Ready to send your data to Sumo Logic? Learn the details of data collection, including:
- Installed versus Hosted Collectors
- Deployment Options and Best Practices
- Creating your Sources
- Processing Rules
- Local File Configuration Management
- Collector Management API
** This webinar is intended for Administrators with access to create Data Collectors.
QuickStart your Sumo Logic service with this exclusive webinar. At these monthly live events you will learn how to capitalize on critical capabilities that can amplify your log analytics and monitoring experience while providing you with meaningful business and IT insights.
Live Webinar is found here: https://youtu.be/Q1yWlInxWVs
The document discusses various components of the ELK stack including Elasticsearch, Logstash, Kibana, and how they work together. It provides descriptions of each component, what they are used for, and key features of Kibana such as its user interface, visualization capabilities, and why it is used.
How to Design, Build and Map IT and Business Services in SplunkSplunk
Your IT department supports critical business functions, processes and products. You're most effective when your technology initiatives are closely aligned and measured with specific business objectives. This session covers best practices and techniques for designing and building an effective service model, using the domain knowledge of your experts and capturing and reporting on key metrics that everyone can understand. We will design a sample service model and map them to performance indicators to track operational and business objectives. We will also show you how to make Splunk service-ware with Splunk IT Service Intelligence (ITSI).
Sumo Logic - Optimizing Your Search Experience (2016-08-17)Sumo Logic
The document discusses optimizing searches in Sumo Logic. It covers basic search structure, setting performance expectations, and optimization tools like field extraction rules, partitions, and scheduled views. Field extraction rules extract fields during ingestion to standardize searches and simplify parsing. Partitions divide data to improve search performance by searching smaller chunks. Scheduled views pre-aggregate data to significantly improve performance for selective queries and long-term trend analysis. The document provides recommendations on when and how to use these optimization tools to improve search performance.
Hive Bucketing in Apache Spark with Tejas PatilDatabricks
Bucketing is a partitioning technique that can improve performance in certain data transformations by avoiding data shuffling and sorting. The general idea of bucketing is to partition, and optionally sort, the data based on a subset of columns while it is written out (a one-time cost), while making successive reads of the data more performant for downstream jobs if the SQL operators can make use of this property. Bucketing can enable faster joins (i.e. single stage sort merge join), the ability to short circuit in FILTER operation if the file is pre-sorted over the column in a filter predicate, and it supports quick data sampling.
In this session, you’ll learn how bucketing is implemented in both Hive and Spark. In particular, Patil will describe the changes in the Catalyst optimizer that enable these optimizations in Spark for various bucketing scenarios. Facebook’s performance tests have shown bucketing to improve Spark performance from 3-5x faster when the optimization is enabled. Many tables at Facebook are sorted and bucketed, and migrating these workloads to Spark have resulted in a 2-3x savings when compared to Hive. You’ll also hear about real-world applications of bucketing, like loading of cumulative tables with daily delta, and the characteristics that can help identify suitable candidate jobs that can benefit from bucketing.
The document discusses a plan to leverage machine learning to define a quality index for incoming data sets and flag outliers. It details data definitions, mapping, sanity checks, missing value imputation, outlier detection methods, and rules for different datasets. Patterns identified include skewed distributions for certain metrics, correlations between variables, and outliers for specific brands or categories. The goal is to generalize the solution through active learning and integrate it with Excel to flag issues preemptively.
Deep Dive into the New Features of Apache Spark 3.0Databricks
Continuing with the objectives to make Spark faster, easier, and smarter, Apache Spark 3.0 extends its scope with more than 3000 resolved JIRAs. We will talk about the exciting new developments in the Spark 3.0 as well as some other major initiatives that are coming in the future.
This document discusses how Kafka handles timestamps and offsets. It explains that Kafka maintains offset and time-based indexes to allow fetching log data by offset or timestamp. When new log records are appended, the indexes are updated with the largest offset and timestamp. If a record has a timestamp older than the existing minimum in the time index, Kafka will still append it but the time index entry will not be updated.
QuickStart your Sumo Logic service with this exclusive webinar. At these monthly live events you will learn how to capitalize on critical capabilities that can amplify your log analytics and monitoring experience while providing you with meaningful business and IT insights.
Live Webinar is found here: https://youtu.be/Q1yWlInxWVs
The document discusses various components of the ELK stack including Elasticsearch, Logstash, Kibana, and how they work together. It provides descriptions of each component, what they are used for, and key features of Kibana such as its user interface, visualization capabilities, and why it is used.
How to Design, Build and Map IT and Business Services in SplunkSplunk
Your IT department supports critical business functions, processes and products. You're most effective when your technology initiatives are closely aligned and measured with specific business objectives. This session covers best practices and techniques for designing and building an effective service model, using the domain knowledge of your experts and capturing and reporting on key metrics that everyone can understand. We will design a sample service model and map them to performance indicators to track operational and business objectives. We will also show you how to make Splunk service-ware with Splunk IT Service Intelligence (ITSI).
Sumo Logic - Optimizing Your Search Experience (2016-08-17)Sumo Logic
The document discusses optimizing searches in Sumo Logic. It covers basic search structure, setting performance expectations, and optimization tools like field extraction rules, partitions, and scheduled views. Field extraction rules extract fields during ingestion to standardize searches and simplify parsing. Partitions divide data to improve search performance by searching smaller chunks. Scheduled views pre-aggregate data to significantly improve performance for selective queries and long-term trend analysis. The document provides recommendations on when and how to use these optimization tools to improve search performance.
Hive Bucketing in Apache Spark with Tejas PatilDatabricks
Bucketing is a partitioning technique that can improve performance in certain data transformations by avoiding data shuffling and sorting. The general idea of bucketing is to partition, and optionally sort, the data based on a subset of columns while it is written out (a one-time cost), while making successive reads of the data more performant for downstream jobs if the SQL operators can make use of this property. Bucketing can enable faster joins (i.e. single stage sort merge join), the ability to short circuit in FILTER operation if the file is pre-sorted over the column in a filter predicate, and it supports quick data sampling.
In this session, you’ll learn how bucketing is implemented in both Hive and Spark. In particular, Patil will describe the changes in the Catalyst optimizer that enable these optimizations in Spark for various bucketing scenarios. Facebook’s performance tests have shown bucketing to improve Spark performance from 3-5x faster when the optimization is enabled. Many tables at Facebook are sorted and bucketed, and migrating these workloads to Spark have resulted in a 2-3x savings when compared to Hive. You’ll also hear about real-world applications of bucketing, like loading of cumulative tables with daily delta, and the characteristics that can help identify suitable candidate jobs that can benefit from bucketing.
The document discusses a plan to leverage machine learning to define a quality index for incoming data sets and flag outliers. It details data definitions, mapping, sanity checks, missing value imputation, outlier detection methods, and rules for different datasets. Patterns identified include skewed distributions for certain metrics, correlations between variables, and outliers for specific brands or categories. The goal is to generalize the solution through active learning and integrate it with Excel to flag issues preemptively.
Deep Dive into the New Features of Apache Spark 3.0Databricks
Continuing with the objectives to make Spark faster, easier, and smarter, Apache Spark 3.0 extends its scope with more than 3000 resolved JIRAs. We will talk about the exciting new developments in the Spark 3.0 as well as some other major initiatives that are coming in the future.
This document discusses how Kafka handles timestamps and offsets. It explains that Kafka maintains offset and time-based indexes to allow fetching log data by offset or timestamp. When new log records are appended, the indexes are updated with the largest offset and timestamp. If a record has a timestamp older than the existing minimum in the time index, Kafka will still append it but the time index entry will not be updated.
Presented at the Machine Learning class at Chalmers, Gothenburg.
http://www.cse.chalmers.se/research/lab/courses.php?coid=9
Trying to connect their theoretical machine learning class with industry examples.
This document summarizes a team's presentation on sentiment analysis of Twitter data. It introduces the purpose of sentiment analysis and challenges of using Twitter data. It then describes two classification algorithms - a Multinomial Naïve Bayes classifier and a Recursive Deep Model based on Recursive Neural Tensor Networks. The team contributed improvements to the Recursive Deep Model and tested both algorithms on 1400 classified tweets, finding the Recursive Deep Model achieved higher accuracy but with much longer execution time. The conclusion suggests the Recursive Deep Model could be enhanced to support multiple languages.
This document describes a student project on speech-based emotion recognition. The project uses convolutional neural networks (CNN) and mel-frequency cepstral coefficients (MFCC) to classify emotions in speech into categories like happy, sad, fearful, calm and angry. The proposed system provides advantages over existing systems by allowing variable length audio inputs, faster processing, and real-time classification of more emotion categories. It achieves a test accuracy of 91.04% according to the document.
MMCF: Multimodal Collaborative Filtering for Automatic Playlist ConitnuationHojin Yang
The slides used for presentation in the 'ecSys challenge workshop 2018'. The challenge is co-organized by Spotify. Our team('hello world!') won the 2nd place.
