and organizing data into buckets for efficient joins.
- Apache Pig facilitates parallel processing of data in Hadoop using Pig Latin language for ETL of semi-structured data.
- HBase is a NoSQL database in Hadoop that stores data in a column-oriented manner for efficient querying.
- Amazon EMR offers managed big data service with support for Hadoop, Spark, and other frameworks in the cloud.
- Apache Presto allows real-time distributed SQL queries in large systems without schema definition.
- Apache Flink is designed for distributed stream processing in real-time with low latency.
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Amazon
2w
386
Image Credit: Amazon
Architect fault-tolerant applications with instance fleets on Amazon EMR on EC2
- Amazon EMR on EC2 clusters help process large-scale data workloads using frameworks like Apache Spark, Hive, and Trino, but effective capacity planning is essential for managing sudden demand spikes.
- Using consistent EC2 instance types for daily Spark jobs on Amazon EMR can lead to capacity constraints during spikes, necessitating the need for auto scaling and flexible strategies.
- Instance fleets in Amazon EMR offer a flexible way to manage EC2 instances and support Amazon EC2 On-Demand Capacity Reservations for predictable workloads.
- Stable workloads with predictable resource usage benefit from reserving baseline capacity using ODCRs and configuring EMR clusters accordingly.
- Spiky workloads with fluctuating demands require flexibility through instance fleet strategies, intelligent subnet selection, and managed scaling for optimal resource allocation.
- Creating Capacity Reservations and Resource Groups, associating them, and implementing them in EMR clusters with targeted ODCRs helps optimize capacity while ensuring reliability.
- Using Amazon CloudWatch for monitoring ODCR usage and creating resource groups like EMRSparkSteadyStateGroup with proper tagging enhances capacity reservation management.
- For spiky workloads, incorporating EC2 instance flexibility, prioritized allocation strategies, multi-AZ deployment, and managed scaling in EMR clusters improves availability and cost-effectiveness.
- Prioritized allocation strategies and combining instance types in instance fleets enhance resource provisioning and cost optimization for varying workload demands.
- Using diverse instance types, subnets across AZs, and managed scaling in Amazon EMR clusters help balance cost, availability, and performance for optimal resource utilization.
- Implementing a hybrid approach with ODCRs for baseline capacity and strategic instance fleet configurations can effectively manage both predictable and unpredictable workload patterns on Amazon EMR.
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Currentanalysis
2w
208
Image Credit: Currentanalysis
AI Agents Take Center Stage at Salesforce TDX25
- Salesforce introduced AgentExchange, a marketplace for preconfigured AI agents to seamlessly integrate.
- Interoperability among agents and frameworks will be crucial as organizations deploy multiple agents for complex tasks.
- Salesforce's annual developer conference, TDX25, focused heavily on AI agents and the Agentforce platform.
- Salesforce announced Agentforce 2dx suite, an API, partnerships, and use case discussions at TDX25.
- Enterprises are still exploring AI agents; questions arise on use cases, adoption strategies, and interoperability.
- Tasks in customer service, sales, and marketing are prime for AI agent implementation leveraging Salesforce's value proposition.
- Salesforce launched Agentforce for Salesforce Platform and introduced Agentforce 2dx for developers at TDX25.
- AgentExchange was a notable reveal at TDX25, providing a marketplace for over 200 partners to scale AI agent usage.
- Agent interoperability is crucial for deploying complex agents; AgentExchange addresses this challenge.
- AI agents support expected growth through 2025 with enhanced tooling, AI model evaluation, and model interoperability.
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Towards Data Science
2w
271
Image Credit: Towards Data Science
Anatomy of a Parquet File
- Parquet files are produced using PyArrow, which allows for fine-tuned parameter tuning.
- Dataframes in Parquet files are stored in a columns-oriented storage format, unlike Pandas' row-wise approach.
- Parquet files are commonly stored in object storage databases like S3 or GCS for easy access by data pipelines.
- A partitioning strategy organizes Parquet files in directories based on partitioning keys like birth_year and city.
- Partition pruning allows query engines to read only necessary files, based on folder names, reducing I/O.
- Decoding a raw Parquet file involves identifying the 'PAR1' header, row groups with data, and footer holding metadata.
- Parquet uses a hybrid structure, partitioning data into row groups for statistics calculation and query optimization.
- Page size in Parquet files is a trade-off, balancing memory consumption and data retrieval efficiency.
- Encoding algorithms like dictionary encoding and compression are used for optimizing columnar format in Parquet.
- Understanding Parquet's structure aids in making informed decisions on storage strategies and performance optimization.
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Amazon
2w
130
Image Credit: Amazon
Accelerate analytics and AI innovation with the next generation of Amazon SageMaker
- Amazon SageMaker announced the next generation at AWS re:Invent 2024, aiming to accelerate analytics and AI innovation.
- The new Amazon SageMaker integrates AWS ML and analytics capabilities to facilitate data utilization for analytics and AI with governance.
