Qo comparision
•Download as PPTX, PDF•
0 likes•173 views
The document compares several analytical methodologies for performing online advertising campaign analysis on a 2 billion+ impression log file with 350 million unique users. It finds that: 1) iQO delivered the fastest and most uniform performance, with most queries returning in less than 5 seconds. 2) Hadoop and Aster had median response times of several hours, though still 3-4 times faster than relational databases. 3) iQO was the only approach that was able to scale to petabyte volumes and answer 100% of queries, while other approaches like Hadoop would require unwieldy hardware at that scale.
Report
Share
Report
Share
![XLDB Analytics in on-line advertising OVERVIEW A comparison of several analytical methodologies was conducted by performing a standard on-line advertising campaign and audience analysis consisting of 67 queries against a 2 billion+ impression log file with 350 million unique “user-ids”. Methodologies included in the study: ,[object Object]](data:image/gif;base64,R0lGODlhAQABAIAAAAAAAP///yH5BAEAAAAALAAAAAABAAEAAAIBRAA7)
Recommended
Query O
A comparison of several analytical methodologies for performing online advertising campaigns and audience analysis found that iQO delivered the fastest query response times and most uniform performance, answering 100% of queries in 5 seconds or less, while using the fewest resources. Hadoop and Aster Data required large server arrays, custom programming, and query response times measured in minutes to days. Relational databases like MySQL and Oracle could not scale to daily terabyte volumes and returned queries too slowly to measure.
Managing R&D Data on Parallel Compute Infrastructure
Clinical genomic analytics pipelines using Databricks and the Delta Lake for the benefit of loading individual reads from raw sequencing or base-call files have significant advantages over more traditional methods. Analysis pipelines that perform genomic mapping to purpose-built reference data artifacts persisted to tables allows for enhanced performance that is magnitudes greater than previous mapping methods. These scalable, reproducible, and potentially open sourced methods have the ability to transform bioinformatics and R&D data management / governance.
Unifying Streaming and Historical Telemetry Data For Real-time Performance Re...
"In the oil and gas industry, utilizing vast amounts of data has long been identified as an important indicator of operational performance. The measurement of key performance indicators is a routine practice in well construction, but a systematic way of statistically analyzing performance against a large data bank of offset wells is not a common practice. The performance of statistical analysis in real-time is even less common. With the adoption of distributed computing platforms, like Apache Spark, new analysis opportunities become available to leverage large-scale time-series data sets to optimize performance. Two case studies are presented in this talk: the rate of penetration (ROP) and the amount of vibration per run.
By collecting real-time, telemetry data and comparing it with historic sample datasets within the Databricks Unified Analytics Platform, the optimization team was able to quickly determine whether the performance being delivered matched or exceeded past performance with statistical certainty. This is extremely important while trying new techniques with data that is highly variable. By substituting anecdotal evidence with statistical analysis, decision making is more precise and better informed. In this talk we'll share how we accomplished this and the lessons learned along the way."
Spark and Hadoop at Production Scale-(Anil Gadre, MapR)
This document discusses how MapR helps companies take Spark to production scale. It provides examples of companies using Spark and MapR for real-time security analytics, genomics research, and customer analytics. MapR offers high performance, reliability, and support to ensure Spark runs successfully in production environments. The document also announces new Spark-based quick start solutions from MapR and promotes free MapR training.
Building an Enterprise Data Platform with Azure Databricks to Enable Machine ...
This document summarizes Walmart's transition to building an enterprise data platform on Azure Databricks to enable machine learning and data science at scale. Previously, Walmart had a complex and slow legacy technology stack. The new platform goals were to centralize data in the cloud, increase productivity with data science tools, and reduce costs. Key aspects of the new platform included using Azure and Databricks for data processing and machine learning, Airflow for orchestration, and building several machine learning models for applications like fraud detection and product recommendations. Challenges in the transition included optimizing performance and managing resources across the platforms.
Real-Time Forecasting at Scale using Delta Lake and Delta Caching
GumGum receives around 30 billion programmatic inventory impressions amounting to 25 TB of data each day. Inventory impression is the real estate to show potential ads on a publisher page. By generating near-real-time inventory forecast based on campaign-specific targeting rules, GumGum enables the account managers to set up successful future campaigns.
Brandon obrien streaming_data
The document discusses streaming data opportunities and challenges, and outlines Expedia's streaming data ecosystem. It describes how Expedia uses a Kafka streaming data ecosystem to enable decoupled systems and roadmaps, and make it easy for teams to publish and consume streaming data. Key components of Expedia's ecosystem include a centralized data depot, self-service tools, elastic components, and monitoring of velocity and lag. The ecosystem provides producers with an HTTP ingestor to publish to Kafka and S3, and consumers can create apps using built-in Kafka integration. Example use cases of streaming analytics on this ecosystem are also mentioned.
Optimize Data for the Logical Data Warehouse
Rodan Zadeh, Director of Product Management at Attunity talks about how to optimize data for the logical data warehouse for the Cisco Virtual Tradeshow.
Recommended
Query O
A comparison of several analytical methodologies for performing online advertising campaigns and audience analysis found that iQO delivered the fastest query response times and most uniform performance, answering 100% of queries in 5 seconds or less, while using the fewest resources. Hadoop and Aster Data required large server arrays, custom programming, and query response times measured in minutes to days. Relational databases like MySQL and Oracle could not scale to daily terabyte volumes and returned queries too slowly to measure.
Managing R&D Data on Parallel Compute Infrastructure
Clinical genomic analytics pipelines using Databricks and the Delta Lake for the benefit of loading individual reads from raw sequencing or base-call files have significant advantages over more traditional methods. Analysis pipelines that perform genomic mapping to purpose-built reference data artifacts persisted to tables allows for enhanced performance that is magnitudes greater than previous mapping methods. These scalable, reproducible, and potentially open sourced methods have the ability to transform bioinformatics and R&D data management / governance.
Unifying Streaming and Historical Telemetry Data For Real-time Performance Re...
"In the oil and gas industry, utilizing vast amounts of data has long been identified as an important indicator of operational performance. The measurement of key performance indicators is a routine practice in well construction, but a systematic way of statistically analyzing performance against a large data bank of offset wells is not a common practice. The performance of statistical analysis in real-time is even less common. With the adoption of distributed computing platforms, like Apache Spark, new analysis opportunities become available to leverage large-scale time-series data sets to optimize performance. Two case studies are presented in this talk: the rate of penetration (ROP) and the amount of vibration per run.
By collecting real-time, telemetry data and comparing it with historic sample datasets within the Databricks Unified Analytics Platform, the optimization team was able to quickly determine whether the performance being delivered matched or exceeded past performance with statistical certainty. This is extremely important while trying new techniques with data that is highly variable. By substituting anecdotal evidence with statistical analysis, decision making is more precise and better informed. In this talk we'll share how we accomplished this and the lessons learned along the way."
Spark and Hadoop at Production Scale-(Anil Gadre, MapR)
This document discusses how MapR helps companies take Spark to production scale. It provides examples of companies using Spark and MapR for real-time security analytics, genomics research, and customer analytics. MapR offers high performance, reliability, and support to ensure Spark runs successfully in production environments. The document also announces new Spark-based quick start solutions from MapR and promotes free MapR training.
Building an Enterprise Data Platform with Azure Databricks to Enable Machine ...
This document summarizes Walmart's transition to building an enterprise data platform on Azure Databricks to enable machine learning and data science at scale. Previously, Walmart had a complex and slow legacy technology stack. The new platform goals were to centralize data in the cloud, increase productivity with data science tools, and reduce costs. Key aspects of the new platform included using Azure and Databricks for data processing and machine learning, Airflow for orchestration, and building several machine learning models for applications like fraud detection and product recommendations. Challenges in the transition included optimizing performance and managing resources across the platforms.