HIVE: Data Warehousing & Analytics on HadoopZheng Shao
Hive is a data warehousing system built on Hadoop that allows users to query data using SQL. It addresses issues with using Hadoop for analytics like programmability and metadata. Hive uses a metastore to manage metadata and supports structured data types, SQL queries, and custom MapReduce scripts. At Facebook, Hive is used for analytics tasks like summarization, ad hoc analysis, and data mining on over 180TB of data processed daily across a Hadoop cluster.
Centralized log-management-with-elastic-stackRich Lee
Centralized log management is implemented using the Elastic Stack including Filebeat, Logstash, Elasticsearch, and Kibana. Filebeat ships logs to Logstash which transforms and indexes the data into Elasticsearch. Logs can then be queried and visualized in Kibana. For large volumes of logs, Kafka may be used as a buffer between the shipper and indexer. Backups are performed using Elasticsearch snapshots to a shared file system or cloud storage. Logs are indexed into time-based indices and a cron job deletes old indices to control storage usage.
This document discusses anomaly detection techniques for intrusion detection systems. It begins by defining anomalies and explaining the principles of anomaly detection models. It then describes some key challenges in anomaly detection and different types of outputs it can provide. The document proceeds to classify anomaly detection techniques into statistical, machine learning and data mining based methods. As examples, it examines several case studies of early statistical anomaly detection systems like Haystack and IDES.
Learning to Rank in Solr: Presented by Michael Nilsson & Diego Ceccarelli, Bl...Lucidworks
This document summarizes Bloomberg's use of machine learning for search ranking within their Solr implementation. It discusses how they process 8 million searches per day and need machine learning to automatically tune rankings over time as their index grows to 400 million documents. They use a Learning to Rank approach where features are extracted from queries and documents, training data is collected, and a ranking model is generated to optimize metrics like click-through rates. Their Solr Learning to Rank plugin allows this model to re-rank search results in Solr for improved relevance.
This session talks about how unit testing of Spark applications is done, as well as tells the best way to do it. This includes writing unit tests with and without Spark Testing Base package, which is a spark package containing base classes to use when writing tests with Spark.
Kibana + timelion: time series with the elastic stackSylvain Wallez
The document discusses Kibana and Timelion, which are tools for visualizing and analyzing time series data in the Elastic Stack. It provides an overview of Kibana's evolution and capabilities for creating dashboards. Timelion is introduced as a scripting language that allows users to transform, aggregate, and calculate on time series data from multiple sources to create visualizations. The document demonstrates Timelion's expression language, which includes functions, combinations, filtering, and attributes to process and render time series graphs.
This document discusses issues with using synonyms in Solr search queries and indexing. It covers two main issues:
1. Index-time "sausagization" where multi-term synonyms are incorrectly treated as single terms during indexing, leading to unexpected phrase matches and non-matches.
2. Interactions between filters that produce token graphs like synonyms and word delimiter filters, which can result in undefined query parsing behavior.
Workarounds discussed include splitting synonyms, injecting "semantic units", and configuring filters to not produce token graphs in certain combinations. The document recommends carefully configuring synonyms and filters to avoid these issues.
Este documento apresenta os principais conceitos do Elasticsearch, incluindo sua arquitetura orientada a documentos, indexação, buscas, failover e escalabilidade. Demonstra também a instalação, interação via API e indexação de documentos no Elasticsearch.
This document provides an overview of Lucene scoring and sorting algorithms. It describes how Lucene constructs a Hits object to handle scoring and caching of search results. It explains that Lucene scores documents by calling the getScore() method on a Scorer object, which depends on the type of query. For boolean queries, it typically uses a BooleanScorer2. The scoring process advances through documents matching the query terms. Sorting requires additional memory to cache fields used for sorting.
The document describes Apache Hive hooks, which allow intercepting function calls or events during query execution in Hive. It provides details on the different hook points in Hive, including pre-execution, post-execution, and failure hooks. It also explains how to configure hooks by setting hook properties and the jar paths for hook implementations. Finally, it outlines the interfaces and contexts provided to hooks at each stage of query processing in Hive.
The 8 Best Examples Of Real-Time Data AnalyticsBernard Marr
Real-time analytics are already being used in a wide range of business applications, including cracking down on fake news and helping police make cities safer. Find out more amazing examples of how companies today are using streaming analytics in real life.
Elastic search
Moteur de recherche
Crée en 2010 par Shay Banon
Basé sur Apache Lucene (+multi-nodes)
Développé en Java
Open source (Licence Apache)
La société a été crée en 2012
La version courante est 2.0
Site officiel: https://www.elastic.co/
by Harald Steck (Netflix Inc., US), Roelof van Zwol (Netflix Inc., US) and Chris Johnson (Spotify Inc., US)
Slides of the tutorial on interactive recommender systems at the 2015 conference on Recommender Systems (RecSys).
Interactive recommender systems enable the user to steer the received recommendations in the desired direction through explicit interaction with the system. In the larger ecosystem of recommender systems used on a website, it is positioned between a lean-back recommendation experience and an active search for a specific piece of content. Besides this aspect, we will discuss several parts that are especially important for interactive recommender systems, including the following: design of the user interface and its tight integration with the algorithm in the back-end; computational efficiency of the recommender algorithm; as well as choosing the right balance between exploiting the feedback from the user as to provide relevant recommendations, and enabling the user to explore the catalog and steer the recommendations in the desired direction.
In particular, we will explore the field of interactive video and music recommendations and their application at Netflix and Spotify. We outline some of the user-experiences built, and discuss the approaches followed to tackle the various aspects of interactive recommendations. We present our insights from user studies and A/B tests.
The tutorial targets researchers and practitioners in the field of recommender systems, and will give the participants a unique opportunity to learn about the various aspects of interactive recommender systems in the video and music domain. The tutorial assumes familiarity with the common methods of recommender systems.
DATE: Wednesday, Sept 16, 2015, 11:00-12:30
The document discusses techniques for storing time series data at scale in a time series database (TSDB). It describes storing 16 bytes of data per sample by compressing timestamps and values. It proposes organizing data into blocks, chunks, and files to handle high churn rates. An index structure uses unique IDs and sorted label mappings to enable efficient queries over millions of time series and billions of samples. Benchmarks show the TSDB can handle over 100,000 samples/second while keeping memory, CPU and disk usage low.
This document provides an overview and introduction to Elasticsearch. It discusses the speaker's experience and community involvement. It then covers how to set up Elasticsearch and Kibana locally. The rest of the document describes various Elasticsearch concepts and features like clusters, nodes, indexes, documents, shards, replicas, and building search-based applications. It also discusses using Elasticsearch for big data, different search capabilities, and text analysis.
Dashboards are fantastic, but how do I get notified of critical events? This webinar will cover how to create alerts that will allow your team to effectively monitor business-critical events. Alert channels include email or webhooks into Slack, PagerDuty, DataDog, ServiceNow, or any other webhook you want to develop. What about running custom scripts triggered from alerts? Let's do it.
Sumo Logic exposes the Search Job API for access to resources and log data from third-party scripts and applications.
Targeting experienced Sumo Administrators, this webinar shows you how to leverage the Search Job API to interact with the Sumo Logic service. Everyone attending should be familiar with the concepts of RESTful web services and JSON.
Presented at the Machine Learning class at Chalmers, Gothenburg.
http://www.cse.chalmers.se/research/lab/courses.php?coid=9
Trying to connect their theoretical machine learning class with industry examples.
This document summarizes a team's presentation on sentiment analysis of Twitter data. It introduces the purpose of sentiment analysis and challenges of using Twitter data. It then describes two classification algorithms - a Multinomial Naïve Bayes classifier and a Recursive Deep Model based on Recursive Neural Tensor Networks. The team contributed improvements to the Recursive Deep Model and tested both algorithms on 1400 classified tweets, finding the Recursive Deep Model achieved higher accuracy but with much longer execution time. The conclusion suggests the Recursive Deep Model could be enhanced to support multiple languages.
This document describes a student project on speech-based emotion recognition. The project uses convolutional neural networks (CNN) and mel-frequency cepstral coefficients (MFCC) to classify emotions in speech into categories like happy, sad, fearful, calm and angry. The proposed system provides advantages over existing systems by allowing variable length audio inputs, faster processing, and real-time classification of more emotion categories. It achieves a test accuracy of 91.04% according to the document.
MMCF: Multimodal Collaborative Filtering for Automatic Playlist ConitnuationHojin Yang
The slides used for presentation in the 'ecSys challenge workshop 2018'. The challenge is co-organized by Spotify. Our team('hello world!') won the 2nd place.
HIVE: Data Warehousing & Analytics on HadoopZheng Shao
Hive is a data warehousing system built on Hadoop that allows users to query data using SQL. It addresses issues with using Hadoop for analytics like programmability and metadata. Hive uses a metastore to manage metadata and supports structured data types, SQL queries, and custom MapReduce scripts. At Facebook, Hive is used for analytics tasks like summarization, ad hoc analysis, and data mining on over 180TB of data processed daily across a Hadoop cluster.
