My insight data engineering project. I created a big data pipeline for performing user sentiment analysis on US Stock market.
www.hashtagcashtag.com
https://github.com/shafiab/MarketSentiment
The document summarizes the status of RPKI deployment in Bangladesh. It finds that over 3% of Bangladeshi prefixes are invalid according to RPKI data, and some internet service providers are announcing prefixes longer than /24. The document recommends using ExaBGP and GIXLG tools to help identify invalidly originated prefixes and engage with internet service providers to resolve issues.
This document describes a big data pipeline for analyzing user sentiment from Twitter and stock market data. The pipeline ingests Twitter and stock market data from various sources into a Kafka cluster. It then uses Spark batch and streaming jobs to perform sentiment analysis, track trending stocks, and generate real-time and batch views that are stored in Cassandra. The views include time series data of tweet volumes and sentiments for individual stocks. The architecture aims to provide both real-time and batch processing capabilities for user sentiment analysis on stock market data.
Role of MySQL in Data Analytics, WarehousingVenu Anuganti
The document discusses the role of MySQL in data analytics and data warehousing. It describes how MySQL is widely used by many companies for online transaction processing (OLTP) and is the de facto standard for developers. While MySQL can be used for small data warehousing and analytics tasks, the document recommends using column-oriented databases with compression for large datasets due to MySQL's limitations in scalability for data warehousing. It provides tips on optimizing MySQL for analytics workloads and discusses using OLAP cubes and real-time analytics for near real-time insights.
The document discusses choosing between SQL and NoSQL databases. It covers the evolution of data architectures from traditional client-server models to newer distributed NoSQL solutions. It provides an overview of different data store types like SQL, NoSQL, key-value, document, column family, and graph databases. The document advises picking the right data model based on business needs, use cases, data storage requirements, and growth patterns then evaluating solutions based on pros and cons. It concludes that for large, growing data, both SQL and NoSQL solutions may be needed.
Designing Scalable Data Warehouse Using MySQLVenu Anuganti
The document discusses designing scalable data warehouses using MySQL. It covers topics like the role of MySQL in data warehousing and analytics, typical data warehouse architectures, scaling out MySQL, and limitations of MySQL for large datasets or as a scalable warehouse solution. Real-time analytics are also discussed, noting the challenges of performance and scalability for near real-time analytics.
This document provides an overview and best practices for operating HBase clusters. It discusses HBase and Hadoop architecture, how to set up an HBase cluster including Zookeeper and region servers, high availability considerations, scaling the cluster, backup and restore processes, and operational best practices around hardware, disks, OS, automation, load balancing, upgrades, monitoring and alerting. It also includes a case study of a 110 node HBase cluster.
NoSQL databases are currently used in several applications scenarios in contrast to Relations Databases. Several type of Databases there exist. In this presentation we compare Key Value, Column Oriented, Document Oriented and Graph Databases. Using a simple case study there are evaluated pros and cons of the NoSQL databases taken into account.
The document summarizes the status of RPKI deployment in Bangladesh. It finds that over 3% of Bangladeshi prefixes are invalid according to RPKI data, and some internet service providers are announcing prefixes longer than /24. The document recommends using ExaBGP and GIXLG tools to help identify invalidly originated prefixes and engage with internet service providers to resolve issues.
This document describes a big data pipeline for analyzing user sentiment from Twitter and stock market data. The pipeline ingests Twitter and stock market data from various sources into a Kafka cluster. It then uses Spark batch and streaming jobs to perform sentiment analysis, track trending stocks, and generate real-time and batch views that are stored in Cassandra. The views include time series data of tweet volumes and sentiments for individual stocks. The architecture aims to provide both real-time and batch processing capabilities for user sentiment analysis on stock market data.