Real-Time Forecasting at Scale using Delta Lake and Delta Caching
GumGum receives around 30 billion programmatic inventory impressions amounting to 25 TB of data each day. Inventory impression is the real estate to show potential ads on a publisher page. By generating near-real-time inventory forecast based on campaign-specific targeting rules, GumGum enables the account managers to set up successful future campaigns.
Brandon obrien streaming_data
The document discusses streaming data opportunities and challenges, and outlines Expedia's streaming data ecosystem. It describes how Expedia uses a Kafka streaming data ecosystem to enable decoupled systems and roadmaps, and make it easy for teams to publish and consume streaming data. Key components of Expedia's ecosystem include a centralized data depot, self-service tools, elastic components, and monitoring of velocity and lag. The ecosystem provides producers with an HTTP ingestor to publish to Kafka and S3, and consumers can create apps using built-in Kafka integration. Example use cases of streaming analytics on this ecosystem are also mentioned.
Optimize Data for the Logical Data Warehouse
Rodan Zadeh, Director of Product Management at Attunity talks about how to optimize data for the logical data warehouse for the Cisco Virtual Tradeshow.
Delivering digital transformation and business impact with io t, machine lear...
A world-leading manufacturer was in search of an IoT solution that could ingest, integrate, and manage data being generated from various types of connected machinery located on factory floors around the globe. The company needed to manage the devices generating the data, integrate the flow of data into existing back-end systems, run advanced analytics on that data, and then deliver services to generate real-time decision making at the edge.
In this session, learn how Clairvoyant, a leading systems integrator and Red Hat partner, was able to accelerate digital transformation for their customer using Internet of Things (IoT) and machine learning in a hybrid cloud environment. Specifically, Clairvoyant and Eurotech will discuss:
• The approach taken to optimize manufacturing processes to cut costs, minimize downtime, and increase efficiency.
• How a data processing pipeline for IoT data was built using an open, end-to-end architecture from Cloudera, Eurotech, and Red Hat.
• How analytics and machine learning inferencing powered at the IoT edge will allow predictions to be made and decisions to be executed in real time.
• The flexible and hybrid cloud environment designed to provide the key foundational elements to quickly and securely roll out IoT use cases.
Smart Meter Data Analytic using Hadoop
This document discusses using Hadoop for smart meter data analytics. Smart meters track energy usage and send data to utility servers. Analyzing large volumes of smart meter data presents challenges due to data growth rates. Hadoop can help by reducing data loads and improving query performance due to its scalability. The document outlines how Hadoop can enable demand response analysis, time of use tariff analysis, and load profile analysis. It also provides diagrams of a Hadoop cluster and data flow for smart meter data analytics.
Empowering Real Time Patient Care Through Spark Streaming
Takeda’s Plasma Derived Therapies (PDT) business unit has recently embarked on a project to use Spark Streaming on Databricks to empower how they deliver value to their Plasma Donation centers. As patients come in and interface without clinics, we store and track all of the patient interactions in real time and deliver outputs and results based on said interactions. The current problem with our existing architecture is that it is very expensive to maintain and has an unsustainable number of failure points. Spark Streaming is essential for allowing this use case because it allows for a more robust ETL pipeline. With Spark Streaming, we are able to replace our existing ETL processes (that are based on Lamdbas, step functions, triggered jobs, etc) into a purely stream driven architecture.
Data is brought into our s3 raw layer as a large set of CSV files through AWS DMS and Informatica IICS as these services bring data from on-prem systems into our cloud layer. We have a stream currently running which takes these raw files up and merges them into Delta tables established in the bronze/stage layer. We are using AWS Glue as the metadata provider for all of these operations. From the stage layer, we have another set of streams using the stage Delta tables as their source, which transform and conduct stream to stream lookups before writing the enriched records into RDS (silver/prod layer). Once the data has been merged into RDS we have a DMS task which lifts the data back into S3 as CSV files. We have a small intermediary stream which merge these CSV files into corresponding delta tables, from which we have our gold/analytic streams. The on-prem systems are able to speak to the silver layer and allow for the near real-time latency that our patient care centers require.
Escaping Flatland: Interactive High-Dimensional Data Analysis in Drug Discove...
This document describes a project at Novartis to use Apache Spark for high-dimensional data analysis from drug screening. Large datasets from various screening technologies were analyzed using Spark pipelines for quality control, normalization, and classification. Visualizations were built using WebGL. The goals were to speed up multi-day batch jobs, create a unified analysis workflow, and build an application for scientists. Future work includes elastic infrastructure, supervised learning of cell phenotypes, and contributing methods to open source.
Turning an idea into a Data-Driven Production System: An Energy Load Forecas...
https://www.bigdataspain.org/2016/program/thu-turning-idea-into-data-driven-production-system-energy-load-forecasting-case-study.html
https://www.youtube.com/watch?v=Df59GmPcVag&list=PL6O3g23-p8Tr5eqnIIPdBD_8eE5JBDBik&t=26s&index=58
Spark Summit Keynote by Seshu Adunuthula
1) eBay's enterprise data platform uses Apache Spark and Hadoop to process large amounts of structured and unstructured data from various sources to power applications and analytics.
2) Key aspects of the platform include an agile data warehouse, data streams platform using Apache Kafka, and data services to simplify access to data and enable collaborative analytics.
3) eBay leverages this platform to power applications such as search, personalization, fraud prevention, and business intelligence through pipelines that ingest behavioral and transactional data.
How Spark Fits into Baidu's Scale-(James Peng, Baidu)
This document summarizes Baidu's use of Apache Spark and Tachyon for interactive data analytics. It describes Baidu's need for faster interactive queries to analyze petabytes of data within 30 seconds. Baidu implemented a query architecture using Spark, SQL and UDFs over Tachyon for hot data and HDFS storage. This provided over 50x acceleration compared to MapReduce, enabling 95% of queries to finish within 30 seconds. While Spark has improved performance, MapReduce still handles high throughput batch workloads at larger scale. Future opportunities include hardware acceleration and continued Spark optimizations for memory management and query planning.
Clinical Suspecting at Scale Using PySpark
This document discusses using PySpark for large-scale clinical machine learning tasks. It describes a use case of predicting patient diagnoses from medical data spanning 1.2M patients. Key challenges included loading 500GB of raw data across 5 categories, joining data with 25,000 features, and training multi-class models. Solutions involved using Parquet formats for faster loading, breaking data into "narrower" frames to reduce joins, defining schemas, converting to sparse representations, and custom feature engineering per category. The document outlines best practices for ML on Spark like these solutions to address issues around performance and scalability.
Software tools directly useable for spatial planners
The document discusses different tools for spatial planners, with a focus on portal solutions for managing and sharing data. It describes the basic components of a portal including: uploading services to allow standardized and non-standardized data formats; updating services to download, upload, and make online and mobile updates to data; data management tools; visualization tools to view internal and external services; and sharing services to allow internal and external access to data and discovery services. Standardized data formats can be directly uploaded while non-standardized formats require transformation models and repositories.
Big Data Streams Architectures. Why? What? How?
With a current zoo of technologies and different ways of their interaction it's a big challenge to architect a system (or adopt existed one) that will conform to low-latency BigData analysis requirements. Apache Kafka and Kappa Architecture in particular take more and more attention over classic Hadoop-centric technologies stack. New Consumer API put significant boost in this direction. Microservices-based streaming processing and new Kafka Streams tend to be a synergy in BigData world.
BigDL: A Distributed Deep Learning Library on Spark: Spark Summit East talk b...