Centralized log-management-with-elastic-stackRich Lee
Centralized log management is implemented using the Elastic Stack including Filebeat, Logstash, Elasticsearch, and Kibana. Filebeat ships logs to Logstash which transforms and indexes the data into Elasticsearch. Logs can then be queried and visualized in Kibana. For large volumes of logs, Kafka may be used as a buffer between the shipper and indexer. Backups are performed using Elasticsearch snapshots to a shared file system or cloud storage. Logs are indexed into time-based indices and a cron job deletes old indices to control storage usage.
This document discusses anomaly detection techniques for intrusion detection systems. It begins by defining anomalies and explaining the principles of anomaly detection models. It then describes some key challenges in anomaly detection and different types of outputs it can provide. The document proceeds to classify anomaly detection techniques into statistical, machine learning and data mining based methods. As examples, it examines several case studies of early statistical anomaly detection systems like Haystack and IDES.
Learning to Rank in Solr: Presented by Michael Nilsson & Diego Ceccarelli, Bl...Lucidworks
This document summarizes Bloomberg's use of machine learning for search ranking within their Solr implementation. It discusses how they process 8 million searches per day and need machine learning to automatically tune rankings over time as their index grows to 400 million documents. They use a Learning to Rank approach where features are extracted from queries and documents, training data is collected, and a ranking model is generated to optimize metrics like click-through rates. Their Solr Learning to Rank plugin allows this model to re-rank search results in Solr for improved relevance.
This session talks about how unit testing of Spark applications is done, as well as tells the best way to do it. This includes writing unit tests with and without Spark Testing Base package, which is a spark package containing base classes to use when writing tests with Spark.
Kibana + timelion: time series with the elastic stackSylvain Wallez
The document discusses Kibana and Timelion, which are tools for visualizing and analyzing time series data in the Elastic Stack. It provides an overview of Kibana's evolution and capabilities for creating dashboards. Timelion is introduced as a scripting language that allows users to transform, aggregate, and calculate on time series data from multiple sources to create visualizations. The document demonstrates Timelion's expression language, which includes functions, combinations, filtering, and attributes to process and render time series graphs.
This document discusses issues with using synonyms in Solr search queries and indexing. It covers two main issues:
1. Index-time "sausagization" where multi-term synonyms are incorrectly treated as single terms during indexing, leading to unexpected phrase matches and non-matches.
2. Interactions between filters that produce token graphs like synonyms and word delimiter filters, which can result in undefined query parsing behavior.
Workarounds discussed include splitting synonyms, injecting "semantic units", and configuring filters to not produce token graphs in certain combinations. The document recommends carefully configuring synonyms and filters to avoid these issues.
Este documento apresenta os principais conceitos do Elasticsearch, incluindo sua arquitetura orientada a documentos, indexação, buscas, failover e escalabilidade. Demonstra também a instalação, interação via API e indexação de documentos no Elasticsearch.
This document provides an overview of Lucene scoring and sorting algorithms. It describes how Lucene constructs a Hits object to handle scoring and caching of search results. It explains that Lucene scores documents by calling the getScore() method on a Scorer object, which depends on the type of query. For boolean queries, it typically uses a BooleanScorer2. The scoring process advances through documents matching the query terms. Sorting requires additional memory to cache fields used for sorting.
The document describes Apache Hive hooks, which allow intercepting function calls or events during query execution in Hive. It provides details on the different hook points in Hive, including pre-execution, post-execution, and failure hooks. It also explains how to configure hooks by setting hook properties and the jar paths for hook implementations. Finally, it outlines the interfaces and contexts provided to hooks at each stage of query processing in Hive.
The 8 Best Examples Of Real-Time Data AnalyticsBernard Marr
Real-time analytics are already being used in a wide range of business applications, including cracking down on fake news and helping police make cities safer. Find out more amazing examples of how companies today are using streaming analytics in real life.
Elastic search
Moteur de recherche
Crée en 2010 par Shay Banon
Basé sur Apache Lucene (+multi-nodes)
Développé en Java
Open source (Licence Apache)
La société a été crée en 2012
La version courante est 2.0
Site officiel: https://www.elastic.co/
by Harald Steck (Netflix Inc., US), Roelof van Zwol (Netflix Inc., US) and Chris Johnson (Spotify Inc., US)
Slides of the tutorial on interactive recommender systems at the 2015 conference on Recommender Systems (RecSys).
Interactive recommender systems enable the user to steer the received recommendations in the desired direction through explicit interaction with the system. In the larger ecosystem of recommender systems used on a website, it is positioned between a lean-back recommendation experience and an active search for a specific piece of content. Besides this aspect, we will discuss several parts that are especially important for interactive recommender systems, including the following: design of the user interface and its tight integration with the algorithm in the back-end; computational efficiency of the recommender algorithm; as well as choosing the right balance between exploiting the feedback from the user as to provide relevant recommendations, and enabling the user to explore the catalog and steer the recommendations in the desired direction.
In particular, we will explore the field of interactive video and music recommendations and their application at Netflix and Spotify. We outline some of the user-experiences built, and discuss the approaches followed to tackle the various aspects of interactive recommendations. We present our insights from user studies and A/B tests.
The tutorial targets researchers and practitioners in the field of recommender systems, and will give the participants a unique opportunity to learn about the various aspects of interactive recommender systems in the video and music domain. The tutorial assumes familiarity with the common methods of recommender systems.
DATE: Wednesday, Sept 16, 2015, 11:00-12:30
The document discusses techniques for storing time series data at scale in a time series database (TSDB). It describes storing 16 bytes of data per sample by compressing timestamps and values. It proposes organizing data into blocks, chunks, and files to handle high churn rates. An index structure uses unique IDs and sorted label mappings to enable efficient queries over millions of time series and billions of samples. Benchmarks show the TSDB can handle over 100,000 samples/second while keeping memory, CPU and disk usage low.
This document provides an overview and introduction to Elasticsearch. It discusses the speaker's experience and community involvement. It then covers how to set up Elasticsearch and Kibana locally. The rest of the document describes various Elasticsearch concepts and features like clusters, nodes, indexes, documents, shards, replicas, and building search-based applications. It also discusses using Elasticsearch for big data, different search capabilities, and text analysis.
Dashboards are fantastic, but how do I get notified of critical events? This webinar will cover how to create alerts that will allow your team to effectively monitor business-critical events. Alert channels include email or webhooks into Slack, PagerDuty, DataDog, ServiceNow, or any other webhook you want to develop. What about running custom scripts triggered from alerts? Let's do it.
Sumo Logic exposes the Search Job API for access to resources and log data from third-party scripts and applications.
Targeting experienced Sumo Administrators, this webinar shows you how to leverage the Search Job API to interact with the Sumo Logic service. Everyone attending should be familiar with the concepts of RESTful web services and JSON.
QuickStart your Sumo Logic service with this exclusive webinar. At these monthly live events you will learn how to capitalize on critical capabilities that can amplify your log analytics and monitoring experience while providing you with meaningful business and IT insights.
Video: https://www.sumologic.com/online-training/#QuickStart
So you've got the search and parsing basics down? Ready to learn more advanced operators? Join us and learn about:
LogReduce, LogCompare, Outlier, Predict, Join, Transaction and many more.
Bring your Graphite-compatible metrics into Sumo LogicSumo Logic
If you use open source Graphite software to monitor mission critical applications, you know well the challenges in running, managing and scaling Graphite. Graphite may be ok to get started, but it creates lots of cost and complexity and total-cost-of-ownership headaches as your environment scales.
Sumo Logic provides the industry’s first machine data analytics platform to natively ingest, index and analyze metrics and log data together in real-time.
In this webinar, we will show a live demo of how to:
Ingest graphite compatible metrics into the Sumo Logic service
Analyze and dashboard the metrics to get real-time real-time insights
Correlate Graphite metrics and logs to troubleshoot issues faster
See how easy it is to migrate from graphite to Sumo Logic.
Sumo Logic exposes the Search Job API for access to resources and log data from third-party scripts and applications.
Targeting experienced Sumo Administrators, this webinar shows you how to leverage the Search Job API to interact with the Sumo Logic service. Everyone attending should be familiar with the concepts of RESTful web services and JSON. Through theory and demo, this webinar covers:
Creating a Search Job
Checking Status of a Search Job
Paging through messages and records
How to Webinar: Monitoring through AlertsSumo Logic
How do I get notified of critical events? This webinar will cover how to create alerts that will allow your team to effectively monitor business-critical events. Alert channels include email or webhooks into Slack, PagerDuty, DataDog, ServiceNow, or any other webhook you want to develop. What about running custom scripts triggered from alerts? Let's do it.
Introduction to LogCompare - Reducing MTTI/MTTR with EaseSumo Logic
For users looking to monitor the performance of their code within any given application, log data can provide valuable insights that can help to reduce MTTI/MTTR and speed up the troubleshooting process. In this presentation, we detail how Sumo Logic's LogCompare feature allows users to quickly and easily compare baseline patterns and troubleshoot issues much more quickly.