- Amazon SageMaker Unified Studio is a single environment for data and AI development to access and analyze organizational data efficiently.
- Unified Studio includes features from various AWS Analytics and AI services, enabling collaboration on data projects securely.
- SageMaker Lakehouse offers unified access to data stored in different sources like S3 and Redshift.
- Amazon Bedrock capabilities in Unified Studio allow rapid development of generative AI applications in a governed environment.
- Amazon Q Developer assists in software development tasks within SageMaker Unified Studio for a streamlined workflow.
- SageMaker Unified Studio reduces complexity and time in developing data-driven applications for businesses.
- The article highlights the benefits of using SageMaker Unified Studio for lead generation and revenue enhancement scenarios.
- The integrated environment offered by Unified Studio simplifies the process, leading to faster time-to-value for analytics and AI projects.
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TechBullion
2w
86
Image Credit: TechBullion
6 Benefits of Using Clinical Data Management Software
- Clinical data management software (CDMS) is gaining popularity in the healthcare industry due to its potential to streamline data management processes and reduce errors.
- One key benefit of CDMS is its ability to prevent mistakes in patient records by using automated checks and alerts to catch inaccuracies before they cause harm.
- CDMS helps in organizing workflows, assigning tasks, tracking progress, and providing notifications to ensure efficient coordination among stakeholders in clinical trials.
- Digital records in CDMS facilitate quick retrieval of patient information, leading to faster decision-making and more efficient care delivery.
- CDMS enhances security measures by utilizing encryption, role-based access, and audit trails to protect sensitive patient data from cyberattacks and unauthorized access.
- Compliance with healthcare regulations such as HIPAA and GDPR is critical, and CDMS software is designed with these regulations in mind to maintain data security and patient trust.
- Implementing CDMS can result in long-term cost savings for healthcare facilities by reducing the need for paper-based processes, storage, and administrative tasks.
- Automating routine administrative tasks through CDMS allows clinical data managers to focus on essential activities like patient care, research, and operational improvements.
- CDMS streamlines data handling in clinical trials, improving accuracy and efficiency by eliminating manual processes and reducing the risk of errors.
- Considering the various benefits of clinical data management software, organizations can evaluate its potential impact and suitability for their specific needs.
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Siliconangle
2w
117
Image Credit: Siliconangle
Business intelligence startup Omni closes $69M funding round
- Business intelligence startup Omni has closed a $69 million funding round led by ICONIQ Growth.
- Omni's sales grew eightfold in the past year and the company generates nearly $10 million in annualized revenue.
- Omni provides a business intelligence platform that allows companies to turn their data into graphs and dashboards to monitor ad campaign performance.
- The funding will be used for product development, embedding graphs in other applications, and expanding the company's workforce.
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Towards Data Science
2w
371
Image Credit: Towards Data Science
Mastering Hadoop, Part 2: Getting Hands-On — Setting Up and Scaling Hadoop
- Hadoop Ozone, a distributed object storage system, was added to the Hadoop architecture in 2020 as an alternative to HDFS for better handling modern data requirements.
- HDFS stores files divided into blocks distributed across nodes, replicated three times for data integrity.
- Hadoop follows a master-slave principle with NameNode as master and DataNodes storing data blocks.
- MapReduce enables parallel processing, with mappers splitting tasks and reducers aggregating results.
- YARN manages cluster resources efficiently, separating resource management from data processing.
- Hadoop Common provides foundational components for the Hadoop ecosystem for seamless operation of all components.
- Hadoop Ozone offers a scalable storage solution optimized for Kubernetes and cloud environments.
- Hadoop can be installed locally for single-node testing and can be scaled in a distributed environment.
- Hadoop can also be deployed in the cloud with providers offering automated scaling and cost-efficient solutions.
- Basic commands in Hadoop enable data storage, processing, and debugging for efficient cluster management.
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Amazon
2w
134
Image Credit: Amazon
Announcing end-of-support for Amazon Kinesis Client Library 1.x and Amazon Kinesis Producer Library 0.x effective January 30, 2026
- Amazon Kinesis Client Library (KCL) 1.x and Amazon Kinesis Producer Library (KPL) 0.x will reach end-of-support on January 30, 2026.
- KCL and KPL will enter maintenance mode on April 17, 2025, receiving updates only for critical bug fixes and security issues.
- KCL is used for processing streaming data from Amazon Kinesis Data Streams, handling tasks such as load balancing and checkpointing.
- KPL helps in achieving high throughput data into Kinesis Data Streams, managing batching and retry logic for producer applications.
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Amazon
2w
113
Image Credit: Amazon
Deploy real-time analytics with StarTree for managed Apache Pinot on AWS
- StarTree provides a managed alternative to Apache Pinot on AWS, offering benefits for real-time analytics use cases.
- StarTree, founded by Kishore Gopalakrishna, handles over 1 billion queries per week and over 1 million events per second.