Role of MySQL in Data Analytics, WarehousingVenu Anuganti
The document discusses the role of MySQL in data analytics and data warehousing. It describes how MySQL is widely used by many companies for online transaction processing (OLTP) and is the de facto standard for developers. While MySQL can be used for small data warehousing and analytics tasks, the document recommends using column-oriented databases with compression for large datasets due to MySQL's limitations in scalability for data warehousing. It provides tips on optimizing MySQL for analytics workloads and discusses using OLAP cubes and real-time analytics for near real-time insights.
The document discusses choosing between SQL and NoSQL databases. It covers the evolution of data architectures from traditional client-server models to newer distributed NoSQL solutions. It provides an overview of different data store types like SQL, NoSQL, key-value, document, column family, and graph databases. The document advises picking the right data model based on business needs, use cases, data storage requirements, and growth patterns then evaluating solutions based on pros and cons. It concludes that for large, growing data, both SQL and NoSQL solutions may be needed.
Designing Scalable Data Warehouse Using MySQLVenu Anuganti
The document discusses designing scalable data warehouses using MySQL. It covers topics like the role of MySQL in data warehousing and analytics, typical data warehouse architectures, scaling out MySQL, and limitations of MySQL for large datasets or as a scalable warehouse solution. Real-time analytics are also discussed, noting the challenges of performance and scalability for near real-time analytics.
This document provides an overview and best practices for operating HBase clusters. It discusses HBase and Hadoop architecture, how to set up an HBase cluster including Zookeeper and region servers, high availability considerations, scaling the cluster, backup and restore processes, and operational best practices around hardware, disks, OS, automation, load balancing, upgrades, monitoring and alerting. It also includes a case study of a 110 node HBase cluster.
NoSQL databases are currently used in several applications scenarios in contrast to Relations Databases. Several type of Databases there exist. In this presentation we compare Key Value, Column Oriented, Document Oriented and Graph Databases. Using a simple case study there are evaluated pros and cons of the NoSQL databases taken into account.
Big Data Analytics with MariaDB ColumnStoreMariaDB plc
Big Data Analytics with MariaDB ColumnStore provides an overview of MariaDB ColumnStore. Key points include:
- MariaDB ColumnStore is an open source columnar storage engine that provides high performance analytics on large datasets in a scalable distributed environment using standard SQL.
- Columnar storage organizes data by columns rather than rows, improving query performance by only accessing relevant columns. It supports workloads from terabytes to petabytes of data.
- Common use cases include data warehousing, financial services, healthcare, telecom, and any workload requiring analysis of millions to billions of rows.
- The architecture employs a distributed query processing model with horizontal partitioning and parallel query execution across nodes for high scalability
Managing data with an RDBMS is probably one of the key IT resources for roughly 40 years. Splitting up logical data structures into physical ones also known as partitioning is a key ingredient. The attached presentation demonstrates examples based on the recently released Oracle 12.2 database.
Update as of May 31st 2017: You might ask yourself: is this any practical? Yes it is. We are currently creating a bit of code to complement the example.
Oracle Query Tuning Tips - Get it Right the First TimeDean Richards
Whether you are a developer or DBA, this presentation will outline a method for determining the best approach for tuning a query every time by utilizing response time analysis and SQL Diagramming techniques. Regardless of the complexity of the statement or database platform being utilized (this method works on all), this quick and systematic approach will lead you down the correct
tuning path with no guessing. If you are a beginner or expert, this approach will save you countless hours tuning a query.
Session aims at introducing less familiar audience to the Oracle database statistics concept, why statistics are necessary and how the Oracle Cost-Based Optimizer uses them
Resilient Predictive Data Pipelines (QCon London 2016)Sid Anand
This document discusses building resilient predictive data pipelines. It begins by distinguishing between ETL and predictive data pipelines, noting that predictive pipelines require high availability with downtimes of less than an hour. The document then outlines design goals for resilient data pipelines, including being scalable, available, instrumented/monitored/alert-enabled, and quickly recoverable. It proposes using AWS services like SQS, SNS, S3, and Auto Scaling Groups to build such pipelines. The document also recommends using Apache Airflow for workflow automation and scheduling to reliably manage pipelines as directed acyclic graphs. It presents an architecture using these techniques and assesses how well it meets the outlined design goals.