How Netskope Mastered DevOps with Sumo LogicSumo Logic
This webinar discusses how the leader in cloud app analytics and policy enforcement uses Sumo Logic to ensure optimal performance, availability and security of their cloud platform.
Sumo Logic Co-Founder & VP of Engineering, Kumar Saurabh, joins Netskope VP of Engineering, Abhay Kulkarni, to run a LIVE demo and discusses how Netskope:
- Was able to set up the Sumo Logic service within a single day in various data centers across the world
- Rapidly identifies and troubleshoots issues across 100’s of servers and virtual machines
- Leverages real-time alerts to fix issues to deliver a reliable service
- Makes informed business decisions by analyzing core user behaviors
- Uses out-of-the box applications such as Ngnix and Apache
Marcel Kornacker, Software Enginner at Cloudera - "Data modeling for data sci...Dataconomy Media
The document discusses how relational databases are optimized for flat schemas but much real-world data uses complex schemas. It advocates for using intentional complex schemas to simplify analytic workloads. It describes how SQL engines like Impala can handle complex schemas through extensions like supporting nested data types of struct, map, and array to allow full SQL expressiveness over nested data. Columnar storage is also important for efficiently processing complex schemas.
Sumo Logic Webinar: Visibility into your Host MetricsSumo Logic
This document summarizes a Sumo Logic webinar on ingesting and querying host metrics. The webinar covers installing Sumo Logic collectors to ingest host metrics, querying metrics and building dashboards, using the out-of-the-box Host Metrics app, understanding CloudWatch and Graphite protocols, and the metrics feature roadmap. It provides an overview of Sumo Logic's data flow and how customers can currently get custom metrics via Grafana, StatsD, and CollectD integration.
QuickStart your Sumo Logic service with this exclusive webinar. At these monthly live events you will learn how to capitalize on critical capabilities that can amplify your log analytics and monitoring experience while providing you with meaningful business and IT insights.
https://www.sumologic.com/online-training/#start
QuickStart your Sumo Logic service with this exclusive webinar. At these monthly live events you will learn how to capitalize on critical capabilities that can amplify your log analytics and monitoring experience while providing you with meaningful business and IT insights.
QuickStart your Sumo Logic service with this exclusive webinar. At these monthly live events you will learn how to capitalize on critical capabilities that can amplify your log analytics and monitoring experience while providing you with meaningful business and IT insights.
Video: https://www.sumologic.com/online-training/#start
How Hudl and Cloud Cruiser Leverage Sumo Logic's Unified Logs and MetricsSumo Logic
In this presentation, you will learn how Hudl, a leading software company revolutionizing the way coaches and athletes prepare for and stay ahead of the competition, and Cloud Cruiser, a cloud-based financial management analytics software provider, are leveraging the Sumo Logic's Unified Logs and Metrics platform to improve application health and management. We cover:
- The current challenges in managing application and infrastructure with disparate log and metrics tools at these leading IT organizations
- The benefits of adopting a unified log and metrics analytics solution
- Best practices in improving application and infrastructure availability and performance
Sumo Logic QuickStart Webinar - Dec 2016Sumo Logic
QuickStart your Sumo Logic service with this exclusive webinar. At these monthly live events you will learn how to capitalize on critical capabilities that can amplify your log analytics and monitoring experience while providing you with meaningful business and IT insights.
Video: https://www.sumologic.com/online-training/#start
Standing Up an Effective Enterprise Data Hub -- Technology and BeyondCloudera, Inc.
Federal organizations increasingly are focused on creating environments that enable more data-driven decisions. Yet ensuring that all data is considered and is current, complete, and accurate is a tall order for most. To make data analytics meaningful to support real-world transformation, agency staff need business tools that provide user-friendly dashboards, on-demand reporting, and methods to manage efficiently the rise of voluminous and varied data sets and types commonly associated with big data. In most cases, existing systems are insufficient to support these requirements. Enter the enterprise data hub (EDH), a software architecture specifically designed to be a unified platform that can economically store unlimited data and enable diverse access to it at scale. Plan to attend this discussion to understand the key considerations to making an EDH the architectural center of your agency’s modern data strategy.
The Future of Data Management: The Enterprise Data HubCloudera, Inc.
The document discusses the future of data management through the use of an enterprise data hub (EDH). It notes that an EDH provides a centralized platform for ingesting, storing, exploring, processing, analyzing and serving diverse data from across an organization on a large scale in a cost effective manner. This approach overcomes limitations of traditional data silos and enables new analytic capabilities.
How to Reduce your MTTI/MTTR with a Single ClickSumo Logic
Learn how Sumo Logic enables users to drastically reduce MTTI and MTTR with a single click. In this deck, we cover:
- The power of log analytics for faster troubleshooting and root-cause analysis
- How machine learning & pattern recognition enable faster MTTI and MTTR
- How Sumo Logic's LogReduce and LogCompare features are helping users gain better control of their applications
Enterprise Data Hub: The Next Big Thing in Big DataCloudera, Inc.
If you missed Strata + Hadoop World, you missed quite a bit. This year's event was packed with Big Data practitioners across industries who shared their experiences and how they are driving new innovations like never before. Just because you weren't there, doesn't mean you missed out.
In this session, we'll touch on a few of the key highlights from the show, including:
Key trends in Big Data adoption
The enterprise data hub
How the enterprise data hub is used in practice
Get Certified as a Sumo Power User!
Video: Video: https://www.sumologic.com/online-training/#Start
Designed for users, this series deep-dives into every aspect of analyzing your data. Run as a "how-to" webinar, this session walks viewers through data searching, filtering, parsing, and advanced analytics. This series concludes with "how to"details to create dashboards and alerts to monitor your data and get Sumo Logic to work for you.
Sumo Logic QuickStart Webinar - Jan 2016Sumo Logic
QuickStart your Sumo Logic service with this exclusive webinar. At these monthly live events you will learn how to capitalize on critical capabilities that can amplify your log analytics and monitoring experience while providing you with meaningful business and IT insights
Level 3 Certification: Setting up Sumo Logic - Oct 2018Sumo Logic
Get Certified as a Sumo Power Admin!
Designed for Administrators, this course shows you how to set up your data collection according to your organization’s data sources. Best practices around deployment options ensure you choose a deployment that scales as your organization grows. Because metadata is so important to a healthy environment, learn how to design and set up a naming convention that works best for your teams. Use Chef, Puppet or the likes? Learn how to automate your deployment. Test your deployment with simple searches, and learn to take advantage of optimization tools that can help you stay on top of your deployment.
Webinar: https://www.sumologic.com/online-training/#SettingUpSumo
Designed for Administrators, this course shows you how to set up your data collection according to your organization’s data sources. Best practices around deployment options ensure you choose a deployment that scales as your organization grows. Because metadata is so important to a healthy environment, learn how to design and set up a naming convention that works best for your teams. Use Chef, Puppet or the likes? Learn how to automate your deployment. Test your deployment with simple searches, and learn to take advantage of optimization tools that can help you stay on top of your deployment.
Webinar: https://www.sumologic.com/online-training/#SettingUpSumo
Designed for Administrators, this course shows you how to set up your data collection according to your organization’s data sources. Best practices around deployment options ensure you choose a deployment that scales as your organization grows. Because metadata is so important to a healthy environment, learn how to design and set up a naming convention that works best for your teams. Use Chef, Puppet or the likes? Learn how to automate your deployment. Test your deployment with simple searches, and learn to take advantage of optimization tools that can help you stay on top of your deployment.
Designed for Sumo Administrators, this course shows you how to set up your data collection according to your organization’s data sources. Best practices around deployment options ensure you choose a deployment that scales as your organization grows. Because metadata is so important to a healthy environment, learn how to design and set up a naming convention that works best for your teams. Use Chef, Puppet or the likes? Learn how to automate your deployment. Test your deployment with simple searches, and learn to take advantage of optimization tools that can help you stay on top of your deployment.
Sumo Logic Quickstart Training 10/14/2015Sumo Logic
QuickStart your Sumo Logic service with this exclusive webinar. At these monthly live events you will learn how to capitalize on critical capabilities that can amplify your log analytics and monitoring experience while providing you with meaningful business and IT insights
QuickStart your Sumo Logic service with this exclusive webinar. At these monthly live events you will learn how to capitalize on critical capabilities that can amplify your log analytics and monitoring experience while providing you with meaningful business and IT insights
Designed for Administrators, this course shows you how to set up your data collection according to your organization’s data sources. Best practices around deployment options ensure you choose a deployment that scales as your organization grows. Because metadata is so important to a healthy environment, learn how to design and set up a naming convention that works best for your teams. Use Chef, Puppet or the likes? Learn how to automate your deployment. Test your deployment with simple searches, and learn to take advantage of optimization tools that can help you stay on top of your deployment.
