Axibase Time-Series Database (ATSD) is a purpose-built solution for analyzing and reporting on massive volumes of time-series data collected at high frequency.
Enhanced Data Visualization provided for 200,000 Machines with OpenTSDB and C...YASH Technologies
YASH tuned applications and databases to maximize system performance, distributed the storage of monitored data, and eliminated destructive down-sampling.
Enhanced Data Visualization provided for 200,000 Machines with OpenTSDB and ...YASH Technologies
During the implementation of OpenTSDB, YASH tuned applications and databases to maximize system performance,
distributed the storage of monitored data, and eliminated destructive down-sampling.
Time Series data is proliferating with literally every step that we take, just think about things like Fit Bit bracelets that track your every move and financial trading data all of which is timestamped.
Time series data requires high performance reads and writes even with a huge number of data sources. Both speed and scale are integral to success, which makes for a unique challenge for your database.
A time series NoSQL data model requires flexibility to support unstructured, and semi-structured data as well as the ability to write range queries to analyze your time series data. So how can you tackle speed, scale and flexibility all at once?
Join Professional Services Architect Drew Kerrigan and Developer Advocate Matt Brender for a discussion of:
Examples of time series data sets, from IoT to Finance to jet engines
What makes time series queries different from other database queries
How to model your dataset to answer the right questions about your data
How to store, query and analyze a set of time series data points
Learn how a NoSQL database model and Riak TS can help you address the unique challenges of time series data.
Using druid for interactive count distinct queries at scaleItai Yaffe
At NMC (Nielsen Marketing Cloud) we need to present to our clients the number of unique users who meet a given criteria. The condition is typically a set-theoretic expression over a stream of events for a given time range. Historically, we have used ElasticSearch to answer these types of questions, however, we have encountered major scaling issues. In this presentation we will detail the journey of researching, benchmarking and productionizing a new technology, Druid, with DataSketches, to overcome the limitations we were facing
Transforming and Scaling Large Scale Data Analytics: Moving to a Cloud-based ...DataWorks Summit
The Census Bureau is the U.S. government's largest statistical agency with a mission to provide current facts and figures about America's people, places and economy. The Bureau operates a large number of surveys to collect this data, the most well known being the decennial population census. Data is being collected in increasing volumes and the analytics solutions must be able to scale to meet the ever increasing needs while maintaining the confidentiality of the data. Past data analytics have occurred in processing silos inhibiting the sharing of information and common reference data is replicated across multiple system. The use of the Hortonworks Data Platform, Hortonworks Data Flow and other open-source technologies is enabling the creation of a cloud-based enterprise data lake and analytics platform. Cloud object stores are used to provide scalable data storage and cloud compute supports permanent and transient clusters. Data governance tools are used to track the data lineage and to provide access controls to sensitive data.
Enhanced Data Visualization provided for 200,000 Machines with OpenTSDB and C...YASH Technologies
YASH tuned applications and databases to maximize system performance, distributed the storage of monitored data, and eliminated destructive down-sampling.
Enhanced Data Visualization provided for 200,000 Machines with OpenTSDB and ...YASH Technologies
During the implementation of OpenTSDB, YASH tuned applications and databases to maximize system performance,
distributed the storage of monitored data, and eliminated destructive down-sampling.
Time Series data is proliferating with literally every step that we take, just think about things like Fit Bit bracelets that track your every move and financial trading data all of which is timestamped.
Time series data requires high performance reads and writes even with a huge number of data sources. Both speed and scale are integral to success, which makes for a unique challenge for your database.
A time series NoSQL data model requires flexibility to support unstructured, and semi-structured data as well as the ability to write range queries to analyze your time series data. So how can you tackle speed, scale and flexibility all at once?
Join Professional Services Architect Drew Kerrigan and Developer Advocate Matt Brender for a discussion of:
Examples of time series data sets, from IoT to Finance to jet engines
What makes time series queries different from other database queries
How to model your dataset to answer the right questions about your data
How to store, query and analyze a set of time series data points
Learn how a NoSQL database model and Riak TS can help you address the unique challenges of time series data.