Segue um material interessante do que a Vodafone está fazendo com o Splunk.
Esse em especial foi apresentado no .conf2013, convenção mundial da Splunk e teremos o .conf2014 em Outubro desse ano - programem-se e participem, vale cada centavo!
Lembrando, o .conf2014 já está com as inscrições abertas e em preço promocional.
Mais informações, aqui: http://conf.splunk.com/?r=homepage
This document discusses testing practices at Cookpad, a company that develops large web and mobile applications. It outlines their philosophy of prioritizing user experience and keeping development cycles quick through practices like frequent small releases and automated testing. For their web application, which is a large Ruby on Rails monocodebase, it describes their extensive use of RSpec tests as well as tools they developed to help speed up testing. For their mobile applications, it discusses their bi-weekly release process and focus on unit, integration and UI testing using tools like Appium and Espresso. It also acknowledges challenges in moving to microservices and continuous testing of mobile apps.
Large Scale Feature Aggregation Using Apache Spark with Pulkit Bhanot and Ami...Databricks
Aggregation based features account for a quarter of the several 1000s features used by the ML-based decisioning system by the Risk team at Uber. We observed several repetitive, cumbersome steps needed for onboarding a feature, every single time. Therefore, to accelerate developer velocity, and to enable Feature Engineering at scale, we decided to develop a generic spark based infrastructure to simplify the process to no more than a simple spec file, containing a parameterized query, along with some metadata on where the feature should be aggregated and stored.
In the presentation, we will describe the architecture of the final solution, highlighting some of the advanced capabilities like backfill support and self-healing for correctness. We will showcase how, using data stored in Hive and using Spark, we developed a highly scalable solution to carry out feature aggregation in an incremental way. By dividing data aggregation responsibility across the realtime access layer, and the batch computation components, we ensured that only entities for which there is actual value changes are dispersed to our real-time access store (Cassandra). We will share how we did data modeling in Cassandra using its native capabilities such as counters, and how we worked around some of the limitations of Cassandra. We will also cover the details about the access service how we do different types of feature stitching together. How, based on our data model we were able to ensure that all the feature for an entity with the same aggregation window, were queried via a single query. Finally, we will cover some of the details on how these incremental aggregated features have enabled shorter turnaround times for the models using such features.
This presentation recounts the story of Macys.com and Bloomingdales.com's migration from legacy RDBMS to NoSQL Cassandra in partnership with DataStax.
One thing that differentiates this talk from others on Cassandra is Macy's philosophy of "doing more with less." You will see why we emphasize the performance tuning aspects of iterative development when you see how much processing we can support on relatively small configurations.
This session will cover:
1) The process that led to our decision to use Cassandra
2) The approach we used for migrating from DB2 & Coherence to Cassandra without disrupting the production environment
3) The various schema options that we tried and how we settled on the current one. We'll show you a selection of some of our extensive performance tuning benchmarks, as well as how these performance results figured into our final schema designs.
4) Our lessons learned and next steps
This document outlines an agenda for an advanced Splunk user training workshop. The workshop covers topics like field aliasing, common information models, event types, tags, dashboard customization, index replication for high availability, report acceleration, and lookups. It provides overviews and examples for each topic and directs attendees to additional documentation resources for more in-depth learning. The workshop also includes demonstrations of dashboard customization techniques and discusses support options through the Splunk community.
This document describes a metadata-driven data loading framework that aims to simplify and optimize the onboarding of data applications at Walmart. The key points are:
1) The framework provides a centralized platform with plug-and-play onboarding capabilities to abstract away the complexities of integrating various data sources, sinks, and processors.
2) It utilizes metadata to configure applications and optimize resource allocation and scheduling based on priority. Connectors provide ready-to-use integrations and custom SQL UDFs allow flexible querying.
3) An orchestrator builds optimized execution plans and schedules application runs, while a scheduler optimizer prioritizes high-priority applications by dequeuing lower-priority jobs if needed.