This document discusses anatomy of cloud hacks by analyzing past data breaches and vulnerabilities. It begins by looking at known attacks where compromised infrastructure was based in the cloud. Specific case studies of attacks on Code Spaces, Olindata, and Tesla are described. The document then covers techniques for enumerating cloud services and resources like storage containers. Methods for gaining an initial foothold like leaked credential hunting and exploiting server-side request forgery are also outlined.
Microsoft Sentinel provides cloud-native SIEM and SOAR capabilities powered by AI and automation. It can integrate with various components like servers, cloud servers, network devices, firewalls, and security solutions to provide global visibility of IT security. The implementation includes event analysis, automation of incident response, and creation of dashboards and reports. It also provides log retention, data integrity, fault tolerance, and integration with third-party services and APIs.
Using AWS to Build a Scalable Big Data Management & Processing Service (BDT40...Amazon Web Services
This document summarizes Christian Beedgen's presentation on using AWS to build a scalable machine data analytics service. The presentation covers the architecture of Sumo Logic's service, which ingests machine-generated log data from customers in near real-time and performs analytics. It discusses how the service is built as loosely coupled microservices deployed across AWS with automation. Challenges of scaling such a distributed system are also addressed.
Hack proof your aws cloud cloudcheckr_040416Jarrett Plante
Migrating from the data center to the cloud requires us to rethink much of what we do to secure our applications. The idea of physical security morphs as infrastructure becomes virtualized by AWS APIs. In a new world of ephemeral, auto-scaling infrastructure, you need to adapt your security architecture to meet both compliance and security threats.
In the presentation we will cover topics including:
- Minimizing attack vectors and surface area
- Perimeter assessments of your VPCs
- Internal vs. External threats
- Monitoring threats
- Re-evaluating Intrusion Detection, Activity Monitoring, and Vulnerability Assessment in AWS
This document discusses how to use Azure Sentinel and Microsoft Defender ATP to catch cyber threats. It provides an overview of the Microsoft security ecosystem and capabilities of Azure Sentinel and Defender ATP. Specifically, it outlines how to enable various data sources, design detection rules, and conduct hunting queries using these solutions.
Using AWS To Build A Scalable Machine Data Analytics ServiceChristian Beedgen
Christian Beedgen presented on using AWS to build a scalable machine data analytics service. He discussed Sumo Logic's architecture which uses loosely coupled AWS components like S3, DynamoDB, and EC2 to ingest, index, analyze and query large volumes of machine log data in real-time. Deployment is automated using tools like Jenkins, and components are deployed across availability zones for high availability. The system scales horizontally by sharding data and queries by customer account.
DEF CON 24 - workshop - Craig Young - brainwashing embedded systemsFelipe Prado
Firmware analysis often involves searching firmware images for known file headers and file systems like SquashFS to extract contained files. Automated binary analysis tools like binwalk can help extract files from images. HTTP interfaces are common targets for security testing since they are often exposed without authentication. Testing may uncover vulnerabilities like XSS, CSRF, SQLi or command injection. Wireless interfaces also require testing to check for issues like weak encryption or exposure of credentials in cleartext.
QuickStart your Sumo Logic service with this exclusive webinar. At these monthly live events you will learn how to capitalize on critical capabilities that can amplify your log analytics and monitoring experience while providing you with meaningful business and IT insights.
Deep Dive on Accelerating Content, APIs, and Applications with Amazon CloudFr...Amazon Web Services
This document provides an overview of Amazon CloudFront and Lambda@Edge. It discusses how CloudFront is a global content delivery network that can accelerate content delivery, including both static and dynamic content. It also introduces Lambda@Edge, which allows running code at the edge using AWS Lambda. Lambda@Edge functions can be triggered by CloudFront events to customize content delivery, such as modifying requests and responses. The document provides details on CloudFront pricing and architecture, including how it uses edge locations globally to improve performance.
Webinar here: https://youtu.be/MEmFFwNmLxg
Sumo Logic "How To" Webinar - Monitoring you Data: Alerting on Outliers
Dashboards are fantastic, but how do I get notified of critical events? This webinar will cover how to create alerts that will allow your team to effectively monitor business-critical events. Alert channels include email or webhooks into Slack, PagerDuty, DataDog, ServiceNow, or any other webhook you want to develop. What about running custom scripts triggered from alerts? Let's do it.
This document provides an overview of a Sumo Logic webinar on getting started with Sumo Logic. The webinar covers understanding data collection, searching, parsing and analyzing data, visualizing data through dashboards and alerts, and taking advantage of apps and the content library. It also discusses topics like continuous intelligence, the Sumo Logic data flow, collecting and parsing data, searching and analyzing data, dashboards, alerts, and apps.
Similar to "How to" Webinar: Sending Data to Sumo Logic (20)
This document provides an agenda and overview for a Sumo Logic webinar training session. The agenda includes sections on data collection, search and analysis, and visualizing and monitoring. It discusses Sumo Logic's analytics platform and data flow. It also provides instructions for logging into a training environment and demonstrates examples of searching log data and creating dashboards and alerts.
This document provides instructions for a Sumo Logic welcome webinar, including login information, activities to complete on the Sumo Logic platform, and an overview of advanced analytics functions like geo lookup, outlier detection, prediction, log reduction, log comparison, and creating alerts and dashboards. Participants are guided through exploring sample log data, running log searches, and setting up a dashboard and alert. Contact information is provided for attendees to get additional help or training on using Sumo Logic.
Sumo Logic Cert Jam - Advanced Metrics with KubernetesSumo Logic
This document provides an overview of a training course on using Kubernetes on Sumo Logic. The course teaches participants how to:
1. Discover and explore Kubernetes data and metadata in Sumo Logic, including hands-on labs to identify metadata and search with metadata.
2. Install apps, partner apps, and pre-built dashboards for Kubernetes monitoring.
3. Monitor, troubleshoot, and create alerts using techniques like the Explore tab and custom dashboards.
4. Get certified in Kubernetes on Sumo Logic by taking an exam at the end of the course.
Sumo Logic Cert Jam - Security & ComplianceSumo Logic
This document outlines an agenda for a Sumo Logic Security and Compliance certification course. The agenda includes a presentation and hands-on labs covering topics like building a starter SOC dashboard, exporting dashboards, and compliance. It also includes an introduction to security and compliance and a Sumo Logic certification exam. Hands-on labs will guide students through building dashboards and using features like lookup filters, compliance apps, and integrating threat intelligence from CrowdStrike. The course aims to help students detect, investigate, and respond to security threats in real-time using Sumo Logic's centralized log management platform.
Sumo Logic Cert Jam - Advanced Metrics with KubernetesSumo Logic
This document outlines an agenda for a course to become certified as a Sumo Kubernetes Analyst. The course will provide an introduction to Kubernetes and Sumo Logic's monitoring capabilities, including four different views into Kubernetes systems. Attendees will participate in hands-on labs and have the opportunity to get certified through an online exam.
This document provides an agenda for a Sumo Metrics Analyst certification course. The course covers collecting, analyzing, and monitoring metrics using Sumo Logic. It includes hands-on labs on collecting host and AWS metrics, analyzing metric formats, converting logs to metrics, and creating dashboards and alerts. The course aims to help students master metrics and earn a Sumo Logic certification by passing an online exam at the end.
Sumo Logic Cert Jam - Security AnalyticsSumo Logic
With security threats on the rise, come join our Security and Compliance experts to learn how Sumo Logic’s Threat Intelligence can help you stay on top of your environment by matching IOCs like IP address, domain names, URL, email addresses, MD5 hashes and more, to increase velocity and accuracy of threat detection. Hands on labs help cement the knowledge learned.
Designed for all Sumo users, this series deep-dives into every aspect of analyzing your data. Run as a "how-to" webinar, this session walks viewers through data searching, filtering, parsing, and advanced analytics. This series concludes with "how to"details to create dashboards and alerts to monitor your data and get Sumo Logic to work for you.
Brand new to Sumo Logic? Get started with these 5 easy steps and get certified!
Learn the basics for how to search, parse and analyze the logs and metrics that are important to your organization. This session will guide you through running searches, simple parsing and basic analytics on your data. Learn how to convert your queries to charts and add them to Dashboards to help you visualize trends and easily identify anomalies. Lastly, learn how Alerts can help you stay on top of your critical events.
Sumo Logic Cert Jam - Fundamentals (Spanish)Sumo Logic
Este documento presenta los 5 pasos para convertirse en un usuario Fundamentals certificado de Sumo Logic. Explica cómo Sumo Logic puede ayudar a monitorear datos, buscar y analizar logs, y crear alertas y tableros. También proporciona información sobre cómo tomar el examen de certificación Fundamentals de Sumo Logic.