Using druid for interactive count distinct queries at scaleItai Yaffe
At NMC (Nielsen Marketing Cloud) we need to present to our clients the number of unique users who meet a given criteria. The condition is typically a set-theoretic expression over a stream of events for a given time range. Historically, we have used ElasticSearch to answer these types of questions, however, we have encountered major scaling issues. In this presentation we will detail the journey of researching, benchmarking and productionizing a new technology, Druid, with DataSketches, to overcome the limitations we were facing
Transforming and Scaling Large Scale Data Analytics: Moving to a Cloud-based ...DataWorks Summit
The Census Bureau is the U.S. government's largest statistical agency with a mission to provide current facts and figures about America's people, places and economy. The Bureau operates a large number of surveys to collect this data, the most well known being the decennial population census. Data is being collected in increasing volumes and the analytics solutions must be able to scale to meet the ever increasing needs while maintaining the confidentiality of the data. Past data analytics have occurred in processing silos inhibiting the sharing of information and common reference data is replicated across multiple system. The use of the Hortonworks Data Platform, Hortonworks Data Flow and other open-source technologies is enabling the creation of a cloud-based enterprise data lake and analytics platform. Cloud object stores are used to provide scalable data storage and cloud compute supports permanent and transient clusters. Data governance tools are used to track the data lineage and to provide access controls to sensitive data.
A Study Review of Common Big Data Architecture for Small-Medium EnterpriseRidwan Fadjar
This slide was created to present the result of my paper about "A Study Review of Common Big Data Architecture for Small-Medium Enterprise" at MSCEIS FPMIPA Universitas Pendidikan Indonesia 2019.
In cooperate with: https://www.linkedin.com/in/faijinali and https://www.linkedin.com/in/fajriabdillah
codecentric AG: CQRS and Event Sourcing Applications with CassandraDataStax Academy
CQRS (Command Query Responsibility Segregation) is a pattern, which separates the process of querying and updating data. As a query only returns data without any side effects, a command is designed to change data. CQRS is often combined with Event Sourcing. This is an architecture in which all changes to an application state are stored as a sequence of events.
Because of its great capability to store time series data Cassandra is the perfect fit for implementing the event store. But there a still a lot of open questions: What about the data modeling? What techniques will be used to process and store data in the Cassandra database? How to access the current state of the application, without replaying every event? And what about failure handling?
In this talk, I will give a brief introduction to CQRS and the Event Sourcing pattern and will then answer the questions above using a real life example of a data store for customer data.
"Quantum Clustering - Physics Inspired Clustering Algorithm", Sigalit Bechler...Dataconomy Media
"Quantum Clustering - Physics Inspired Clustering Algorithm", Sigalit Bechler, Researcher at Similar Web
Watch more from Data Natives Berlin 2016 here: http://bit.ly/2fE1sEo
Visit the conference website to learn more: www.datanatives.io
Follow Data Natives:
https://www.facebook.com/DataNatives
https://twitter.com/DataNativesConf
Stay Connected to Data Natives by Email: Subscribe to our newsletter to get the news first about Data Natives 2017: http://bit.ly/1WMJAqS
About the Author:
Sigalit Bechler is a data science researcher with a diverse academic background - a B.Sc. in electrical engineering, a B.Sc. in physics (cum laude) from Tel Aviv University's prestigious program for parallel B.Sc. in Physics and in Electrical Engineering, an M.Sc. in condensed matter (cum laude), and have started her Ph.D. in bioinformatics. Prior to her M.Sc. I have served as a captain in a technology unit of the IDF. She is passionate about science and solving complex big data problems that require out of the box thinking, and like to dive deep into the details. She always take a positive, proactive approach, and put an emphasis on understanding the big picture as well.
In this session we will take a look at Azure Data Lake from an administrator's perspective.
Do you know who has what access where? How much data is in your data lake? What about the accesses to the data lake, is everything running normally?
In this session we will show you what possibilities the portal offers you to keep an eye on the Azure Data Lake. In addition, we will show you further scripts and tools to perform the corresponding tasks.
Dive with us into the depths of your Data Lake.
Proofpoint: Fraud Detection and Security on Social MediaDataStax Academy
Social media has become the new frontier for cyber-attackers. The explosive growth of this new communications platform, combined with the potential to reach millions of people through a single post, has provided a low barrier for exploitation. In this talk, we will focus on how Cassandra is used to enable our fight against bad actors on social media. In particular, we will discuss how we use Cassandra for anomaly detection, social mob alerting, trending topics, and fraudulent classification. We will also speak about our Cassandra data models, integration with Spark Streaming, and how we use KairosDB for our time series data. Watch us don our superhero-Cassandra capes as we fight against the bad guys!