Hitchhiker's Guide to free Oracle tuning toolsBjoern Rost
Instance and SQL tuning with EM12c Cloud Control is so easy, it is not even much fun
anymore. Also, not every customer may have the appropriate license or database
edition, or all you have available remotely is a command-line login to a database.
This presentation showcases a few open-source database tuning tools such as Snapper
and ASH replacements that DBAs can use to gather and review metrics and wait events
from the command line and even in standard edition.
Andreea Marin - Our journey into Cassandra performance optimisation -Codemotion
In Relay42 we need to handle real-time reprocessing of data with expiring TTLs. Due to the large amount of data that we have to store, we use Cassandra as our storage medium. The reprocessing of this data can include intensive deletion of data that results in the creation of tombstones, an issue that affected the performance of our entire platform. During this talk we will share the steps we took for fixing this issue, starting from the discovery, the monitoring, the cluster specific tweaking and the code changes that we had to do.
This document provides 9 hints for optimizing Oracle database performance:
1. Take a methodical and empirical approach to tuning by focusing on root causes, measuring performance before and after changes, and avoiding "silver bullets".
2. Design databases and applications with performance in mind from the beginning.
3. Index wisely by only creating useful indexes that improve performance without excessive overhead.
4. Leverage built-in Oracle tools like DBMS_XPLAN and SQL Trace to measure performance.
5. Tune the optimizer by adjusting parameters and statistics to encourage better execution plans.
6. Focus SQL and PL/SQL tuning on problem queries, joins, sorts, and DML statements.
7. Address
Monitoring Large-Scale Apache Spark Clusters at DatabricksAnyscale
At Databricks, we manage Apache Spark clusters for customers to run various production workloads. In this talk, we share our experiences in building a real-time monitoring system for thousands of Spark nodes, including the lessons we learned and the value we’ve seen from our efforts so far.
The was part of the talk presented at #monitorSF Meetup held at Databricks HQ in SF.
Talk for PerconaLive 2016 by Brendan Gregg. Video: https://www.youtube.com/watch?v=CbmEDXq7es0 . "Systems performance provides a different perspective for analysis and tuning, and can help you find performance wins for your databases, applications, and the kernel. However, most of us are not performance or kernel engineers, and have limited time to study this topic. This talk summarizes six important areas of Linux systems performance in 50 minutes: observability tools, methodologies, benchmarking, profiling, tracing, and tuning. Included are recipes for Linux performance analysis and tuning (using vmstat, mpstat, iostat, etc), overviews of complex areas including profiling (perf_events), static tracing (tracepoints), and dynamic tracing (kprobes, uprobes), and much advice about what is and isn't important to learn. This talk is aimed at everyone: DBAs, developers, operations, etc, and in any environment running Linux, bare-metal or the cloud."
Data Platform at Twitter: Enabling Real-time & Batch Analytics at ScaleSriram Krishnan
The Data Platform at Twitter supports engineers and data scientists running batch jobs on Hadoop clusters that are several 1000s of nodes, and real-time jobs on top of systems such as Storm. In this presentation, I discuss the overall Data Platform stack at Twitter. In particular, I talk about enabling real-time and batch analytics at scale with the help of Scalding, which is a Scala DSL for batch jobs using MapReduce, Summingbird, which is a framework for combined real-time and batch processing, and Tsar, which is a framework for real-time time-series aggregations.
Indexing Strategies for Oracle Databases - Beyond the Create Index StatementSean Scott
B-tree indexes are the most common type of index and order data within the index in branches and leaves. Composite indexes consist of more than one column to improve performance. When choosing indexes, consider columns frequently used in queries, primary keys, and foreign keys. Index maintenance includes rebuilding, coalescing, and shrinking indexes.
Blood finder application project report (1).pdfKamal Acharya
Blood Finder is an emergency time app where a user can search for the blood banks as
well as the registered blood donors around Mumbai. This application also provide an
opportunity for the user of this application to become a registered donor for this user have
to enroll for the donor request from the application itself. If the admin wish to make user
a registered donor, with some of the formalities with the organization it can be done.
