Seravia in the Cloud
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The document discusses using various cloud technologies and tools for building a search engine called Seravia. It outlines using MongoDB for document storage, Sphinx for full-text search, and Amazon Web Services like EC2, S3, and EMR for scalable infrastructure and distributed computing. The architecture involves crawling data, preprocessing and analyzing it on AWS, then storing documents and search indexes across MongoDB and Sphinx to power the search engine deployed on EC2 instances behind a load balancer.
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Over 90% of today’s data was generated in the last 2 years, and the rate of data growth isn’t slowing down. In this session, we’ll step through the challenges and best practices on how to capture all the data that is being generated, understand what data you have, and start driving insights and even predict the future using purpose built AWS Services. We’ll frame the session and demonstrations around common pitfalls of building Data Lakes and how to successful drive analytics and insights from the data. This session will focus on the architecture patterns bringing together key AWS Services and rather than a deep dive on any single service. We’ll show how services such as Amazon S3, Amazon Glue, AWS Glue, Amazon Redshift, Amazon Athena, Amazon EMR, and Amazon Kinesis, and Amazon Machine Learning services are put together to build a successful data lake for various role including both data scientists and business users.
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Organizations often need to quickly analyze large amounts of data, such as logs generated from a wide variety of sources and formats. However, traditional approaches require a lot of time and effort designing complex data transformation and loading processes; and configuring data warehouses. Using AWS, you can start querying your datasets within minutes. In this session you will learn how you can deploy a managed Presto environment in minutes to interactively query log data using standard ANSI SQL. Presto is a popular open source SQL engine for running interactive analytic queries against data sources of all sizes. We will talk about common use cases and best practices for running Presto on Amazon EMR.
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Using Data Lakes: Data Analytics Week SF
Data Analytics Week at the San Francisco Loft
Using Data Lakes
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Hemant Borole - Sr. Big Data Consultant, AWS
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In this presentation, we will demonstrate how to use Amazon Elastic MapReduce as your scalable data warehouse. Amazon EMR supports clusters with thousands of nodes and is used to access petabyte scale data warehouses. Amazon EMR is not only fast, but it is also easy to use for rapid development and adhoc analysis. We will show you how access the large scale data warehouses with emerging tools such as Hue, Hive, low latency SQL applications like Presto, and alternative execution engines like Apache Spark. We will also show you how these tools integrate directly with other AWS big data services such as Amazon S3, Amazon DynamoDB, and Amazon Kinesis.
Using Data Lakes
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Using Data Lakes
A data lake can be used as a source for both structured and unstructured data - but how? We'll look at using open standards including Spark and Presto with Amazon EMR, Amazon Redshift Spectrum and Amazon Athena to process and understand data.
Level: Intermediate
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Hannah Marlowe - Consultant - Federal, AWS
Build Data Lakes and Analytics on AWS: Patterns & Best Practices - BDA305 - A...
In this session, we show you how to understand what data you have, how to drive insights, and how to make predictions using purpose-built AWS services. Learn about the common pitfalls of building data lakes and discover how to successfully drive analytics and insights from your data. Also learn how services such as Amazon S3, AWS Glue, Amazon Redshift, Amazon Athena, Amazon EMR, Amazon Kinesis, and Amazon ML services work together to build a successful data lake for various roles, including data scientists and business users.
Build Data Lakes & Analytics on AWS: Patterns & Best Practices - BDA305 - Ana...
In this session, we show you how to understand what data you have, how to drive insights, and how to make predictions using purpose-built AWS services. Learn about the common pitfalls of building data lakes, and discover how to successfully drive analytics and insights from your data. Also learn how services such as Amazon S3, AWS Glue, Amazon Redshift, Amazon Athena, Amazon EMR, Amazon Kinesis, and Amazon ML services work together to build a successful data lake for various roles, including data scientists and business users.
Full Stack Analytics on AWS - AWS Summit Cape Town 2017
This document summarizes key aspects of full stack analytics on AWS, including foundational services like storage, data ingestion, processing and analytics, machine learning, and security. It discusses AWS services like S3, Athena, Glue, Kinesis, Rekognition, and how they can be used together for cost-effective analytics from ingestion to machine learning to building smarter applications. Security is addressed at both the service and data levels using tools like IAM, encryption, and third party integration.