Bigdata Hadoop project payment gateway domainKamal A
Live Hadoop project in payment gateway domain for people seeking real time work experience in bigdata domain. Email: Onlinetraining2011@gmail.com ,
Skypeid: onlinetraining2011
My profile: www.linkedin.com/pub/kamal-a/65/2b2/2b5
Can My Inventory Survive Eventual Consistency?DataStax
Let’s explore an inventory use case and discover how it’s possible to use an eventually consistent data store like Cassandra / DSE to support scalability, consistency, continuous availability. Combine that with analytics and I’ll show you how to build an inventory system for the future.
Cloudian HyperStore offer 100% S3 compatibility for low-cost, scalable smart object storage.
With HyperStore 6.0, we are focused on bringing down operational costs so that you can more effectively track, manage, and optimize your data storage as you scale.
Breakout: Hadoop and the Operational Data StoreCloudera, Inc.
As disparate data volumes continue to be operationalized across the enterprise, data will need to be processed, cleansed, transformed, and made available to end users at greater speeds. Traditional ODS systems run into issues when trying to process large data volumes causing operations to be backed up, data to be archived, and ETL/ ELT processes to fail. Join this breakout to learn how to battle these issues.
Power Your Delta Lake with Streaming Transactional ChangesDatabricks
Organizations are adopting data digitization and data-driven decision making is at the heart of this transformation. Cloud Data Lakes and Datawarehouses provide great flexibility to proto-type and roll out applications continuously at much lower costs.
Transactional databases are optimized for processing huge volumes of transactions in real-time, whereas the cloud data lake needs to be optimized for analyzing huge volumes of data quickly. This brings about a challenge in creating a streamlined data flow process from capturing realtime transactions into a cloud datawarehouse to drive realtime insights in a scalable and cost effective manner.
In this session, we’ll show how organizations can easily overcome that challenge by adopting a robust platform with StreamSets and Delta Lake. StreamSets provides a no-code framework to automate ingestion of transactional data and data processing on Spark, while Delta Lake provides ACID transactions, scalable metadata handling, and unifies streaming and batch data processing.
Learn more about Cloudian HyperStore's various features and benefits, including 100% S3 compatibility, multi-tenancy, data protection, and proactive data rebuilding.
Microsoft R enable enterprise-wide, scalable experimental data science and operational machine learning, by providing a collection of servers and tools that extend the capabilities of open-source R In these slides, we give a quick introduction to Microsoft R Server architecture, and a comprehensive overview of ScaleR, the core libraries to Microsoft R, that enables parallel execution and use external data frames (xdfs). A tutorial-like presentation covering how to: 1) setup the environments, 2) read data, 3) process & transform, 4) analyse, summarize, visualize, 5) learn & predict, and finally 6) deploy and consume (using msrdeploy).
4Developers 2018: Przetwarzanie Big Data w oparciu o architekturę Lambda na p...PROIDEA
Według szacunków do 2020 roku wygenerujmy 40 Zetta byte’ów, a do roku 2025 aż 163 Zetta byte’ów różnego rodzaju danych, a ich dokładna analiza ACpozwali na odkrywanie nowych zjawisk, optymalizacje procesów, czy wspomaganie procesów decyzyjnych. Aby efektywnie przetwarzać tak duże zbiory danych potrzebujemy nowych technik analizy danych oraz innowacyjnych rozwiązań technologicznych. Ważną role pełni tutaj chmura Azure, która oferuje szereg usług, przy użyciu których możemy tworzyć rozwiązania na potrzeby przetwarzania Big Data zarówno w trybie batch’owych jak i ‘near real time’. Podczas sesji stworzymy przykładowe rozwiązanie przetwarzania Big Data oparte o architekturę Lambda , z wykorzystaniem usług platformy Azure, takich jak Azure Data Factory, Azure Stream Analytics, Azure HdInsight, Azure Event (IoT) Hub, czy Azure Data Lake.
IBM Informix - The Ideal Database for Internet of Things
Exclusive luncheon at IBM World of Watson 2016. Informix is the best fit for IoT sensor data analytics at the edge and in the cloud.