Specialization of this application is that the user will not have to register on sign-in for
searching the blood banks and blood donors it can be just done by installing the
application to the mobile.
The purpose of making this application is to save the user’s time for searching blood of
needed blood group during the time of the emergency.
This is an android application developed in Java and XML with the connectivity of
SQLite database. This application will provide most of basic functionality required for an
emergency time application. All the details of Blood banks and Blood donors are stored
in the database i.e. SQLite.
This application allowed the user to get all the information regarding blood banks and
blood donors such as Name, Number, Address, Blood Group, rather than searching it on
the different websites and wasting the precious time. This application is effective and
user friendly.
Big Data Analytics with MariaDB ColumnStoreMariaDB plc
Big Data Analytics with MariaDB ColumnStore provides an overview of MariaDB ColumnStore. Key points include:
- MariaDB ColumnStore is an open source columnar storage engine that provides high performance analytics on large datasets in a scalable distributed environment using standard SQL.
- Columnar storage organizes data by columns rather than rows, improving query performance by only accessing relevant columns. It supports workloads from terabytes to petabytes of data.
- Common use cases include data warehousing, financial services, healthcare, telecom, and any workload requiring analysis of millions to billions of rows.
- The architecture employs a distributed query processing model with horizontal partitioning and parallel query execution across nodes for high scalability
Managing data with an RDBMS is probably one of the key IT resources for roughly 40 years. Splitting up logical data structures into physical ones also known as partitioning is a key ingredient. The attached presentation demonstrates examples based on the recently released Oracle 12.2 database.
Update as of May 31st 2017: You might ask yourself: is this any practical? Yes it is. We are currently creating a bit of code to complement the example.
Oracle Query Tuning Tips - Get it Right the First TimeDean Richards
Whether you are a developer or DBA, this presentation will outline a method for determining the best approach for tuning a query every time by utilizing response time analysis and SQL Diagramming techniques. Regardless of the complexity of the statement or database platform being utilized (this method works on all), this quick and systematic approach will lead you down the correct
tuning path with no guessing. If you are a beginner or expert, this approach will save you countless hours tuning a query.
Session aims at introducing less familiar audience to the Oracle database statistics concept, why statistics are necessary and how the Oracle Cost-Based Optimizer uses them
Resilient Predictive Data Pipelines (QCon London 2016)Sid Anand
This document discusses building resilient predictive data pipelines. It begins by distinguishing between ETL and predictive data pipelines, noting that predictive pipelines require high availability with downtimes of less than an hour. The document then outlines design goals for resilient data pipelines, including being scalable, available, instrumented/monitored/alert-enabled, and quickly recoverable. It proposes using AWS services like SQS, SNS, S3, and Auto Scaling Groups to build such pipelines. The document also recommends using Apache Airflow for workflow automation and scheduling to reliably manage pipelines as directed acyclic graphs. It presents an architecture using these techniques and assesses how well it meets the outlined design goals.
Segue um material interessante do que a Vodafone está fazendo com o Splunk.
Esse em especial foi apresentado no .conf2013, convenção mundial da Splunk e teremos o .conf2014 em Outubro desse ano - programem-se e participem, vale cada centavo!
Lembrando, o .conf2014 já está com as inscrições abertas e em preço promocional.
Mais informações, aqui: http://conf.splunk.com/?r=homepage
This document discusses testing practices at Cookpad, a company that develops large web and mobile applications. It outlines their philosophy of prioritizing user experience and keeping development cycles quick through practices like frequent small releases and automated testing. For their web application, which is a large Ruby on Rails monocodebase, it describes their extensive use of RSpec tests as well as tools they developed to help speed up testing. For their mobile applications, it discusses their bi-weekly release process and focus on unit, integration and UI testing using tools like Appium and Espresso. It also acknowledges challenges in moving to microservices and continuous testing of mobile apps.