BigDL is a distributed deep Learning framework built for Big Data platform using Apache Spark. It combines the benefits of “high performance computing” and “Big Data” architecture, providing native support for deep learning functionalities in Spark, orders of magnitude speedup than out-of-box open source DL frameworks (e.g., Caffe/Torch) wrt single node performance (by leveraging Intel MKL), and the scale-out of deep learning workloads based on the Spark architecture. We’ll also share how our users adopt BigDL for their deep learning applications (such as image recognition, object detection, NLP, etc.), which allows them to use their Big Data (e.g., Apache Hadoop and Spark) platform as the unified data analytics platform for data storage, data processing and mining, feature engineering, traditional (non-deep) machine learning, and deep learning workloads.
SAP HANA Database
My students' presentation of a paper "SAP HANA Database" by Farber, et al. (2011). It describes SAP HANA.
Spark Summit EU 2015: Matei Zaharia keynote
2015 was a year of continued growth for Spark, with numerous additions to the core project and very fast growth of use cases across the industry. In this talk, I’ll look back at how the Spark community is has grown and changed in 2015, based on a large Apache Spark user survey conducted by Databricks. We see some interesting trends in the diversity of runtime environments (which are increasingly not just Hadoop); the types of applications run on Spark; and the types of users, now that features like R support and DataFrames are available in Spark. I’ll also cover the ongoing work in the upcoming releases of Spark to support new use cases.
The Rise of Engineering-Driven Analytics by Loren Shure
1) Engineering-driven analytics is becoming more pervasive as data sources, computing power, and machine learning techniques expand.
2) MATLAB provides tools for engineering-driven analytics including support for engineering data types, machine learning algorithms, and model deployment in embedded systems.
3) Examples demonstrate how MATLAB has been used for applications in building energy optimization, automotive emergency braking, manufacturing quality control, and medical diagnostics.
Energy sector
4 Circles has extensive experience working with energy sector clients on projects involving legacy code conversion, database integration, and adoption of open source technologies. Some key projects included converting hard coded legacy code to be more generic, connecting Excel to Access databases, enhancing solar data hub code to use Spring and Hibernate, and deploying applications to the cloud. 4 Circles also performed research on time series databases and trained energy experts on NoSQL databases. Clients included Siemens USA and the National Renewable Energy Laboratory.
Artificial Intelligence and Analytic Ops to Continuously Improve Business Out...
Analytic Ops is an approach that focuses on continuously improving business outcomes through artificial intelligence by getting AI solutions into production quickly while ensuring regulatory compliance. It addresses typical challenges where only 15% of advanced analytics projects reach production due to underestimating complexity and lack of agility. Analytic Ops prioritizes production, focuses on business value, and allows for iterative changes through agile processes and best practices from software development. This enables the creation of sustainable data products and models in a fraction of the usual time.
Obfuscating LinkedIn Member Data
Krishnan Raman presented on LinkedIn's data obfuscation pipeline. The pipeline aims to analyze LinkedIn data to improve machine learning models, discover data quickly for analysis, and access data efficiently while complying with privacy regulations. It determines which files contain personally identifiable information (PII) to obfuscate, handles schema evolution, and preserves file names and types. WhereHows is used to track dataset lineage and locations. Obfuscated data is emitted with metrics on job progress captured in timeseries for monitoring the data pipeline. Challenges include unclean data, complex schemas, balancing failures vs dropped rows, and accounting for changing data and schemas. Auditing data, metadata, robust monitoring systems, and re-ob
Data Architecture for Modern Applications
Quick insights is always an ask. Its all about data ingestion, scalable data platform that is cost effective and how it plays well in your ecosystem. Learn the criteria, how to make decisions and define your architecture for the future
Real-Time Analytics and Actions Across Large Data Sets with Apache Spark
Around the world, businesses are turning to AI to transform the way they operate and serve their customers. But before they can implement these technologies, companies must address the roadblock of moving from batch analytics to making real-time decisions by rapidly accessing and analyzing the relevant information amidst a sea of data. Yaron will explain how to make Spark handle multivariate real-time, historical and event data simultaneously to provide immediate and intelligent responses. He will present several time sensitive use-cases including fraud detection, prevention of outages and customer recommendations to demonstrate how to perform predictive analytics and real-time actions with Spark.
Speaker: Yaron Ekshtein
Headaches and Breakthroughs in Building Continuous Applications
At SpotX, we have built and maintained a portfolio of Spark Streaming applications -- all of which process records in the millions per minute. From pure data ingestion, to ETL, to real-time reporting, to live customer-facing products and features, continuous applications are in our DNA. Come along with us as we outline our journey from square one to present in the world of Spark Streaming. We'll detail what we've learned about efficient processing and monitoring, reliability and stability, and long term support of a streaming app. Come learn from our mistakes, and leave with some handy settings and designs you can implement in your own streaming apps.
OraStream lossless audio music services
OraStream is a music streaming service that offers lossless audio up to 24-bit/192kHz, higher quality than competitors which only offer up to 16-bit/44.1kHz. It uses a layered bitrate system to stream at the highest quality a user's connection allows. Streaming revenue is growing significantly and subscriptions now make up over a quarter of digital music revenues. OraStream differentiates itself by being the only service that can stream higher quality lossless audio to mobile devices as well as desktops. It aims to expand the digital music market by appealing to audiophiles.
More Related Content
What's hot
Delivering digital transformation and business impact with io t, machine lear...
A world-leading manufacturer was in search of an IoT solution that could ingest, integrate, and manage data being generated from various types of connected machinery located on factory floors around the globe. The company needed to manage the devices generating the data, integrate the flow of data into existing back-end systems, run advanced analytics on that data, and then deliver services to generate real-time decision making at the edge.
In this session, learn how Clairvoyant, a leading systems integrator and Red Hat partner, was able to accelerate digital transformation for their customer using Internet of Things (IoT) and machine learning in a hybrid cloud environment. Specifically, Clairvoyant and Eurotech will discuss:
• The approach taken to optimize manufacturing processes to cut costs, minimize downtime, and increase efficiency.
• How a data processing pipeline for IoT data was built using an open, end-to-end architecture from Cloudera, Eurotech, and Red Hat.
• How analytics and machine learning inferencing powered at the IoT edge will allow predictions to be made and decisions to be executed in real time.
• The flexible and hybrid cloud environment designed to provide the key foundational elements to quickly and securely roll out IoT use cases.
Smart Meter Data Analytic using Hadoop
This document discusses using Hadoop for smart meter data analytics. Smart meters track energy usage and send data to utility servers. Analyzing large volumes of smart meter data presents challenges due to data growth rates. Hadoop can help by reducing data loads and improving query performance due to its scalability. The document outlines how Hadoop can enable demand response analysis, time of use tariff analysis, and load profile analysis. It also provides diagrams of a Hadoop cluster and data flow for smart meter data analytics.
Empowering Real Time Patient Care Through Spark Streaming
Takeda’s Plasma Derived Therapies (PDT) business unit has recently embarked on a project to use Spark Streaming on Databricks to empower how they deliver value to their Plasma Donation centers. As patients come in and interface without clinics, we store and track all of the patient interactions in real time and deliver outputs and results based on said interactions. The current problem with our existing architecture is that it is very expensive to maintain and has an unsustainable number of failure points. Spark Streaming is essential for allowing this use case because it allows for a more robust ETL pipeline. With Spark Streaming, we are able to replace our existing ETL processes (that are based on Lamdbas, step functions, triggered jobs, etc) into a purely stream driven architecture.