A Study Review of Common Big Data Architecture for Small-Medium EnterpriseRidwan Fadjar
This slide was created to present the result of my paper about "A Study Review of Common Big Data Architecture for Small-Medium Enterprise" at MSCEIS FPMIPA Universitas Pendidikan Indonesia 2019.
In cooperate with: https://www.linkedin.com/in/faijinali and https://www.linkedin.com/in/fajriabdillah
codecentric AG: CQRS and Event Sourcing Applications with CassandraDataStax Academy
CQRS (Command Query Responsibility Segregation) is a pattern, which separates the process of querying and updating data. As a query only returns data without any side effects, a command is designed to change data. CQRS is often combined with Event Sourcing. This is an architecture in which all changes to an application state are stored as a sequence of events.
Because of its great capability to store time series data Cassandra is the perfect fit for implementing the event store. But there a still a lot of open questions: What about the data modeling? What techniques will be used to process and store data in the Cassandra database? How to access the current state of the application, without replaying every event? And what about failure handling?
In this talk, I will give a brief introduction to CQRS and the Event Sourcing pattern and will then answer the questions above using a real life example of a data store for customer data.
"Quantum Clustering - Physics Inspired Clustering Algorithm", Sigalit Bechler...Dataconomy Media
"Quantum Clustering - Physics Inspired Clustering Algorithm", Sigalit Bechler, Researcher at Similar Web
Watch more from Data Natives Berlin 2016 here: http://bit.ly/2fE1sEo
Visit the conference website to learn more: www.datanatives.io
Follow Data Natives:
https://www.facebook.com/DataNatives
https://twitter.com/DataNativesConf
Stay Connected to Data Natives by Email: Subscribe to our newsletter to get the news first about Data Natives 2017: http://bit.ly/1WMJAqS
About the Author:
Sigalit Bechler is a data science researcher with a diverse academic background - a B.Sc. in electrical engineering, a B.Sc. in physics (cum laude) from Tel Aviv University's prestigious program for parallel B.Sc. in Physics and in Electrical Engineering, an M.Sc. in condensed matter (cum laude), and have started her Ph.D. in bioinformatics. Prior to her M.Sc. I have served as a captain in a technology unit of the IDF. She is passionate about science and solving complex big data problems that require out of the box thinking, and like to dive deep into the details. She always take a positive, proactive approach, and put an emphasis on understanding the big picture as well.
In this session we will take a look at Azure Data Lake from an administrator's perspective.
Do you know who has what access where? How much data is in your data lake? What about the accesses to the data lake, is everything running normally?
In this session we will show you what possibilities the portal offers you to keep an eye on the Azure Data Lake. In addition, we will show you further scripts and tools to perform the corresponding tasks.
Dive with us into the depths of your Data Lake.
Proofpoint: Fraud Detection and Security on Social MediaDataStax Academy
Social media has become the new frontier for cyber-attackers. The explosive growth of this new communications platform, combined with the potential to reach millions of people through a single post, has provided a low barrier for exploitation. In this talk, we will focus on how Cassandra is used to enable our fight against bad actors on social media. In particular, we will discuss how we use Cassandra for anomaly detection, social mob alerting, trending topics, and fraudulent classification. We will also speak about our Cassandra data models, integration with Spark Streaming, and how we use KairosDB for our time series data. Watch us don our superhero-Cassandra capes as we fight against the bad guys!
Bigdata Hadoop project payment gateway domainKamal A
Live Hadoop project in payment gateway domain for people seeking real time work experience in bigdata domain. Email: Onlinetraining2011@gmail.com ,
Skypeid: onlinetraining2011
My profile: www.linkedin.com/pub/kamal-a/65/2b2/2b5
Can My Inventory Survive Eventual Consistency?DataStax
Let’s explore an inventory use case and discover how it’s possible to use an eventually consistent data store like Cassandra / DSE to support scalability, consistency, continuous availability. Combine that with analytics and I’ll show you how to build an inventory system for the future.
Cloudian HyperStore offer 100% S3 compatibility for low-cost, scalable smart object storage.
With HyperStore 6.0, we are focused on bringing down operational costs so that you can more effectively track, manage, and optimize your data storage as you scale.
Breakout: Hadoop and the Operational Data StoreCloudera, Inc.