Large Scale Feature Aggregation Using Apache Spark with Pulkit Bhanot and Ami...Databricks
Aggregation based features account for a quarter of the several 1000s features used by the ML-based decisioning system by the Risk team at Uber. We observed several repetitive, cumbersome steps needed for onboarding a feature, every single time. Therefore, to accelerate developer velocity, and to enable Feature Engineering at scale, we decided to develop a generic spark based infrastructure to simplify the process to no more than a simple spec file, containing a parameterized query, along with some metadata on where the feature should be aggregated and stored.
In the presentation, we will describe the architecture of the final solution, highlighting some of the advanced capabilities like backfill support and self-healing for correctness. We will showcase how, using data stored in Hive and using Spark, we developed a highly scalable solution to carry out feature aggregation in an incremental way. By dividing data aggregation responsibility across the realtime access layer, and the batch computation components, we ensured that only entities for which there is actual value changes are dispersed to our real-time access store (Cassandra). We will share how we did data modeling in Cassandra using its native capabilities such as counters, and how we worked around some of the limitations of Cassandra. We will also cover the details about the access service how we do different types of feature stitching together. How, based on our data model we were able to ensure that all the feature for an entity with the same aggregation window, were queried via a single query. Finally, we will cover some of the details on how these incremental aggregated features have enabled shorter turnaround times for the models using such features.
This presentation recounts the story of Macys.com and Bloomingdales.com's migration from legacy RDBMS to NoSQL Cassandra in partnership with DataStax.
One thing that differentiates this talk from others on Cassandra is Macy's philosophy of "doing more with less." You will see why we emphasize the performance tuning aspects of iterative development when you see how much processing we can support on relatively small configurations.
This session will cover:
1) The process that led to our decision to use Cassandra
2) The approach we used for migrating from DB2 & Coherence to Cassandra without disrupting the production environment
3) The various schema options that we tried and how we settled on the current one. We'll show you a selection of some of our extensive performance tuning benchmarks, as well as how these performance results figured into our final schema designs.
4) Our lessons learned and next steps
This document outlines an agenda for an advanced Splunk user training workshop. The workshop covers topics like field aliasing, common information models, event types, tags, dashboard customization, index replication for high availability, report acceleration, and lookups. It provides overviews and examples for each topic and directs attendees to additional documentation resources for more in-depth learning. The workshop also includes demonstrations of dashboard customization techniques and discusses support options through the Splunk community.
This document describes a metadata-driven data loading framework that aims to simplify and optimize the onboarding of data applications at Walmart. The key points are:
1) The framework provides a centralized platform with plug-and-play onboarding capabilities to abstract away the complexities of integrating various data sources, sinks, and processors.
2) It utilizes metadata to configure applications and optimize resource allocation and scheduling based on priority. Connectors provide ready-to-use integrations and custom SQL UDFs allow flexible querying.
3) An orchestrator builds optimized execution plans and schedules application runs, while a scheduler optimizer prioritizes high-priority applications by dequeuing lower-priority jobs if needed.
Hitchhiker's Guide to free Oracle tuning toolsBjoern Rost
Instance and SQL tuning with EM12c Cloud Control is so easy, it is not even much fun
anymore. Also, not every customer may have the appropriate license or database
edition, or all you have available remotely is a command-line login to a database.
This presentation showcases a few open-source database tuning tools such as Snapper
and ASH replacements that DBAs can use to gather and review metrics and wait events
from the command line and even in standard edition.
Andreea Marin - Our journey into Cassandra performance optimisation -Codemotion
In Relay42 we need to handle real-time reprocessing of data with expiring TTLs. Due to the large amount of data that we have to store, we use Cassandra as our storage medium. The reprocessing of this data can include intensive deletion of data that results in the creation of tombstones, an issue that affected the performance of our entire platform. During this talk we will share the steps we took for fixing this issue, starting from the discovery, the monitoring, the cluster specific tweaking and the code changes that we had to do.