- Compared to open-source Pinot, StarTree streamlines infrastructure management for real-time analytics, allowing organizations to focus on insights.
- StarTree offers enterprise-grade security with RBAC, SOC 2 compliance, encryption, and SSO capabilities.
- StarTree automates data ingestion at scale, supporting various connectors to seamlessly ingest and model data for optimized query performance.
- StarTree's tiered storage system transitions data efficiently between hot and cold storage, optimizing both performance and cost.
- StarTree enhances scalability with off-heap upserts, supporting companies like Amberdata in handling high event workloads.
- StarTree customer success stories include Sovrn, Amberdata, and Nubank, showcasing improved query performance, reduced SLA times, and cost savings.
- StarTree offers flexible deployment options, including hosted SaaS for operational ease or customer hosted SaaS for more control.
- Organizations can choose between self-managed Pinot or StarTree based on their preferences for infrastructure management and operational ease.
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TechBullion
2w
334
Image Credit: TechBullion
How AI and Data Engineering Are Transforming Enterprise Data Architecture
- AI and cloud-based data engineering are transforming enterprise data architecture, enabling businesses to improve agility and efficiency.
- Digvijay Waghela, a Data Architect at Chewy, played a crucial role in the successful DBT Snowflake Migration project, bringing advancements to data management.
- The project included the integration of Snowflake with various AWS-based applications, improving real-time data visibility and cross-functional analytics.
- AI-powered automation and modular architecture are driving efficiency and long-term business growth, setting a new industry benchmark for cloud-based analytics.
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Siliconangle
2w
217
Image Credit: Siliconangle
Ditto raises $82M in funding for its edge database
- Ditto, the developer of a database optimized for use in edge environments, has raised $82 million in funding.
- Top Tier Capital Partners and Acrew Capital led the Series B investment round.
- The funding brings Ditto's valuation to $462 million, doubling its worth from previous funding.
- Ditto's edge-optimized database allows companies to build custom applications for employees and includes features such as offline capabilities and peer-to-peer networking.
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Currentanalysis
2w
140
Image Credit: Currentanalysis
AWS Offers Deepseek-R1 on Amazon Bedrock as US Companies Embrace the Chinese Startup
- AWS offers DeepSeek-R1 as a fully managed, serverless large language model on Amazon Bedrock.
- DeepSeek-R1, a generative AI model, provides advanced reasoning capabilities and reduced computing costs.
- The inclusion of DeepSeek-R1 in Amazon Bedrock allows users to leverage the technology with built-in security and observability.
- Thousands of customers have already deployed DeepSeek-R1 model on Amazon Bedrock since its launch in January 2025.
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Precisely
2w
392
Image Credit: Precisely
How to Make Better Data-Driven Decisions as a Customer Experience Leader
- Customer experience leaders understand the importance of making timely, data-driven decisions to meet customer demands and enhance loyalty.
- Integrating legacy systems into a unified CCM platform can streamline decision-making by providing centralized content control and enhanced visibility into customer interactions.
- Accessing the right customer data at the right time is essential for personalized and efficient communication, achievable through a unified CCM platform.
- Empowering teams with the ability to create personalized communications and offer self-service options can reduce reliance on IT and improve customer satisfaction.
- Reliable archiving capabilities ensure accessible and well-organized customer data for accurate and timely decision-making in customer interactions.
- A unified CCM solution accelerates delivery, reduces operational costs, and empowers teams to handle communication updates faster, leading to improved customer experiences.
- Real-world successes of a unified CCM solution include faster time to market, self-management of communications, and simplification of communication management.
- Centralizing customer communications enhances internal processes and customer experiences, leading to clearer, consistent, and convenient interactions.
- A unified customer communication solution simplifies content creation, ensures messages are data-driven, and delivers engaging, personalized experiences for improved retention and satisfaction.
- By leveraging data-driven decisions and unified solutions, customer experience leaders can create seamless experiences that drive loyalty, trust, and long-term success.
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Amazon
2w
418
Image Credit: Amazon
Develop and test AWS Glue 5.0 jobs locally using a Docker container
- AWS Glue 5.0 offers performance-optimized Apache Spark 3.5 runtime for data integration at scale.
- Developers can use Python or Scala with the AWS Glue ETL library for job creation.
- AWS provides an official AWS Glue Docker image on Amazon ECR Public Gallery for local development.
- Developing and testing AWS Glue 5.0 jobs locally using a Docker container is demonstrated.
- AWS Glue 5.0 Docker image includes Apache Spark, various libraries, and connectors.
- Prerequisites for setting up and configuring AWS Glue Docker container are mentioned.
- Options like spark-submit, REPL shell (pyspark), pytest, and Visual Studio Code are available for job testing.
- Differences between AWS Glue 4.0 and 5.0 Docker images are highlighted.
- Considerations and features not supported when using AWS Glue container images are discussed.
- The article concludes by emphasizing AWS Glue 5.0 Docker images' flexibility for development and testing.
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