As disparate data volumes continue to be operationalized across the enterprise, data will need to be processed, cleansed, transformed, and made available to end users at greater speeds. Traditional ODS systems run into issues when trying to process large data volumes causing operations to be backed up, data to be archived, and ETL/ ELT processes to fail. Join this breakout to learn how to battle these issues.
Power Your Delta Lake with Streaming Transactional ChangesDatabricks
Organizations are adopting data digitization and data-driven decision making is at the heart of this transformation. Cloud Data Lakes and Datawarehouses provide great flexibility to proto-type and roll out applications continuously at much lower costs.
Transactional databases are optimized for processing huge volumes of transactions in real-time, whereas the cloud data lake needs to be optimized for analyzing huge volumes of data quickly. This brings about a challenge in creating a streamlined data flow process from capturing realtime transactions into a cloud datawarehouse to drive realtime insights in a scalable and cost effective manner.
In this session, we’ll show how organizations can easily overcome that challenge by adopting a robust platform with StreamSets and Delta Lake. StreamSets provides a no-code framework to automate ingestion of transactional data and data processing on Spark, while Delta Lake provides ACID transactions, scalable metadata handling, and unifies streaming and batch data processing.
Learn more about Cloudian HyperStore's various features and benefits, including 100% S3 compatibility, multi-tenancy, data protection, and proactive data rebuilding.
Microsoft R enable enterprise-wide, scalable experimental data science and operational machine learning, by providing a collection of servers and tools that extend the capabilities of open-source R In these slides, we give a quick introduction to Microsoft R Server architecture, and a comprehensive overview of ScaleR, the core libraries to Microsoft R, that enables parallel execution and use external data frames (xdfs). A tutorial-like presentation covering how to: 1) setup the environments, 2) read data, 3) process & transform, 4) analyse, summarize, visualize, 5) learn & predict, and finally 6) deploy and consume (using msrdeploy).
4Developers 2018: Przetwarzanie Big Data w oparciu o architekturę Lambda na p...PROIDEA
Według szacunków do 2020 roku wygenerujmy 40 Zetta byte’ów, a do roku 2025 aż 163 Zetta byte’ów różnego rodzaju danych, a ich dokładna analiza ACpozwali na odkrywanie nowych zjawisk, optymalizacje procesów, czy wspomaganie procesów decyzyjnych. Aby efektywnie przetwarzać tak duże zbiory danych potrzebujemy nowych technik analizy danych oraz innowacyjnych rozwiązań technologicznych. Ważną role pełni tutaj chmura Azure, która oferuje szereg usług, przy użyciu których możemy tworzyć rozwiązania na potrzeby przetwarzania Big Data zarówno w trybie batch’owych jak i ‘near real time’. Podczas sesji stworzymy przykładowe rozwiązanie przetwarzania Big Data oparte o architekturę Lambda , z wykorzystaniem usług platformy Azure, takich jak Azure Data Factory, Azure Stream Analytics, Azure HdInsight, Azure Event (IoT) Hub, czy Azure Data Lake.
IBM Informix - The Ideal Database for Internet of Things
Exclusive luncheon at IBM World of Watson 2016. Informix is the best fit for IoT sensor data analytics at the edge and in the cloud.
Chronix Time Series Database - The New Time Series Kid on the BlockQAware GmbH
Apache Big Data Conference 2016, Vancouver BC: Talk by Florian Lautenschlager (@flolaut, Senior Software Engineer).
Abstract: There is a new open source time series database on the block that allows one to store billions of time series points and access them within a few milliseconds.
Chronix is a young but mature open source time series database that catches a compression rate of 98% compared to data in CSV files while an average query took 21 milliseconds. Chronix is built on top of Apache Solr, a bulletproof NoSQL database with impressive search capabilities. Chronix relies on Solr plugins and everyone who has a Solr running can create a new Chronix core within a few minutes.
In this presentation Florian shows how Chronix achieves its efficiency in both by means of an ideal chunking, by selecting the best compression technique, by enhancing the stored data with pre-computed attributes, and by specialized time series query functions.
This is a run-through at a 200 level of the Microsoft Azure Big Data Analytics for the Cloud data platform based on the Cortana Intelligence Suite offerings.