Data is brought into our s3 raw layer as a large set of CSV files through AWS DMS and Informatica IICS as these services bring data from on-prem systems into our cloud layer. We have a stream currently running which takes these raw files up and merges them into Delta tables established in the bronze/stage layer. We are using AWS Glue as the metadata provider for all of these operations. From the stage layer, we have another set of streams using the stage Delta tables as their source, which transform and conduct stream to stream lookups before writing the enriched records into RDS (silver/prod layer). Once the data has been merged into RDS we have a DMS task which lifts the data back into S3 as CSV files. We have a small intermediary stream which merge these CSV files into corresponding delta tables, from which we have our gold/analytic streams. The on-prem systems are able to speak to the silver layer and allow for the near real-time latency that our patient care centers require.
Escaping Flatland: Interactive High-Dimensional Data Analysis in Drug Discove...
This document describes a project at Novartis to use Apache Spark for high-dimensional data analysis from drug screening. Large datasets from various screening technologies were analyzed using Spark pipelines for quality control, normalization, and classification. Visualizations were built using WebGL. The goals were to speed up multi-day batch jobs, create a unified analysis workflow, and build an application for scientists. Future work includes elastic infrastructure, supervised learning of cell phenotypes, and contributing methods to open source.
Turning an idea into a Data-Driven Production System: An Energy Load Forecas...
https://www.bigdataspain.org/2016/program/thu-turning-idea-into-data-driven-production-system-energy-load-forecasting-case-study.html
https://www.youtube.com/watch?v=Df59GmPcVag&list=PL6O3g23-p8Tr5eqnIIPdBD_8eE5JBDBik&t=26s&index=58
Spark Summit Keynote by Seshu Adunuthula
1) eBay's enterprise data platform uses Apache Spark and Hadoop to process large amounts of structured and unstructured data from various sources to power applications and analytics.
2) Key aspects of the platform include an agile data warehouse, data streams platform using Apache Kafka, and data services to simplify access to data and enable collaborative analytics.
3) eBay leverages this platform to power applications such as search, personalization, fraud prevention, and business intelligence through pipelines that ingest behavioral and transactional data.
How Spark Fits into Baidu's Scale-(James Peng, Baidu)
This document summarizes Baidu's use of Apache Spark and Tachyon for interactive data analytics. It describes Baidu's need for faster interactive queries to analyze petabytes of data within 30 seconds. Baidu implemented a query architecture using Spark, SQL and UDFs over Tachyon for hot data and HDFS storage. This provided over 50x acceleration compared to MapReduce, enabling 95% of queries to finish within 30 seconds. While Spark has improved performance, MapReduce still handles high throughput batch workloads at larger scale. Future opportunities include hardware acceleration and continued Spark optimizations for memory management and query planning.
Clinical Suspecting at Scale Using PySpark
This document discusses using PySpark for large-scale clinical machine learning tasks. It describes a use case of predicting patient diagnoses from medical data spanning 1.2M patients. Key challenges included loading 500GB of raw data across 5 categories, joining data with 25,000 features, and training multi-class models. Solutions involved using Parquet formats for faster loading, breaking data into "narrower" frames to reduce joins, defining schemas, converting to sparse representations, and custom feature engineering per category. The document outlines best practices for ML on Spark like these solutions to address issues around performance and scalability.
Software tools directly useable for spatial planners
The document discusses different tools for spatial planners, with a focus on portal solutions for managing and sharing data. It describes the basic components of a portal including: uploading services to allow standardized and non-standardized data formats; updating services to download, upload, and make online and mobile updates to data; data management tools; visualization tools to view internal and external services; and sharing services to allow internal and external access to data and discovery services. Standardized data formats can be directly uploaded while non-standardized formats require transformation models and repositories.
Big Data Streams Architectures. Why? What? How?
With a current zoo of technologies and different ways of their interaction it's a big challenge to architect a system (or adopt existed one) that will conform to low-latency BigData analysis requirements. Apache Kafka and Kappa Architecture in particular take more and more attention over classic Hadoop-centric technologies stack. New Consumer API put significant boost in this direction. Microservices-based streaming processing and new Kafka Streams tend to be a synergy in BigData world.
BigDL: A Distributed Deep Learning Library on Spark: Spark Summit East talk b...
BigDL is a distributed deep Learning framework built for Big Data platform using Apache Spark. It combines the benefits of “high performance computing” and “Big Data” architecture, providing native support for deep learning functionalities in Spark, orders of magnitude speedup than out-of-box open source DL frameworks (e.g., Caffe/Torch) wrt single node performance (by leveraging Intel MKL), and the scale-out of deep learning workloads based on the Spark architecture. We’ll also share how our users adopt BigDL for their deep learning applications (such as image recognition, object detection, NLP, etc.), which allows them to use their Big Data (e.g., Apache Hadoop and Spark) platform as the unified data analytics platform for data storage, data processing and mining, feature engineering, traditional (non-deep) machine learning, and deep learning workloads.
SAP HANA Database
My students' presentation of a paper "SAP HANA Database" by Farber, et al. (2011). It describes SAP HANA.
Spark Summit EU 2015: Matei Zaharia keynote
2015 was a year of continued growth for Spark, with numerous additions to the core project and very fast growth of use cases across the industry. In this talk, I’ll look back at how the Spark community is has grown and changed in 2015, based on a large Apache Spark user survey conducted by Databricks. We see some interesting trends in the diversity of runtime environments (which are increasingly not just Hadoop); the types of applications run on Spark; and the types of users, now that features like R support and DataFrames are available in Spark. I’ll also cover the ongoing work in the upcoming releases of Spark to support new use cases.
The Rise of Engineering-Driven Analytics by Loren Shure
1) Engineering-driven analytics is becoming more pervasive as data sources, computing power, and machine learning techniques expand.
2) MATLAB provides tools for engineering-driven analytics including support for engineering data types, machine learning algorithms, and model deployment in embedded systems.
3) Examples demonstrate how MATLAB has been used for applications in building energy optimization, automotive emergency braking, manufacturing quality control, and medical diagnostics.
Energy sector
4 Circles has extensive experience working with energy sector clients on projects involving legacy code conversion, database integration, and adoption of open source technologies. Some key projects included converting hard coded legacy code to be more generic, connecting Excel to Access databases, enhancing solar data hub code to use Spring and Hibernate, and deploying applications to the cloud. 4 Circles also performed research on time series databases and trained energy experts on NoSQL databases. Clients included Siemens USA and the National Renewable Energy Laboratory.
Artificial Intelligence and Analytic Ops to Continuously Improve Business Out...
Analytic Ops is an approach that focuses on continuously improving business outcomes through artificial intelligence by getting AI solutions into production quickly while ensuring regulatory compliance. It addresses typical challenges where only 15% of advanced analytics projects reach production due to underestimating complexity and lack of agility. Analytic Ops prioritizes production, focuses on business value, and allows for iterative changes through agile processes and best practices from software development. This enables the creation of sustainable data products and models in a fraction of the usual time.
Obfuscating LinkedIn Member Data
Krishnan Raman presented on LinkedIn's data obfuscation pipeline. The pipeline aims to analyze LinkedIn data to improve machine learning models, discover data quickly for analysis, and access data efficiently while complying with privacy regulations. It determines which files contain personally identifiable information (PII) to obfuscate, handles schema evolution, and preserves file names and types. WhereHows is used to track dataset lineage and locations. Obfuscated data is emitted with metrics on job progress captured in timeseries for monitoring the data pipeline. Challenges include unclean data, complex schemas, balancing failures vs dropped rows, and accounting for changing data and schemas. Auditing data, metadata, robust monitoring systems, and re-ob
Data Architecture for Modern Applications
Quick insights is always an ask. Its all about data ingestion, scalable data platform that is cost effective and how it plays well in your ecosystem. Learn the criteria, how to make decisions and define your architecture for the future
Real-Time Analytics and Actions Across Large Data Sets with Apache Spark
Around the world, businesses are turning to AI to transform the way they operate and serve their customers. But before they can implement these technologies, companies must address the roadblock of moving from batch analytics to making real-time decisions by rapidly accessing and analyzing the relevant information amidst a sea of data. Yaron will explain how to make Spark handle multivariate real-time, historical and event data simultaneously to provide immediate and intelligent responses. He will present several time sensitive use-cases including fraud detection, prevention of outages and customer recommendations to demonstrate how to perform predictive analytics and real-time actions with Spark.