This document outlines the agenda and logistics for a Sumo Metrics Certified Analyst training course. The course will teach students how to use a unified logs and metrics solution, collect metrics data, analyze metrics using tools and queries, and apply their knowledge through hands-on labs covering common use cases. These include monitoring host metrics, analyzing AWS metrics, working with different metric formats, and converting logs to metrics. Students will learn to visualize metrics using charts and dashboards, and configure metric monitors and alerts. Upon completing the course, students will take an online certification exam to test their mastery of the skills covered.
Security Certification: Security Analytics using Sumo Logic - Oct 2018Sumo Logic
Get Certified as a Sumo Security Power User!
With security threats on the rise, come join our Security and Compliance experts to learn how Sumo Logic’s Threat Intelligence can help you stay on top of your environment by matching IOCs like IP address, domain names, URL, email addresses, MD5 hashes and more, to increase velocity and accuracy of threat detection. Hands on labs help cement the knowledge learned.
Level 2 Certification: Using Sumo Logic - Oct 2018Sumo Logic
This document outlines the curriculum for the Sumo Logic Level 2 Certification. It covers advanced searching, parsing, filtering, and analytics techniques using Sumo Logic. It also covers visualizing and monitoring data through dashboards and alerts. Hands-on labs reinforce these skills. The goal is to help users make Sumo Logic work for them by monitoring trends, critical events, and learning from peer use cases.
Sumo Logic QuickStart Webinar - Get CertifiedSumo Logic
Video: https://www.sumologic.com/online-training/#start
Brand new to Sumo Logic?
Get started with these 5 easy steps. Learn how to capitalize on critical capabilities that can amplify your log analytics and monitoring experience while providing you with meaningful business and IT insights.
You Build It, You Secure It: Introduction to DevSecOpsSumo Logic
In this presentation, DevOps and DevSecOps expert John Willis dives into how to implement DevSecOps, including:
- Why traditional DevOps has shifted and what this shift means
- How DevSecOps can change the game for your team
- Tips and tricks for getting DevSecOps started within your organization
Making the Shift from DevOps to Practical DevSecOps | Sumo Logic WebinarSumo Logic
In this webinar, Sumo Logic VP of Security and Compliance George Gerchow dives into how to make the shift to DevSecOps, discussing how to:
- Incorporate fundamental and high impact security best practices into your current DevOps operations
- Gain visibility into your compliance posture
- Identify potential risks and threats in your environments
Machine Analytics: Correlate Your Logs and MetricsSumo Logic
To effectively manage your application, it’s critical to have visibility into both logs and metrics. Metrics can provide app and infrastructure KPI’s, while logs provide context into application and infrastructure execution KPIs. Managing one without the other, provides you with incomplete data; you need both to troubleshoot application issues quickly and efficiently.
This webinar will feature a live demo of Sumo Logic’s Unified Logs and Metrics machine data analytics platform and show how to:
Natively ingest your logs, host metrics, AWS metrics and Graphite-compatible metrics
Proactively set alerts based on logs and metrics thresholds
Analyze and correlate logs and metrics in real-time and in a unified way to reduce mean time to problem resolution (MTTR)
Scaling Your Tools for Your Modern ApplicationSumo Logic
In this presentation, we discuss Hootsuite - a customer of Sumo Logic and the leading provider of social media management services for enterprises - and their journey off of open source tools to Sumo Logic, including:
- The challenges in running & managing solutions like ELK and Graphite
- Sumo Logic unified logs and metrics monitoring solution and its advanced analytics, dashboarding and troubleshooting capabilities
- How Hootsuite was able to leverage Sumo Logic to deliver excellent user experience to their end customers
E-Invoicing Implementation: A Step-by-Step Guide for Saudi Arabian CompaniesQuickdice ERP
Explore the seamless transition to e-invoicing with this comprehensive guide tailored for Saudi Arabian businesses. Navigate the process effortlessly with step-by-step instructions designed to streamline implementation and enhance efficiency.
DDS Security Version 1.2 was adopted in 2024. This revision strengthens support for long runnings systems adding new cryptographic algorithms, certificate revocation, and hardness against DoS attacks.
UI5con 2024 - Keynote: Latest News about UI5 and it’s EcosystemPeter Muessig
Learn about the latest innovations in and around OpenUI5/SAPUI5: UI5 Tooling, UI5 linter, UI5 Web Components, Web Components Integration, UI5 2.x, UI5 GenAI.
Recording:
https://www.youtube.com/live/MSdGLG2zLy8?si=INxBHTqkwHhxV5Ta&t=0
Introducing Crescat - Event Management Software for Venues, Festivals and Eve...Crescat
Crescat is industry-trusted event management software, built by event professionals for event professionals. Founded in 2017, we have three key products tailored for the live event industry.
Crescat Event for concert promoters and event agencies. Crescat Venue for music venues, conference centers, wedding venues, concert halls and more. And Crescat Festival for festivals, conferences and complex events.
With a wide range of popular features such as event scheduling, shift management, volunteer and crew coordination, artist booking and much more, Crescat is designed for customisation and ease-of-use.
Over 125,000 events have been planned in Crescat and with hundreds of customers of all shapes and sizes, from boutique event agencies through to international concert promoters, Crescat is rigged for success. What's more, we highly value feedback from our users and we are constantly improving our software with updates, new features and improvements.
If you plan events, run a venue or produce festivals and you're looking for ways to make your life easier, then we have a solution for you. Try our software for free or schedule a no-obligation demo with one of our product specialists today at crescat.io
E-commerce Development Services- Hornet DynamicsHornet Dynamics
For any business hoping to succeed in the digital age, having a strong online presence is crucial. We offer Ecommerce Development Services that are customized according to your business requirements and client preferences, enabling you to create a dynamic, safe, and user-friendly online store.
Neo4j - Product Vision and Knowledge Graphs - GraphSummit ParisNeo4j
Dr. Jesús Barrasa, Head of Solutions Architecture for EMEA, Neo4j
Découvrez les dernières innovations de Neo4j, et notamment les dernières intégrations cloud et les améliorations produits qui font de Neo4j un choix essentiel pour les développeurs qui créent des applications avec des données interconnectées et de l’IA générative.
Do you want Software for your Business? Visit Deuglo
Deuglo has top Software Developers in India. They are experts in software development and help design and create custom Software solutions.
Deuglo follows seven steps methods for delivering their services to their customers. They called it the Software development life cycle process (SDLC).
Requirement — Collecting the Requirements is the first Phase in the SSLC process.
Feasibility Study — after completing the requirement process they move to the design phase.
Design — in this phase, they start designing the software.
Coding — when designing is completed, the developers start coding for the software.
Testing — in this phase when the coding of the software is done the testing team will start testing.
Installation — after completion of testing, the application opens to the live server and launches!
Maintenance — after completing the software development, customers start using the software.
Need for Speed: Removing speed bumps from your Symfony projects ⚡️Łukasz Chruściel
No one wants their application to drag like a car stuck in the slow lane! Yet it’s all too common to encounter bumpy, pothole-filled solutions that slow the speed of any application. Symfony apps are not an exception.
In this talk, I will take you for a spin around the performance racetrack. We’ll explore common pitfalls - those hidden potholes on your application that can cause unexpected slowdowns. Learn how to spot these performance bumps early, and more importantly, how to navigate around them to keep your application running at top speed.
We will focus in particular on tuning your engine at the application level, making the right adjustments to ensure that your system responds like a well-oiled, high-performance race car.
Flutter is a popular open source, cross-platform framework developed by Google. In this webinar we'll explore Flutter and its architecture, delve into the Flutter Embedder and Flutter’s Dart language, discover how to leverage Flutter for embedded device development, learn about Automotive Grade Linux (AGL) and its consortium and understand the rationale behind AGL's choice of Flutter for next-gen IVI systems. Don’t miss this opportunity to discover whether Flutter is right for your project.
Zoom is a comprehensive platform designed to connect individuals and teams efficiently. With its user-friendly interface and powerful features, Zoom has become a go-to solution for virtual communication and collaboration. It offers a range of tools, including virtual meetings, team chat, VoIP phone systems, online whiteboards, and AI companions, to streamline workflows and enhance productivity.
Using Query Store in Azure PostgreSQL to Understand Query PerformanceGrant Fritchey
Microsoft has added an excellent new extension in PostgreSQL on their Azure Platform. This session, presented at Posette 2024, covers what Query Store is and the types of information you can get out of it.
Microservice Teams - How the cloud changes the way we workSven Peters
A lot of technical challenges and complexity come with building a cloud-native and distributed architecture. The way we develop backend software has fundamentally changed in the last ten years. Managing a microservices architecture demands a lot of us to ensure observability and operational resiliency. But did you also change the way you run your development teams?
Sven will talk about Atlassian’s journey from a monolith to a multi-tenanted architecture and how it affected the way the engineering teams work. You will learn how we shifted to service ownership, moved to more autonomous teams (and its challenges), and established platform and enablement teams.
8 Best Automated Android App Testing Tool and Framework in 2024.pdfkalichargn70th171
Regarding mobile operating systems, two major players dominate our thoughts: Android and iPhone. With Android leading the market, software development companies are focused on delivering apps compatible with this OS. Ensuring an app's functionality across various Android devices, OS versions, and hardware specifications is critical, making Android app testing essential.