FSI201 FINRA’s Managed Data Lake – Next Gen Analytics in the CloudAmazon Web Services
FINRA’s Data Lake unlocks the value in its data to accelerate analytics and machine learning at scale. FINRA's Technology group has changed its customer's relationship with data by creating a Managed Data Lake that enables discovery on Petabytes of capital markets data, while saving time and money over traditional analytics solutions. FINRA’s Managed Data Lake includes a centralized data catalog and separates storage from compute, allowing users to query from petabytes of data in seconds. Learn how FINRA uses Spot instances and services such as Amazon S3, Amazon EMR, Amazon Redshift, and AWS Lambda to provide the 'right tool for the right job' at each step in the data processing pipeline. All of this is done while meeting FINRA’s security and compliance responsibilities as a financial regulator.
Thomas Weise, Apache Apex PMC Member and Architect/Co-Founder, DataTorrent - ...Dataconomy Media
Thomas Weise, Apache Apex PMC Member and Architect/Co-Founder of DataTorrent presented "Streaming Analytics with Apache Apex" as part of the Big Data, Berlin v 8.0 meetup organised on the 14th of July 2016 at the WeWork headquarters.
Amazon Kinesis is the AWS service for real-time streaming big data ingestion and processing. This talk gives a detailed exploration of Kinesis stream processing. We'll discuss in detail techniques for building, and scaling Kinesis processing applications, including data filtration and transformation. Finally we'll address tips and techniques to emitting data into S3, DynamoDB, and Redshift.
[db tech showcase Tokyo 2017] C37: MariaDB ColumnStore analytics engine : use...Insight Technology, Inc.
MariaDB ColumnStore is the analytics engine for MariaDB. This talk will introduce the product, use cases, and also introduce the new features coming in the next major release 1.1.
AWS APAC Webinar Week - Real Time Data Processing with KinesisAmazon Web Services
Extracting real-time information from streaming data generated by mobile devices, sensors, and servers used to require distributed systems skills and writing custom code. This presentation will introduce Kinesis Streams and Kinesis Firehose, the AWS services for real-time streaming big data ingestion and processing.
We’ll provide an overview of the key scenarios and business use cases suitable for real-time processing, and how Kinesis can help customers shift from a traditional batch-oriented processing of data to a continual real-time processing model. We’ll explore the key concepts, attributes, APIs and features of the service, and discuss building a Kinesis-enabled application for real-time processing. This talk will also include key lessons learnt, architectural tips and design considerations in working with Kinesis and building real-time processing applications.
In this webinar, we will also provide an overview of Amazon Kinesis Firehose. We will then walk through a demo showing how to create an Amazon Kinesis Firehose delivery stream, send data to the stream, and configure it to load the data automatically into Amazon S3 and Amazon Redshift.
이제 빅데이터란 개념은 익숙한 것이 되었지만 이를 비지니스에 적용하고 최대의 효과를 얻는 방법에 대한 고찰은 여전히 필요합니다. 소중한 데이터를 쉽게 저장 및 분석하고 시각화하는 것은 비즈니스에 대한 통찰을 얻기 위한 중요한 과정입니다.
이 강연에서는 AWS Elastic MapReduce, Amazon Redshift, Amazon Kinesis 등 AWS가 제공하는 다양한 데이터 분석 도구를 활용해 보다 간편하고 빠른 빅데이터 분석 서비스를 구축하는 방법에 대해 소개합니다.
Logging, Metrics, and APM: The Operations TrifectaElasticsearch
Learn how Elasticsearch efficiently combines logs, metrics, and APM data in a single store and see how Kibana is used to search logs, analyze metrics, and leverage APM features for better performance monitoring and faster troubleshooting.
Infrastructure monitoring made easy, from ingest to insightElasticsearch
Visibility into your infrastructure is critical, and the Elastic (ELK) Stack, brings its logging strengths to your metrics use case. Discover how simplified data onboarding with hundreds of prebuilt integrations, automated insights with alerting and machine learning, and new visual tools built for exploring infrastructure metrics are streamlining the monitoring use case.
Supercharging Smart Meter BIG DATA Analytics with Microsoft Azure Cloud- SRP ...Mike Rossi
Explosive growth of Smart Meter (SM) deployments has presented key infrastructure challenges across the utility industry. The huge volumes of smart meter data has led the industry to a tipping point which requires investments in modernizing existing data warehouses. Typical modernization efforts lead to huge capital expenditures for DW appliances and storage. Sizing this new infrastructure is tricky and can lead to underutilized or poorly performing hardware.