Speaker: Yaron Ekshtein
Headaches and Breakthroughs in Building Continuous Applications
At SpotX, we have built and maintained a portfolio of Spark Streaming applications -- all of which process records in the millions per minute. From pure data ingestion, to ETL, to real-time reporting, to live customer-facing products and features, continuous applications are in our DNA. Come along with us as we outline our journey from square one to present in the world of Spark Streaming. We'll detail what we've learned about efficient processing and monitoring, reliability and stability, and long term support of a streaming app. Come learn from our mistakes, and leave with some handy settings and designs you can implement in your own streaming apps.
What's hot (20)
Delivering digital transformation and business impact with io t, machine lear...
Delivering digital transformation and business impact with io t, machine lear...
Empowering Real Time Patient Care Through Spark Streaming
Empowering Real Time Patient Care Through Spark Streaming
Escaping Flatland: Interactive High-Dimensional Data Analysis in Drug Discove...
Escaping Flatland: Interactive High-Dimensional Data Analysis in Drug Discove...
Turning an idea into a Data-Driven Production System: An Energy Load Forecas...
Turning an idea into a Data-Driven Production System: An Energy Load Forecas...
How Spark Fits into Baidu's Scale-(James Peng, Baidu)
How Spark Fits into Baidu's Scale-(James Peng, Baidu)
Software tools directly useable for spatial planners
Software tools directly useable for spatial planners
BigDL: A Distributed Deep Learning Library on Spark: Spark Summit East talk b...
BigDL: A Distributed Deep Learning Library on Spark: Spark Summit East talk b...
The Rise of Engineering-Driven Analytics by Loren Shure
The Rise of Engineering-Driven Analytics by Loren Shure
Artificial Intelligence and Analytic Ops to Continuously Improve Business Out...
Artificial Intelligence and Analytic Ops to Continuously Improve Business Out...
Real-Time Analytics and Actions Across Large Data Sets with Apache Spark
Real-Time Analytics and Actions Across Large Data Sets with Apache Spark
Headaches and Breakthroughs in Building Continuous Applications
Headaches and Breakthroughs in Building Continuous Applications
Viewers also liked
OraStream lossless audio music services
OraStream is a music streaming service that offers lossless audio up to 24-bit/192kHz, higher quality than competitors which only offer up to 16-bit/44.1kHz. It uses a layered bitrate system to stream at the highest quality a user's connection allows. Streaming revenue is growing significantly and subscriptions now make up over a quarter of digital music revenues. OraStream differentiates itself by being the only service that can stream higher quality lossless audio to mobile devices as well as desktops. It aims to expand the digital music market by appealing to audiophiles.
Webquest
This document defines and describes what a webquest is. A webquest is an inquiry-oriented activity where students learn by obtaining information from online resources, potentially supplemented by videoconferencing. The document outlines the typical structure of a webquest, including an introduction, task, process, evaluation, conclusion, and list of resources. It also defines short-term and long-term webquests and notes that webquests are designed to be student-centered and focus on questioning, critical thinking, and problem-solving. Some pros and cons of using webquests in the classroom are also provided.
OraStream Demo
OraStream is a high-resolution audio music store that allows consumers to stream or download over 80,000 albums in formats up to 192kHz/24bit resolution from major classical labels on home and mobile networks. It provides cloud-based music lockers so users can access their playlists, favorite albums, and purchases from any device virtually anywhere while enjoying the highest possible sound quality.
WOMEN\'S KNITWEAR SAMPLES
This document appears to be a product catalog listing items for sale from a company called "Breath of the Cat". It lists various clothing items organized by collection/type including Sheer Light, Shine Surface, Hand-Drawn, Tubular Knits, Cardigans, Dress Me Up, Novelty, and Leather. Each collection lists 1-2 specific items for sale characterized by materials used and details.
ENGLISH RESUMEE (UP DATE)
This resume is for Vicente Calleja Fernández, who has a Master's Degree in Visual Arts from the Escuela Nacional de Artes Plásticas at UNAM. He has held individual exhibitions including "El sueño de la Razón" and has received scholarships for his artwork. Vicente has published illustrations and taught drawing and painting subjects at several universities. His artistic mediums include drawing, painting, sculpture, and metal casting.
A B B Y M O R I
Just a sample of my work, Includes two mini Item driven collections, inspiration as well as editorial.
My Town
This document lists important landmarks and places in the town of Barreiro, Portugal such as the Statue of Alfredo da Silva, the Mill of Jim, the Church of Barreiro, monuments in the town, the Fórum of Barreiro, the Hospital of Barreiro, and the Pavilhão Municipal Luís De Carvalho.
MEN\'S KNITWEAR
This document appears to be a product catalog from a company called Abbey Moriarty located in New York City. It lists several cotton, cashmere, and nylon items including a 12gg cotton cashmere cardigan and vest, 5gg bio cotton jaquard circle scarf and sweatshirt, 1.5gg oboe items made of 78% cotton and 22% nylon, and a 14gg ironcot item made of 80% cotton and 20% iron.
Viewers also liked (9)
Similar to Qo comparision
The Hidden Value of Hadoop Migration
Many organizations focus on the licensing cost of Hadoop when considering migrating to a cloud platform. But other costs should be considered, as well as the biggest impact, which is the benefit of having a modern analytics platform that can handle all of your use cases. This session will cover lessons learned in assisting hundreds of companies to migrate from Hadoop to Databricks.
Analyzing petabytes of smartmeter data using Cloud Bigtable, Cloud Dataflow, ...
The Energy Industry is in transition due to the exponential growth of data being generated by the ever increasing number of connected devices which comprise the Smart Grid. Learn how Energyworx uses GCP to collect and ingest this IoT data with ease and is helping her customers uncover hidden value from this data, allowing them to create new business models and concepts.
Unlocking Operational Intelligence from the Data Lake
The document discusses unlocking operational intelligence from data lakes using MongoDB. It begins by describing how digital transformation is driving changes in data volume, velocity, and variety. It then discusses how MongoDB can help operationalize data lakes by providing real-time access and analytics on data stored in data lakes, while also integrating batch processing capabilities. The document provides an example reference architecture of how MongoDB can be used with a data lake (Hadoop) and stream processing framework (Kafka) to power operational applications and machine learning models with both real-time and batch data and analytics.
Apache CarbonData+Spark to realize data convergence and Unified high performa...
Challenges in Data Analytics:
Different application scenarios need different storage solutions: HBASE is ideal for point query scenarios but unsuitable for multi-dimensional queries. MPP is suitable for data warehouse scenarios but engine and data are coupled together which hampers scalability. OLAP stores used in BI applications perform best for Aggregate queries but full scan queries perform at a sub-optimal performance. Moreover, they are not suitable for real-time analysis. These distinct systems lead to low resource sharing and need different pipelines for data and application management.
Apache Kylin’s Performance Boost from Apache HBase
Hongbin Ma and Luke Han (Kyligence)
Apache Kylin is an open source distributed analytics engine that provides a SQL interface and multi-dimensional analysis on Hadoop supporting extremely large datasets. In the forthcoming Kylin release, we optimized query performance by exploring the potentials of parallel storage on top of HBase. This talk explains how that work was done.