Hand Rolled Applicative User ValidationCode KataPhilip Schwarz
Could you use a simple piece of Scala validation code (granted, a very simplistic one too!) that you can rewrite, now and again, to refresh your basic understanding of Applicative operators <*>, <*, *>?
The goal is not to write perfect code showcasing validation, but rather, to provide a small, rough-and ready exercise to reinforce your muscle-memory.
Despite its grandiose-sounding title, this deck consists of just three slides showing the Scala 3 code to be rewritten whenever the details of the operators begin to fade away.
The code is my rough and ready translation of a Haskell user-validation program found in a book called Finding Success (and Failure) in Haskell - Fall in love with applicative functors.
Takashi Kobayashi and Hironori Washizaki, "SWEBOK Guide and Future of SE Education," First International Symposium on the Future of Software Engineering (FUSE), June 3-6, 2024, Okinawa, Japan
A Study of Variable-Role-based Feature Enrichment in Neural Models of CodeAftab Hussain
Understanding variable roles in code has been found to be helpful by students
in learning programming -- could variable roles help deep neural models in
performing coding tasks? We do an exploratory study.
- These are slides of the talk given at InteNSE'23: The 1st International Workshop on Interpretability and Robustness in Neural Software Engineering, co-located with the 45th International Conference on Software Engineering, ICSE 2023, Melbourne Australia
What is Augmented Reality Image Trackingpavan998932
Augmented Reality (AR) Image Tracking is a technology that enables AR applications to recognize and track images in the real world, overlaying digital content onto them. This enhances the user's interaction with their environment by providing additional information and interactive elements directly tied to physical images.
1. Sumo Logic Confidential
Data Collection
June 2016
How-To Webinar
Welcome.
To give everyone a
chance to successfully
connect, we’ll start at
10:05 AM Pacific.
2. Sumo Logic Confidential
At the completion of this webinar, you will be able to…
Design a Sumo Logic deployment that fits your
organization
Install Collectors
Create your Data Sources
Understand Local File Configuration Management
5. Sumo Logic ConfidentialSumo Logic Confidential
Enterprise Logs are Everywhere
Custom App
Code
Server / OS
Virtual
Databases
Network
Open
Source
Middleware
Content
Delivery
IaaS,
PaaS
SaaS Security
6. Sumo Logic Confidential
Designing Your Deployment
• Sumo Logic Data
Collection is
infinitely flexible.
• Design a Sumo
Logic deployment
that's right for
your organization.
• Installed versus
Hosted Collectors.
7. Sumo Logic Confidential
Host A
Collectors and Sources
Apache Access
Apache Error
Collector
A
Host B
Collector
B
Host C
Collector
C
Apache Access
Apache Error
IIS Logs
IIS W3C Logs
9. Sumo Logic ConfidentialSumo Logic Confidential
Collector and Deployment Options
Collector
Cloud Data
Collection
Centralized
Data
Collection
Local Data
Collection
Collector
CollectorCollector
Collector
Hosted Collectors Installed Collectors
10. Sumo Logic Confidential
Source Types
S3 Bucket
Any data written to S3 buckets via AWS, Lambda
Scripts, custom Apps
HTTPS
Akamai, Log Appender Libraries, etc.
Google
Google API
Typical Scenarios
AWS Only Customers, while it's possible to
rely on Cloud Data Collection entirely, this is
not typical. These source types are normally
just part of the overall collection strategies
Benefits/Drawbacks
+ No Software Installation
- S3 Latency issues
- Https Post Caching Need
Cloud Data Collection
Most Data is generated in the Cloud and by Cloud Services and is collected via Sumo Logics Cloud Integrations.
11. Sumo Logic Confidential
Local Data Collection
The Sumo Logic Collector is installed on all target Hosts and, where possible, sends log data produced on those target Hosts directly to
Sumo Logic Backend via https connection.
Source Types
Local Files
Operating Systems, Middleware, Custom Apps,
etc.
Windows Events
Local Windows Events
Docker
Logs and Stats
Syslog (dedicated Collector)
Network Devices, Snare, etc
Script (dedicated Collector)
Cloud API’s, Database Content, binary data
Typical Scenarios
Customers with large amounts of (similar)
servers, using orchestration/automation,
mostly OS and application logs
- On Premise Datacenters
- Cloud Instances
Benefits/Drawbacks
+ No Hardware Requirement
+ Automation (Chef/Puppet/Scripting)
- Outbound Internet Access Required
- Resource Usage on Target
13. Sumo Logic Confidential
Source Types
Syslog
Operating Systems, Middleware, Custom
Applications, etc
Windows Events
Remote Windows Events
Script
Cloud API’s, Database Content, binary data
Typical Scenarios
Customers with mostly Windows
Environments or existing logging
infrastructure (syslog/logstash)
- On Premise Datacenters
Benefits/Drawbacks
+ No Outbound Internet Access
+ Leverage existing logging Infrastructure
- Scale
- Dedicated Hardware
- Complexity (Failover, syslog rules)
Centralized Data Collection
The Sumo Logic Collector is installed on a set of dedicated machines, these collect log data from the target Hosts via various remote
mechanisms and forward the data to the Sumo Logic Backend. This can be accomplished by either using Sumo Logic syslog source
type or by running Syslog Servers (syslog-ng, rsyslog), write to file, and collect from there.
15. Sumo Logic Confidential
Deployment Options Summary
Collector Benefits Drawbacks
Local
• Direct access to source logs
• Ease of troubleshooting
• No additional HW requirements
• More Complex Management
• Resource usage on target host
• Need for outbound internet access
Centralize
d
• Fewer collectors and sources
• Simplified management
• Target hosts don’t need outbound
internet access
• Need for dedicated hardware
• More complex setup (users, permissions)
• Harder to troubleshoot
• Requires careful planning in order to scale
Hosted
• Agentless
• Build it into your infrastructure (S3)
• Direct HTTP POST
• Requires local script to POST or curl
messages
Resources:
Design Your Deployment
Best Practices: Local and Centralized Data Collection
17. Sumo Logic Confidential
Host A
Collectors and Sources
Apache Access
Apache Error
Collector
A
Host B
Collector
B
Host C
Collector
C
Apache Access
Apache Error
IIS Logs
IIS W3C Logs
18. Sumo Logic ConfidentialSumo Logic Confidential
Defining a Source
A single Collector can have
multiple Sources.
Key fields to define when
configuring any Source type:
• Name
• Description
• Historical Data
• Source Host
• Source Category
• File path
– Excluding syslog
• Timestamp Parsing
19. Sumo Logic ConfidentialSumo Logic Confidential
Source Specific: Remote File
Required for remote collection:
• Listening port
• Remote login credentials
– Username and password
– Local SSH
• Absolute file path
20. Sumo Logic ConfidentialSumo Logic Confidential
Source Specific: Syslog
Required for Syslog collection:
• Protocol
• Listening port
21. Sumo Logic ConfidentialSumo Logic Confidential
Source Specific: Windows Event Collection
Required for Windows Event Collection:
• Remote specific:
– Remote host name(s)
– Windows Domain
– Username / password
• Windows Event Type
22. Sumo Logic ConfidentialSumo Logic Confidential
Source Specific: Windows Performance Collection
Required for Windows Performance Collection:
• Remote specific:
– Remote host name(s)
– Windows Domain
– Username / password
• Frequency
• Perfmon Queries
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Source Specific: Script
Required for script based collection:
• Execution frequency
• Command type
• Path to script
• Script to execute
• Working directory
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Source Specific: HTTP
Required for HTTP Source:
• How to treat incoming POST
requests
After Configuration:
• Use URL to send POST
messages to the collector
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Source Specific: Amazon S3 and AWS sources
Required for Amazon S3:
• IAM
– Key ID
– Security Key
• Bucket name
• Path expression
• Scan interval
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Configuration: Filtering Source Data
• Regular expressions are used to create rules to filter data sent from a Source.
• The filters affect only data sent to Sumo Logic; logs on your end remain intact.
• Filter Types
– Exclude Filter (Black List)
– Include Filter (White List)
– Hash Filter (i.e. Replace credit card number with unique randomly generated code)
– Mask Filter (i.e. Mask each character with #)
– Note
• Exclude filters override all other filter types for a specific value
• Mask and hash filters are applied after exclusion and inclusion filters
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Configuration: Filtering Files (Blacklisting)
• Blacklist files or set of files that shouldn’t be ingested
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Metadata Fields
Name Description
_collector Name of the collector this data came from
_source Name of the source this data came through
_sourceHost Hostname of the server this data came from
_sourceName Name of the log file (including path)
_sourceCategory Category designation of source data
Tags added to your messages when data is collected
Host A
Apache Access
Apache Error
Collector
A
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Host A
Metadata Field Usage
Apache Access
_sourceCategory =
WS/Apache/Access
Apache Error
_sourceCategory =
WS/Apache/Error
Collector
A
Host B
Collector
B
Host C
Collector
C
Apache Access
_sourceCategory =
WS/Apache/Access
Apache Error
_sourceCategory =
WS/Apache/Error
IIS Logs
_sourceCategory =
WS/IIS
IIS W3C Logs
_sourceCategory =
WS/IIS/W3C
Sample Searches for
_sourceCategory:
= WS/Apache/Access
= WS/Apache/*
= WS/*
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Source Category Best Practices
• Recommended nomenclature for Source Categories
Component1/Component2/Component3…
• From least descriptive to most descriptive
Networking/Firewall/Cisco/FWSM
Networking/Firewall/Cisco/ASA
Networking/Firewall/PAN/PA7050
Networking/Router/Cisco/2821
• Note: Not all types of logs need to have the same amount of levels.