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This document discusses big data analytics and architectural principles for building big data solutions. It covers collecting and storing data from various sources, processing and analyzing data using services like Amazon Kinesis, Redshift, EMR and Athena, and choosing the right tools based on factors like data structure, access patterns, and latency requirements. Key principles emphasized include building decoupled systems, leveraging managed services, using event-driven architectures, and focusing on cost efficiency.
Semplificare l'analisi dei dati con architetture "Serverless": architetture e...
This document discusses serverless big data architectures using AWS services. It describes how serverless computing eliminates the need to provision and manage servers. AWS services like S3, DynamoDB, Kinesis, Lambda, Glue and Athena allow ingesting, storing, processing and analyzing data without managing infrastructure. These services scale automatically based on usage and provide built-in availability and fault tolerance.
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Overview
Dive into the world of anomaly detection on edge devices with our comprehensive hands-on tutorial. This SlideShare presentation will guide you through the entire process, from data collection and model training to edge deployment and real-time monitoring. Perfect for those looking to implement robust anomaly detection systems on resource-constrained IoT/edge devices.
Key Topics Covered
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- Understand the fundamentals of anomaly detection and its importance in identifying unusual behavior or failures in systems.
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Wir erklären Ihnen, wie Sie häufige Konfigurationsprobleme lösen können, die dazu führen können, dass mehr Benutzer gezählt werden als nötig, und wie Sie überflüssige oder ungenutzte Konten identifizieren und entfernen können, um Geld zu sparen. Es gibt auch einige Ansätze, die zu unnötigen Ausgaben führen können, z. B. wenn ein Personendokument anstelle eines Mail-Ins für geteilte Mailboxen verwendet wird. Wir zeigen Ihnen solche Fälle und deren Lösungen. Und natürlich erklären wir Ihnen das neue Lizenzmodell.
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My slides at Nordic Testing Days 6.6.2024
Climate impact / sustainability of software testing discussed on the talk. ICT and testing must carry their part of global responsibility to help with the climat warming. We can minimize the carbon footprint but we can also have a carbon handprint, a positive impact on the climate. Quality characteristics can be added with sustainability, and then measured continuously. Test environments can be used less, and in smaller scale and on demand. Test techniques can be used in optimizing or minimizing number of tests. Test automation can be used to speed up testing.
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- These are slides of the talk given at IEEE International Conference on Software Testing Verification and Validation Workshop, ICSTW 2022.
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Read Taking AI to the Next Level in Manufacturing to gain insights on AI adoption in the manufacturing industry, such as:
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2. Which barriers stand in the way of AI adoption.
3. How data quality and governance form the backbone of AI.
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Lambda, Elastic Beanstalk, Lightsail, Amplify, S3 (and more!) can each host websites + APIs. But which one should we choose?
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* Step-by-step implementation guide
* Live demos with code snippets
* Enhancing LLM capabilities with vector search
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#MongoDB #VectorSearch #AI #SemanticSearch #TechInnovation #DataScience #LLM #MachineLearning #SearchTechnology
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Seravia in the Cloud
- 1. Seravia in the Cloud Danny Yang May 7, 2011
- 3. Sample of Data 2.0 Companies
- 12. Seravia on AWS WWW Data Crawlware ELB EC2 rails, mongo, mysql, sphinx S3 EC2 parsing, pentaho, ETL S3 EMR hadoop, hive, BI EC2 S3 EC2 rails, mongo, mysql, sphinx
- 13. WWW architecture ELB EC2 webserver S3 EC2 webserver EC2 mongo EC2 sphinx EC2 mysql EC2 webserver, rails EC2 mongo EC2 sphinx
- 14. Data Architecture EC2 post-processing EC2 Parsing, ETL S3 EC2 Parsing, ETL EMR Hadoop, hive, BI EC2 post-processing 1. Raw data – html, xml, text files 2. Pre-processed – unrelated tsv files 3. Analyzed – related tsv files and reports 4. Post-processed – json documents EC2 post-processing
- 15. Crawlware Architecture EC2 Crawler EC2 Crawler EC2 Controller S3 EC2 Crawler EC2 Crawler EC2 Controller
- 17. Q & A