Logical Data Fabric and Data Mesh – Driving Business Outcomes
Watch full webinar here: https://buff.ly/3qgGjtA
Presented at TDWI VIRTUAL SUMMIT - Modernizing Data Management
While the technological advances of the past decade have addressed the scale of data processing and data storage, they have failed to address scale in other dimensions: proliferation of sources of data, diversity of data types and user persona, and speed of response to change. The essence of the data mesh and data fabric approaches is that it puts the customer first and focuses on outcomes instead of outputs.
In this session, Saptarshi Sengupta, Senior Director of Product Marketing at Denodo, will address key considerations and provide his insights on why some companies are succeeding with these approaches while others are not.
Watch On-Demand and Learn:
- Why a logical approach is necessary and how it aligns with data fabric and data mesh
- How some of the large enterprises are using logical data fabric and data mesh for their data and analytics needs
- Tips to create a good data management modernization roadmap for your organization
Benefits of Hadoop as Platform as a Service
The document summarizes research done at the Barcelona Supercomputing Center on evaluating Hadoop platforms as a service (PaaS) compared to infrastructure as a service (IaaS). Key findings include:
- Provider (Azure HDInsight, Rackspace CBD, etc.) did not significantly impact performance of wordcount and terasort benchmarks.
- Data size and number of datanodes were more important factors, with diminishing returns on performance from adding more nodes.
- PaaS can save on maintenance costs compared to IaaS but may be more expensive depending on workload and VM size needed. Tuning may still be required with PaaS.
Creating a Modern Data Architecture for Digital Transformation
By managing Data in Motion, Data at Rest, and Data in Use differently, modern Information Management Solutions are enabling a whole range of architecture and design patterns that allow enterprises to fully harness the value in data flowing through their systems. In this session we explored some of the patterns (e.g. operational data lakes, CQRS, microservices and containerisation) that enable CIOs, CDOs and senior architects to tame the data challenge, and start to use data as a cross-enterprise asset.
Accelerating Big Data Analytics
The document discusses using Attunity Replicate to accelerate loading and integrating big data into Microsoft's Analytics Platform System (APS). Attunity Replicate provides real-time change data capture and high-performance data loading from various sources into APS. It offers a simplified and automated process for getting data into APS to enable analytics and business intelligence. Case studies are presented showing how major companies have used APS and Attunity Replicate to improve analytics and gain business insights from their data.
Transform your DBMS to drive engagement innovation with Big Data
This document discusses how organizations can save money on database management systems (DBMS) by moving from expensive commercial DBMS to more affordable open-source options like PostgreSQL. It notes that PostgreSQL has matured and can now handle mission critical workloads. The document recommends partnering with EnterpriseDB to take advantage of their commercial support and features for PostgreSQL. It highlights how customers have seen cost savings of 35-80% by switching to PostgreSQL and been able to reallocate funds to new business initiatives.
Powering Real-Time Big Data Analytics with a Next-Gen GPU Database
Freed from the constraints of storage, network and memory, many big data analytics systems now are routinely revealing themselves to be compute bound. To compensate, big data analytic systems often result in wide horizontal sprawl (300-node Spark or NoSQL clusters are not unusual!)— to bring in enough compute for the task at hand. High system complexity and crushing operational costs often result. As the world shifts from physical to virtual assets and methods of engagement, there is an increasing need for systems of intelligence to live alongside the more traditional systems of record and systems of analysis. New approaches to data processing are required to support the real-time processing of data required to drive these systems of intelligence.
Join 451 Research and Kinetica to learn:
•An overview of the business and technical trends driving widespread interest in real-time analytics
•Why systems of analysis need to be transformed and augmented with systems of intelligence bringing new approaches to data processing
•How a new class of solution—a GPU-accelerated, scale out, in-memory database–can bring you orders of magnitude more compute power, significantly smaller hardware footprint, and unrivaled analytic capabilities.
•Hear how other companies in a variety of industries, such as financial services, entertainment, pharmaceutical, and oil and gas, benefit from augmenting their legacy systems with a modern analytics database.
Delivering fast, powerful and scalable analytics
This session will provide insight on making the most of your data assets with analytics, and what you need for your next analytics project. We’ll showcase how the MariaDB AX solution delivers fast and scalable analytics using real-world use cases.
Retail & CPG
The document discusses new rules and strategies for retailers in an evolving customer relationship landscape. It notes there are now 56 touchpoints between a customer's moment of inspiration and transaction. It then discusses components of digital transformation like customer experience management, cross-channel order orchestration, and building a single customer view. The document outlines how retailers can create customer connections and profiles by leveraging enterprise data. It also discusses the need for customer engagement in stores through technologies like self-scanning and mobile payments. Finally, it discusses how front-end store technologies can empower associates and optimize processes.
Yellowbrick MicroStrategy webcast
The document summarizes the benefits of using Yellowbrick Data Warehouse with MicroStrategy for business analytics. Yellowbrick provides a purpose-built all-flash SQL data warehouse that can scale from tens of terabytes to multiple petabytes. It offers capabilities like real-time data ingestion, interactive queries, and support for thousands of concurrent users. When used with MicroStrategy, it enables faster dashboards and queries, supports more MicroStrategy users with the same data footprint, and allows querying live data without pre-aggregations. Customers saw performance improvements like building 182 billion row tables in under two minutes.
ShareChat’s Path to High-Performance NoSQL with ScyllaDB
🎥 Sign up for upcoming webinars or browse through our library of on-demand recordings here: https://www.scylladb.com/resources/webinars/
To power India’s leading social media platform, ShareChat needs to deliver impressive performance at rapidly-increasing scale – but without rapidly-increasing cost. With 180M monthly active users expecting real-time engagement with 2.5B posts per month, having the right database implementation is essential.
Hear how Geetish Nayak, Staff Engineer/Architect - Platforms at ShareChat modernized the database powering their services to stay ahead of these challenges. Geetish will share the strategies they applied to improve performance 3-5x while reducing costs 50-80%. You will learn:
- About their technical challenges related to supporting new locations, debugging, and performance at scale
- What NoSQL capabilities were most critical for preparing their data architecture for the company’s next level of growth
- How they measured the impact of their NoSQL migration, and what they have achieved so far
- Best Practices that ShareChat followed when adopting ScyllaDB
DX2000 from NEC lets you put big data to work
The data that your business collects is constantly growing, making it increasingly difficult for traditional systems to keep up with resource demands. Understanding your big data can help you serve your customers better, improve product quality, and grow your revenue, but you need a platform that can handle the strain.
In hands-on tests in our datacenter, the Scalable Modular Server DX2000 from NEC processed big data quickly and scaled nearly linearly as we added server nodes. In our k-means data cluster analysis test, a DX2000 solution running Apache Spark and Red Hat Enterprise Linux OpenStack Platform processed 100GB in approximately 2 minutes. We also saw that as we doubled the number of server nodes, the DX2000 solution cut analysis time in half when processing the same amount of data, producing excellent scalability.
The Scalable Modular Server DX2000 by NEC is a good choice when you’re ready to put big data to work for you.
ADV Slides: When and How Data Lakes Fit into a Modern Data Architecture
Whether to take data ingestion cycles off the ETL tool and the data warehouse or to facilitate competitive Data Science and building algorithms in the organization, the data lake – a place for unmodeled and vast data – will be provisioned widely in 2020.
Though it doesn’t have to be complicated, the data lake has a few key design points that are critical, and it does need to follow some principles for success. Avoid building the data swamp, but not the data lake! The tool ecosystem is building up around the data lake and soon many will have a robust lake and data warehouse. We will discuss policy to keep them straight, send data to its best platform, and keep users’ confidence up in their data platforms.
Data lakes will be built in cloud object storage. We’ll discuss the options there as well.
Get this data point for your data lake journey.