The Cloud is the catalyst to solving these Big Data challenges.
Utilizing a Cloud architecture delivers huge benefits by:
Maximizing use of existing architecture
Minimizing new CapEx expenditures
Lowering overall storage costs
Enabling scale on demand
Amazon Kinesis is a fully managed service for real-time processing of streaming data at massive scale. In this webinar, developers will learn how to build and deploy a streaming data processing application with Amazon Kinesis. We will cover the following: - A brief overview of Amazon Kinesis and drill down on key technical concepts. - Amazon Kinesis Client Library capabilities that enable customers to build fault tolerant, continuous processing applications that scale elastically. - The role of the supporting connector library for moving data into stores like S3 and Redshift. - Best practices for streaming data ingestion and processing with Amazon Kinesis.
Speaker: Jay Runkel, Principal Solution Architect, MongoDB
Session Type: 40 minute main track session
Track: Operations
When architecting a MongoDB application, one of the most difficult questions to answer is how much hardware (number of shards, number of replicas, and server specifications) am I going to need for an application. Similarly, when deploying in the cloud, how do you estimate your monthly AWS, Azure, or GCP costs given a description of a new application? While there isn’t a precise formula for mapping application features (e.g., document structure, schema, query volumes) into servers, there are various strategies you can use to estimate the MongoDB cluster sizing. This presentation will cover the questions you need to ask and describe how to use this information to estimate the required cluster size or cloud deployment cost.
What You Will Learn:
- How to architect a sharded cluster that provides the required computing resources while minimizing hardware or cloud computing costs
- How to use this information to estimate the overall cluster requirements for IOPS, RAM, cores, disk space, etc.
- What you need to know about the application to estimate a cluster size
Explore our comprehensive data analysis project presentation on predicting product ad campaign performance. Learn how data-driven insights can optimize your marketing strategies and enhance campaign effectiveness. Perfect for professionals and students looking to understand the power of data analysis in advertising. for more details visit: https://bostoninstituteofanalytics.org/data-science-and-artificial-intelligence/
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Empowering the Data Analytics Ecosystem: A Laser Focus on Value
The data analytics ecosystem thrives when every component functions at its peak, unlocking the true potential of data. Here's a laser focus on key areas for an empowered ecosystem:
1. Democratize Access, Not Data:
Granular Access Controls: Provide users with self-service tools tailored to their specific needs, preventing data overload and misuse.
Data Catalogs: Implement robust data catalogs for easy discovery and understanding of available data sources.
2. Foster Collaboration with Clear Roles:
Data Mesh Architecture: Break down data silos by creating a distributed data ownership model with clear ownership and responsibilities.
Collaborative Workspaces: Utilize interactive platforms where data scientists, analysts, and domain experts can work seamlessly together.
3. Leverage Advanced Analytics Strategically:
AI-powered Automation: Automate repetitive tasks like data cleaning and feature engineering, freeing up data talent for higher-level analysis.
Right-Tool Selection: Strategically choose the most effective advanced analytics techniques (e.g., AI, ML) based on specific business problems.
4. Prioritize Data Quality with Automation:
Automated Data Validation: Implement automated data quality checks to identify and rectify errors at the source, minimizing downstream issues.
Data Lineage Tracking: Track the flow of data throughout the ecosystem, ensuring transparency and facilitating root cause analysis for errors.
5. Cultivate a Data-Driven Mindset:
Metrics-Driven Performance Management: Align KPIs and performance metrics with data-driven insights to ensure actionable decision making.
Data Storytelling Workshops: Equip stakeholders with the skills to translate complex data findings into compelling narratives that drive action.
Benefits of a Precise Ecosystem:
Sharpened Focus: Precise access and clear roles ensure everyone works with the most relevant data, maximizing efficiency.
Actionable Insights: Strategic analytics and automated quality checks lead to more reliable and actionable data insights.
Continuous Improvement: Data-driven performance management fosters a culture of learning and continuous improvement.
Sustainable Growth: Empowered by data, organizations can make informed decisions to drive sustainable growth and innovation.