• Benefits
– Simple search scoping by using wild cards anywhere in the string
– Simple, intuitive and self-maintaining partitions/index
– Simple and self maintaining RBAC rules
• Blog Post: Good SourceCategory, Bad SourceCategory
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Automating Deployments
• Silent installation
Use sumo.conf
Provide name, credentials and source file parameter for initial setup only
• Local Configuration Collector Management
Manage configuration locally using a JSON file with Chef/Puppet
Available for both new and existing collectors
• Collector Management API
Define an initial Source configuration for your Collectors using a JSON file
Retrieve and update Collector Configuration from an HTTP endpoint
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Installed Collector Deployment Tips
• Install using Collector Guidelines/Requrements
• Access Keys
– Used for collector registration and API
– ID/Key Pair instead of user/pass
• Especially important when storing credentials on disk
• Collector Logs
– Logs in: $SUMO_HOME/logs
– Current Log: $SUMO_HOME/logs/collector.log
– Check for Out of Memory Errors
– Increase memory if needed as described on Support Site Post
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Questions?
Additional Resources
Search Video Library and Documentation
Search/Post to Community Forums
Search, post, respond
Submit/vote for feature requests
Submit Tips & Tricks
Open a Support Case
Sumo Logic Services
Customer Success, Professional Services,
Training
Welcome everyone. My name is….
I’m joined by Maisie and Ryan who are part of our Engineering team that works on Data Collection. They will be fielding questions at the end of this webinar’s Q&A session.
Housekeeping items:
Everyone is on mute to avoid distractions
If you want to ask a question, please do so using the GTW question panel
This webinar will be recorded and shared with all of you, along with the slides
Please note that this webinar is specifically for users with Admin priviledges who have access to install and manage Collectors.
At the completion of this webinar, you will be able to…
Sumo Logic Data Flow is broken into 3 main areas:
Data Collection through configurable Collectors and Sources. Collectors collect, compress, cache and encrypt the data for secure transfer.
Search and Analyze – Users can run searches and correlate events in real-time across the entire application stack. We will be spending most of our time in this area during this webinar, as this is most likely what you will first be doing as a new user.
Visualize and Monitor- Users have the ability to create custom dashboards to help you easily monitor your data in real-time. Custom alerts notify you when specific events are identified across your stack.
I will cover Data Collection at a high-level, and cover the next 2 areas through a demo.
What data can we ingest?
We can ingest data from just about any source you can imagine - structured or unstructured. Here are just a few of the devices, applications and frameworks you may be using - all of which produce log data that SL can analyze.
The left hands side can present you technology stack – from custom application code all the way down to your network devices.
Sumo Logic Installed and Hosted Collectors are infinitely flexible.
Design a Sumo Logic deployment that's right for your organization.
<Review slide citing some examples>
At a High-level, Customers collect and send data to Sumo Logic through the use of Collectors and Sources. We’ll cover collectors first and then dive into Sources.
This is an great example what we see at a typical customer. This customer is sending web server log files to the Sumo Logic service.
Host A and Host B are each sending a couple of log files through a locally installed Sumo Logic collector.
In the case of Host C, which is sending IIS log files, it’s using a hosted collector where a local script can send data to an HTTP endpoint (running curl and POST commands).
Hosted Collectors
Allow for seamless collection from Amazon S3 buckets and HTTP Sources.
Hosted Collectors don't require installation or activation, nor do Hosted Collectors have physical requirements, since they're hosted in AWS.
Because there are no performance issues to consider, you can configure as many S3 and HTTP Sources as you'd like for a single Hosted Collector.
Installed Collectors
Sumo Logic Installed Collectors are lightweight and efficient. You can choose to install a small number of Collectors to minimize maintenance or just because you want to keep your topology simple (Centralized).
Alternatively, you can choose to install many Collectors on many machines (Local) to distribute the bandwidth impact across your network.
Installed Collectors are deployed in your environment, either on a local machine, a machine in your organization, or even an Amazon Machine Image (AMI).
Installed Collectors require a software download and installation. Upgrades to Collector software are released regularly.
A few things to consider:
Consider having an Installed Collector on a dedicated machine if:
You are running a very high-bandwidth network with high logging levels.
You want a central collection point for many Sources.
Consider having more than one Installed Collector if:
You expect the combined number of files coming into one Collector to exceed 500.
Your hardware has memory or CPU limitations.
You expect combined logging traffic for one Collector to be higher than 15,000 events per second.
Your network clusters or regions are geographically separated.
You prefer to install many Collectors, for example, one per machine to collect local files.
IMPORTANT: For system requirement details, see Installed Collector Requirements.
The Sumo Logic Collector is installed on all target Hosts and, where possible, sends log data produced on those target Hosts directly to Sumo Logic Backend via https connection.
The Sumo Logic Collector is installed on all target Hosts and, where possible, sends log data produced on those target Hosts directly to Sumo Logic Backend via https connection.
The Sumo Logic Collector is installed on a set of dedicated machines, these collect log data from the target Hosts via various remote mechanisms and forward the data to the Sumo Logic Backend. This can be accomplished by either using Sumo Logic syslog source type or by running Syslog Servers (syslog-ng, rsyslog), write to file, and collect from there.
The Sumo Logic Collector is installed on a set of dedicated machines, these collect log data from the target Hosts via various remote mechanisms and forward the data to the Sumo Logic Backend. This can be accomplished by either using Sumo Logic syslog source type or by running Syslog Servers (syslog-ng, rsyslog), write to file, and collect from there.
In most cases our customers will employ a mix of the above options to account for different limitations on both the log types and source types. For example, network devices only broadcast syslog, so even if you generally employ local file collection paradigm, you still need some syslog infrastructure to collect these logs. Same is true for any Cloud API logs (e.g. Okta Event, etc) you may want to collect via script.
Another example: an AWS-only customer, will most likely still choose to install collectors on all their EC2 instances (local collection) and collect AWS Audit logs (CloudTrail, ELB, etc) via the S3 integration.
Which strategy you choose does not depend primarily on where the data lives, but on the following:
sensitivity in terms of outbound internet access,
technical abilities in your team (setting up centralized infrastructure requires knowledge, hardware and a need for monitoring/scaling/fault tolerance)
Whether or not there is a logging infrastructure already in place.
At a high-level, we only recommend the Centralize method if the following are true:
- You absolutely cannot live with the internet access requirements
- You have an existing infrastructure (syslog/logstash)
- Your data volume or your number of target hosts is pretty large
At a High-level, Customers collect and send data to Sumo Logic through the use of Collectors and Sources. We’ll cover collectors first and then dive into Sources.
This is an great example what we see at a typical customer. This customer is sending web server log files to the Sumo Logic service.
Host A and Host B are each sending a couple of log files through a locally installed Sumo Logic collector.
In the case of Host C, which is sending IIS log files, it’s using a hosted collector where a local script can send data to an HTTP endpoint (running curl and POST commands). Hosted collectors are also able to load data from AWS S3 buckets.
Name: something that is relevant to the data you are collecting
Description: reference to understand the source
Source Category; custom label that you can easily use to search data gathered by this source
Timestamp
Host
File Path or Source (Name)
Source Specific Config
Local/Remote Path
script/ File/Windows
Name: something that is relevant to the data you are collecting
Description: reference to understand the source
Source Category; custom label that you can easily use to search data gathered by this source
Timestamp
Host
Hosted:
S3: path expression allows you to identify which objects to upload from S3 can use wildcard to define the path expression and capture more files. Exact file name will only pick up files that match that
Great, data is ingested into the Sumo Logic service, but something else is also happening in the background.
Every single message ingested gets tagged with metadata that makes it much easier to search for related messages.
This table shows the 5 main tags (review them all)
In particular, I want to point out the source Category metadata field, as choosing the right naming convention can make a big impact on your searching capabilities and performance.
This example will highlight the importance of defining the proper source category:
Notice I’ve added the desired SourceCategory for each Source:
= WS/Apache/Access
Searches across Apache Security logs in both Host A and Host B
= WS/Apache/*
Searches across all Apache sources in both Host A and Host B
= WS/*
Searches across all Web Servers across all hosts