Bridging the Last Mile: Getting Data to the People Who Need It (APAC)
Watch full webinar here: https://bit.ly/34iCruM
Many organizations are embarking on strategically important journeys to embrace data and analytics. The goal can be to improve internal efficiencies, improve the customer experience, drive new business models and revenue streams, or – in the public sector – provide better services. All of these goals require empowering employees to act on data and analytics and to make data-driven decisions. However, getting data – the right data at the right time – to these employees is a huge challenge and traditional technologies and data architectures are simply not up to this task. This webinar will look at how organizations are using Data Virtualization to quickly and efficiently get data to the people that need it.
Attend this session to learn:
- The challenges organizations face when trying to get data to the business users in a timely manner
- How Data Virtualization can accelerate time-to-value for an organization’s data assets
- Examples of leading companies that used data virtualization to get the right data to the users at the right time
Can data virtualization uphold performance with complex queries?
Watch full webinar here: https://bit.ly/2JzypTx
There are myths about data virtualization that are based on misconceptions and even falsehoods. These myths can confuse and worry people who - quite rightly - look at data virtualization as a critical technology for a modern, agile data architecture.
We've decided that we need to set the record straight, so we put together this webinar series. It's time to bust a few myths!
In the first webinar of the series, we’ll be busting the 'performance' myth. “What about performance?” is usually the first question that we get when talking to people about data virtualization. After all, the data virtualization layer sits between you and your data, so how does this affect the performance of your queries? Sometimes the myth is perpetuated by people with alternative solutions…the ‘Put all your data in our Cloud and everything will be fine. Data virtualization? Nah, you don’t need that! It can't handle big queries anyway,’ type of thing.
Join us for this webinar to look at the basis of the 'performance' myth and examine whether there is any underlying truth to it.
Customer value analysis of big data products
Business value analysis through Customer Value Model for software technology choices with a case study from Mobile Advertising industry for Big Data use case.
Similar to Qo comparision (20)
Analyzing petabytes of smartmeter data using Cloud Bigtable, Cloud Dataflow, ...
Analyzing petabytes of smartmeter data using Cloud Bigtable, Cloud Dataflow, ...
Unlocking Operational Intelligence from the Data Lake
Unlocking Operational Intelligence from the Data Lake
Apache CarbonData+Spark to realize data convergence and Unified high performa...
Apache CarbonData+Spark to realize data convergence and Unified high performa...
Apache Kylin’s Performance Boost from Apache HBase
Apache Kylin’s Performance Boost from Apache HBase
Logical Data Fabric and Data Mesh – Driving Business Outcomes
Logical Data Fabric and Data Mesh – Driving Business Outcomes
Creating a Modern Data Architecture for Digital Transformation
Creating a Modern Data Architecture for Digital Transformation
Transform your DBMS to drive engagement innovation with Big Data
Transform your DBMS to drive engagement innovation with Big Data
Powering Real-Time Big Data Analytics with a Next-Gen GPU Database
Powering Real-Time Big Data Analytics with a Next-Gen GPU Database
ShareChat’s Path to High-Performance NoSQL with ScyllaDB
ShareChat’s Path to High-Performance NoSQL with ScyllaDB
ADV Slides: When and How Data Lakes Fit into a Modern Data Architecture
ADV Slides: When and How Data Lakes Fit into a Modern Data Architecture
Bridging the Last Mile: Getting Data to the People Who Need It (APAC)
Bridging the Last Mile: Getting Data to the People Who Need It (APAC)
Can data virtualization uphold performance with complex queries?
Can data virtualization uphold performance with complex queries?
Recently uploaded
Skybuffer SAM4U tool for SAP license adoption
Manage and optimize your license adoption and consumption with SAM4U, an SAP free customer software asset management tool.
SAM4U, an SAP complimentary software asset management tool for customers, delivers a detailed and well-structured overview of license inventory and usage with a user-friendly interface. We offer a hosted, cost-effective, and performance-optimized SAM4U setup in the Skybuffer Cloud environment. You retain ownership of the system and data, while we manage the ABAP 7.58 infrastructure, ensuring fixed Total Cost of Ownership (TCO) and exceptional services through the SAP Fiori interface.
Let's Integrate MuleSoft RPA, COMPOSER, APM with AWS IDP along with Slack
Discover the seamless integration of RPA (Robotic Process Automation), COMPOSER, and APM with AWS IDP enhanced with Slack notifications. Explore how these technologies converge to streamline workflows, optimize performance, and ensure secure access, all while leveraging the power of AWS IDP and real-time communication via Slack notifications.
AWS Cloud Cost Optimization Presentation.pptx
This presentation provides valuable insights into effective cost-saving techniques on AWS. Learn how to optimize your AWS resources by rightsizing, increasing elasticity, picking the right storage class, and choosing the best pricing model. Additionally, discover essential governance mechanisms to ensure continuous cost efficiency. Whether you are new to AWS or an experienced user, this presentation provides clear and practical tips to help you reduce your cloud costs and get the most out of your budget.
System Design Case Study: Building a Scalable E-Commerce Platform - Hiike
This case study explores designing a scalable e-commerce platform, covering key requirements, system components, and best practices.
Generating privacy-protected synthetic data using Secludy and Milvus
During this demo, the founders of Secludy will demonstrate how their system utilizes Milvus to store and manipulate embeddings for generating privacy-protected synthetic data. Their approach not only maintains the confidentiality of the original data but also enhances the utility and scalability of LLMs under privacy constraints. Attendees, including machine learning engineers, data scientists, and data managers, will witness first-hand how Secludy's integration with Milvus empowers organizations to harness the power of LLMs securely and efficiently.
Driving Business Innovation: Latest Generative AI Advancements & Success Story
Are you ready to revolutionize how you handle data? Join us for a webinar where we’ll bring you up to speed with the latest advancements in Generative AI technology and discover how leveraging FME with tools from giants like Google Gemini, Amazon, and Microsoft OpenAI can supercharge your workflow efficiency.
During the hour, we’ll take you through:
Guest Speaker Segment with Hannah Barrington: Dive into the world of dynamic real estate marketing with Hannah, the Marketing Manager at Workspace Group. Hear firsthand how their team generates engaging descriptions for thousands of office units by integrating diverse data sources—from PDF floorplans to web pages—using FME transformers, like OpenAIVisionConnector and AnthropicVisionConnector. This use case will show you how GenAI can streamline content creation for marketing across the board.
Ollama Use Case: Learn how Scenario Specialist Dmitri Bagh has utilized Ollama within FME to input data, create custom models, and enhance security protocols. This segment will include demos to illustrate the full capabilities of FME in AI-driven processes.
Custom AI Models: Discover how to leverage FME to build personalized AI models using your data. Whether it’s populating a model with local data for added security or integrating public AI tools, find out how FME facilitates a versatile and secure approach to AI.
We’ll wrap up with a live Q&A session where you can engage with our experts on your specific use cases, and learn more about optimizing your data workflows with AI.
This webinar is ideal for professionals seeking to harness the power of AI within their data management systems while ensuring high levels of customization and security. Whether you're a novice or an expert, gain actionable insights and strategies to elevate your data processes. Join us to see how FME and AI can revolutionize how you work with data!
Unlock the Future of Search with MongoDB Atlas_ Vector Search Unleashed.pdf
Discover how MongoDB Atlas and vector search technology can revolutionize your application's search capabilities. This comprehensive presentation covers:
* What is Vector Search?
* Importance and benefits of vector search
* Practical use cases across various industries
* Step-by-step implementation guide
* Live demos with code snippets
* Enhancing LLM capabilities with vector search
* Best practices and optimization strategies
Perfect for developers, AI enthusiasts, and tech leaders. Learn how to leverage MongoDB Atlas to deliver highly relevant, context-aware search results, transforming your data retrieval process. Stay ahead in tech innovation and maximize the potential of your applications.