This document provides 9 hints for optimizing Oracle database performance:
1. Take a methodical and empirical approach to tuning by focusing on root causes, measuring performance before and after changes, and avoiding "silver bullets".
2. Design databases and applications with performance in mind from the beginning.
3. Index wisely by only creating useful indexes that improve performance without excessive overhead.
4. Leverage built-in Oracle tools like DBMS_XPLAN and SQL Trace to measure performance.
5. Tune the optimizer by adjusting parameters and statistics to encourage better execution plans.
6. Focus SQL and PL/SQL tuning on problem queries, joins, sorts, and DML statements.
7. Address
Monitoring Large-Scale Apache Spark Clusters at DatabricksAnyscale
At Databricks, we manage Apache Spark clusters for customers to run various production workloads. In this talk, we share our experiences in building a real-time monitoring system for thousands of Spark nodes, including the lessons we learned and the value we’ve seen from our efforts so far.
The was part of the talk presented at #monitorSF Meetup held at Databricks HQ in SF.
Talk for PerconaLive 2016 by Brendan Gregg. Video: https://www.youtube.com/watch?v=CbmEDXq7es0 . "Systems performance provides a different perspective for analysis and tuning, and can help you find performance wins for your databases, applications, and the kernel. However, most of us are not performance or kernel engineers, and have limited time to study this topic. This talk summarizes six important areas of Linux systems performance in 50 minutes: observability tools, methodologies, benchmarking, profiling, tracing, and tuning. Included are recipes for Linux performance analysis and tuning (using vmstat, mpstat, iostat, etc), overviews of complex areas including profiling (perf_events), static tracing (tracepoints), and dynamic tracing (kprobes, uprobes), and much advice about what is and isn't important to learn. This talk is aimed at everyone: DBAs, developers, operations, etc, and in any environment running Linux, bare-metal or the cloud."
Data Platform at Twitter: Enabling Real-time & Batch Analytics at ScaleSriram Krishnan
The Data Platform at Twitter supports engineers and data scientists running batch jobs on Hadoop clusters that are several 1000s of nodes, and real-time jobs on top of systems such as Storm. In this presentation, I discuss the overall Data Platform stack at Twitter. In particular, I talk about enabling real-time and batch analytics at scale with the help of Scalding, which is a Scala DSL for batch jobs using MapReduce, Summingbird, which is a framework for combined real-time and batch processing, and Tsar, which is a framework for real-time time-series aggregations.
Indexing Strategies for Oracle Databases - Beyond the Create Index StatementSean Scott
B-tree indexes are the most common type of index and order data within the index in branches and leaves. Composite indexes consist of more than one column to improve performance. When choosing indexes, consider columns frequently used in queries, primary keys, and foreign keys. Index maintenance includes rebuilding, coalescing, and shrinking indexes.
Blood finder application project report (1).pdfKamal Acharya
Blood Finder is an emergency time app where a user can search for the blood banks as
well as the registered blood donors around Mumbai. This application also provide an
opportunity for the user of this application to become a registered donor for this user have
to enroll for the donor request from the application itself. If the admin wish to make user
a registered donor, with some of the formalities with the organization it can be done.
Specialization of this application is that the user will not have to register on sign-in for
searching the blood banks and blood donors it can be just done by installing the
application to the mobile.
The purpose of making this application is to save the user’s time for searching blood of
needed blood group during the time of the emergency.
This is an android application developed in Java and XML with the connectivity of
SQLite database. This application will provide most of basic functionality required for an
emergency time application. All the details of Blood banks and Blood donors are stored
in the database i.e. SQLite.
This application allowed the user to get all the information regarding blood banks and
blood donors such as Name, Number, Address, Blood Group, rather than searching it on
the different websites and wasting the precious time. This application is effective and
user friendly.