#MongoDB #VectorSearch #AI #SemanticSearch #TechInnovation #DataScience #LLM #MachineLearning #SearchTechnology
Your One-Stop Shop for Python Success: Top 10 US Python Development Providers
Simplify your search for a reliable Python development partner! This list presents the top 10 trusted US providers offering comprehensive Python development services, ensuring your project's success from conception to completion.
Skybuffer AI: Advanced Conversational and Generative AI Solution on SAP Busin...
Skybuffer AI, built on the robust SAP Business Technology Platform (SAP BTP), is the latest and most advanced version of our AI development, reaffirming our commitment to delivering top-tier AI solutions. Skybuffer AI harnesses all the innovative capabilities of the SAP BTP in the AI domain, from Conversational AI to cutting-edge Generative AI and Retrieval-Augmented Generation (RAG). It also helps SAP customers safeguard their investments into SAP Conversational AI and ensure a seamless, one-click transition to SAP Business AI.
With Skybuffer AI, various AI models can be integrated into a single communication channel such as Microsoft Teams. This integration empowers business users with insights drawn from SAP backend systems, enterprise documents, and the expansive knowledge of Generative AI. And the best part of it is that it is all managed through our intuitive no-code Action Server interface, requiring no extensive coding knowledge and making the advanced AI accessible to more users.
Letter and Document Automation for Bonterra Impact Management (fka Social Sol...
Sidekick Solutions uses Bonterra Impact Management (fka Social Solutions Apricot) and automation solutions to integrate data for business workflows.
We believe integration and automation are essential to user experience and the promise of efficient work through technology. Automation is the critical ingredient to realizing that full vision. We develop integration products and services for Bonterra Case Management software to support the deployment of automations for a variety of use cases.
This video focuses on automated letter generation for Bonterra Impact Management using Google Workspace or Microsoft 365.
Interested in deploying letter generation automations for Bonterra Impact Management? Contact us at sales@sidekicksolutionsllc.com to discuss next steps.
Energy Efficient Video Encoding for Cloud and Edge Computing Instances
Energy Efficient Video Encoding for Cloud and Edge Computing Instances
How to Interpret Trends in the Kalyan Rajdhani Mix Chart.pdf
A Mix Chart displays historical data of numbers in a graphical or tabular form. The Kalyan Rajdhani Mix Chart specifically shows the results of a sequence of numbers over different periods.
June Patch Tuesday
Ivanti’s Patch Tuesday breakdown goes beyond patching your applications and brings you the intelligence and guidance needed to prioritize where to focus your attention first. Catch early analysis on our Ivanti blog, then join industry expert Chris Goettl for the Patch Tuesday Webinar Event. There we’ll do a deep dive into each of the bulletins and give guidance on the risks associated with the newly-identified vulnerabilities.
leewayhertz.com-AI in predictive maintenance Use cases technologies benefits ...
Predictive maintenance is a proactive approach that anticipates equipment failures before they happen. At the forefront of this innovative strategy is Artificial Intelligence (AI), which brings unprecedented precision and efficiency. AI in predictive maintenance is transforming industries by reducing downtime, minimizing costs, and enhancing productivity.
Fueling AI with Great Data with Airbyte Webinar
This talk will focus on how to collect data from a variety of sources, leveraging this data for RAG and other GenAI use cases, and finally charting your course to productionalization.
Main news related to the CCS TSI 2023 (2023/1695)
An English 🇬🇧 translation of a presentation to the speech I gave about the main changes brought by CCS TSI 2023 at the biggest Czech conference on Communications and signalling systems on Railways, which was held in Clarion Hotel Olomouc from 7th to 9th November 2023 (konferenceszt.cz). Attended by around 500 participants and 200 on-line followers.
The original Czech 🇨🇿 version of the presentation can be found here: https://www.slideshare.net/slideshow/hlavni-novinky-souvisejici-s-ccs-tsi-2023-2023-1695/269688092 .
The videorecording (in Czech) from the presentation is available here: https://youtu.be/WzjJWm4IyPk?si=SImb06tuXGb30BEH .
Ocean lotus Threat actors project by John Sitima 2024 (1).pptx
Ocean Lotus cyber threat actors represent a sophisticated, persistent, and politically motivated group that poses a significant risk to organizations and individuals in the Southeast Asian region. Their continuous evolution and adaptability underscore the need for robust cybersecurity measures and international cooperation to identify and mitigate the threats posed by such advanced persistent threat groups.
Recently uploaded (20)
Let's Integrate MuleSoft RPA, COMPOSER, APM with AWS IDP along with Slack
Let's Integrate MuleSoft RPA, COMPOSER, APM with AWS IDP along with Slack
System Design Case Study: Building a Scalable E-Commerce Platform - Hiike
System Design Case Study: Building a Scalable E-Commerce Platform - Hiike
Generating privacy-protected synthetic data using Secludy and Milvus
Generating privacy-protected synthetic data using Secludy and Milvus
Driving Business Innovation: Latest Generative AI Advancements & Success Story
Driving Business Innovation: Latest Generative AI Advancements & Success Story
Unlock the Future of Search with MongoDB Atlas_ Vector Search Unleashed.pdf
Unlock the Future of Search with MongoDB Atlas_ Vector Search Unleashed.pdf
Your One-Stop Shop for Python Success: Top 10 US Python Development Providers
Your One-Stop Shop for Python Success: Top 10 US Python Development Providers
Skybuffer AI: Advanced Conversational and Generative AI Solution on SAP Busin...
Skybuffer AI: Advanced Conversational and Generative AI Solution on SAP Busin...
Letter and Document Automation for Bonterra Impact Management (fka Social Sol...
Letter and Document Automation for Bonterra Impact Management (fka Social Sol...
Energy Efficient Video Encoding for Cloud and Edge Computing Instances
Energy Efficient Video Encoding for Cloud and Edge Computing Instances
Overcoming the PLG Trap: Lessons from Canva's Head of Sales & Head of EMEA Da...
Overcoming the PLG Trap: Lessons from Canva's Head of Sales & Head of EMEA Da...
How to Interpret Trends in the Kalyan Rajdhani Mix Chart.pdf
How to Interpret Trends in the Kalyan Rajdhani Mix Chart.pdf
leewayhertz.com-AI in predictive maintenance Use cases technologies benefits ...
leewayhertz.com-AI in predictive maintenance Use cases technologies benefits ...
Ocean lotus Threat actors project by John Sitima 2024 (1).pptx
Ocean lotus Threat actors project by John Sitima 2024 (1).pptx
Qo comparision
- 1. eXtremely large database (XLDB) analytics
- 4. Relational database, row oriented: MySQL, Oracle
- 5. Relational database, column oriented: Vertica
- 6. Hardware approach (brute force computing)
- 8. Distributed relational database: Aster Data
- 11. iQO can readily scale to petabyte volume applications whereas hadoop and Aster would reach unwieldy hardware proportions making them impractical to implement with extremely slow response times
- 12. MySQL and Oracle are not scalable for daily terabyte volumes
- 14. iQO Solutions Scalable & portable Uniquely suited for high volume and high cardinality Most queries returned in seconds Allows for Distinct Counts and Exclusive Counts not available in other solutions Lowest carbon footprint per query
- 16. Custom coding from skilled programmers required for each query
- 17. Inefficient data storage and management
- 18. Distinct counts difficult to achieve
- 19. Requires a team to manage effectively
- 20. No connectivity with BI and data mining tools
- 21. Business Objects
- 24. Distinct Counts difficult to achieve and Exclusive Counts not possible.
- 26. Space and server requirements to difficult to estimate
- 27. Query times would be impossibly long