Impartiality as per ISO /IEC 17025:2017 StandardMuhammadJazib15
This document provides basic guidelines for imparitallity requirement of ISO 17025. It defines in detial how it is met and wiudhwdih jdhsjdhwudjwkdbjwkdddddddddddkkkkkkkkkkkkkkkkkkkkkkkwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwioiiiiiiiiiiiii uwwwwwwwwwwwwwwwwhe wiqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqq gbbbbbbbbbbbbb owdjjjjjjjjjjjjjjjjjjjj widhi owqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqq uwdhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhwqiiiiiiiiiiiiiiiiiiiiiiiiiiiiw0pooooojjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjj whhhhhhhhhhh wheeeeeeee wihieiiiiii wihe
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AI in customer support Use cases solutions development and implementation.pdfmahaffeycheryld
AI in customer support will integrate with emerging technologies such as augmented reality (AR) and virtual reality (VR) to enhance service delivery. AR-enabled smart glasses or VR environments will provide immersive support experiences, allowing customers to visualize solutions, receive step-by-step guidance, and interact with virtual support agents in real-time. These technologies will bridge the gap between physical and digital experiences, offering innovative ways to resolve issues, demonstrate products, and deliver personalized training and support.
https://www.leewayhertz.com/ai-in-customer-support/#How-does-AI-work-in-customer-support
A high-Speed Communication System is based on the Design of a Bi-NoC Router, ...DharmaBanothu
The Network on Chip (NoC) has emerged as an effective
solution for intercommunication infrastructure within System on
Chip (SoC) designs, overcoming the limitations of traditional
methods that face significant bottlenecks. However, the complexity
of NoC design presents numerous challenges related to
performance metrics such as scalability, latency, power
consumption, and signal integrity. This project addresses the
issues within the router's memory unit and proposes an enhanced
memory structure. To achieve efficient data transfer, FIFO buffers
are implemented in distributed RAM and virtual channels for
FPGA-based NoC. The project introduces advanced FIFO-based
memory units within the NoC router, assessing their performance
in a Bi-directional NoC (Bi-NoC) configuration. The primary
objective is to reduce the router's workload while enhancing the
FIFO internal structure. To further improve data transfer speed,
a Bi-NoC with a self-configurable intercommunication channel is
suggested. Simulation and synthesis results demonstrate
guaranteed throughput, predictable latency, and equitable
network access, showing significant improvement over previous
designs
Levelised Cost of Hydrogen (LCOH) Calculator ManualMassimo Talia
The aim of this manual is to explain the
methodology behind the Levelized Cost of
Hydrogen (LCOH) calculator. Moreover, this
manual also demonstrates how the calculator
can be used for estimating the expenses associated with hydrogen production in Europe
using low-temperature electrolysis considering different sources of electricity
Supermarket Management System Project Report.pdfKamal Acharya
Supermarket management is a stand-alone J2EE using Eclipse Juno program.
This project contains all the necessary required information about maintaining
the supermarket billing system.
The core idea of this project to minimize the paper work and centralize the
data. Here all the communication is taken in secure manner. That is, in this
application the information will be stored in client itself. For further security the
data base is stored in the back-end oracle and so no intruders can access it.
Digital Twins Computer Networking Paper Presentation.pptxaryanpankaj78
A Digital Twin in computer networking is a virtual representation of a physical network, used to simulate, analyze, and optimize network performance and reliability. It leverages real-time data to enhance network management, predict issues, and improve decision-making processes.
2. MOTIVATION
• People have opinions
• Different sources, different mediums -Twitter, Reddit, Facebook etc.
• Platform for aggregating opinions and analyzing on aTopic
• v 1.0: User’s opinion of US stock market
16. SPEEDVIEW
• CassandraTTL support can be used for rolling count operation for dashboard
application
• Not available in Cassandra-Spark connector
• Add timestamp and ranking to each ticker generation in each 5 second window
• Partitioned by ranking, clustering order by timestamp
id | timestamp | frequency | sentiment | ticker
----+------------+-----------+-----------+--------
0 | 1430375561 | 55 | -5 | AAPL
0 | 1430370589 | 55 | -5 | AAPL
0 | 1430365508 | 54 | -5 | AAPL
0 | 1430360540 | 54 | -5 | AAPL