By focusing on these precise actions, organizations can create an empowered data analytics ecosystem that delivers real value by driving data-driven decisions and maximizing the return on their data investment.
As Europe's leading economic powerhouse and the fourth-largest hashtag#economy globally, Germany stands at the forefront of innovation and industrial might. Renowned for its precision engineering and high-tech sectors, Germany's economic structure is heavily supported by a robust service industry, accounting for approximately 68% of its GDP. This economic clout and strategic geopolitical stance position Germany as a focal point in the global cyber threat landscape.
In the face of escalating global tensions, particularly those emanating from geopolitical disputes with nations like hashtag#Russia and hashtag#China, hashtag#Germany has witnessed a significant uptick in targeted cyber operations. Our analysis indicates a marked increase in hashtag#cyberattack sophistication aimed at critical infrastructure and key industrial sectors. These attacks range from ransomware campaigns to hashtag#AdvancedPersistentThreats (hashtag#APTs), threatening national security and business integrity.
🔑 Key findings include:
🔍 Increased frequency and complexity of cyber threats.
🔍 Escalation of state-sponsored and criminally motivated cyber operations.
🔍 Active dark web exchanges of malicious tools and tactics.
Our comprehensive report delves into these challenges, using a blend of open-source and proprietary data collection techniques. By monitoring activity on critical networks and analyzing attack patterns, our team provides a detailed overview of the threats facing German entities.
This report aims to equip stakeholders across public and private sectors with the knowledge to enhance their defensive strategies, reduce exposure to cyber risks, and reinforce Germany's resilience against cyber threats.
2. Axibase Time Series Database
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Axibase Time-Series Database (ATSD) is a clustered non-relational database for the storage of
various information coming out of the IT infrastructure. ATSD is specifically designed to store and
analyze large amounts of statistical data collected at high frequency.
3. Database History
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• 1970 – IBM introduced relational algebra for data processing.
• Cambrian explosion of relational database management systems:
• 2000 – first large-scale applications emerge, such as Google Search.
• 2004 – Google Big Table – first non-relational database using distributed file system.
• Currently we are experiencing Cambrian explosion of non-relational (a.k.a. NoSQL) databases:
4. Key Differences Between SQL and NoSQL
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SQL NoSQL
High-level Programming Language SQL
Transactions
Query Optimizer
Non-key indexes
5. Key Differences Between SQL and NoSQL
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SQL NoSQL
Scalability TB PB
Maximum Cluster Size 48 (Oracle RAC) 1000+
Distributed
Read Time
Depends on table size and
indexes
Linear
Write Time
Depends on table size and
indexes
Linear
Table Schema (column names,
data types)
Predetermined
Raw bytes. Schema
determined by application
6. How Proven Is NoSQL Technology
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NoSQL is the leading technology behind big data applications.
• Google – search, gmail, AppEngine
• Yahoo/Microsoft – search
• Amazon – e-commerce, search, cloud computing (AWS DynamoDB)
• IBM Big Insights, Microsoft Azure HD Insight
7. Big Data Adoption
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HBase behind Facebook Messages:
• 6+ billion messages per day
• 75+ billion R/W operations per day
• Peak throughput: 1.5 million R/W operations per second
• 2+ petabytes of data (6+ PB including replicas) with data growth of over 8 TB per day
8. Big Data Adoption
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IBM BigInsights behind Vestas:
• A wind energy company in Denmark is reducing the time to analyze petabytes of data from
several weeks to 15 minutes to improve the accuracy of wind turbine placement.
• Stores 2.8 PB of company historical data together with over 178 external parameters:
temperature, barometric pressure, humidity, precipitation, wind direction, wind velocity etc.
• Stores precise data on weather over the past 11 years.
• Collects data from over 35,000 meteorological stations.
9. Big Data Adoption
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HBase behind Explorys:
• Explorys uses HBase to enable search and analysis of patient populations, treatment protocols,
and clinical outcomes.
• Stores over 275 billion clinical, financial and operational data elements.
• 48 million unique patient files.
• Collecting data from over 340 hospitals and 300,000 healthcare providers.
• Pull data from 22 integrated major healthcare systems.
10. Axibase Time Series Database
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Scalability & Speed
• Collects billions of samples per day. Retains detailed data forever.
Features
• Combines database, rule engine, and visualization in one product.
Analytical Rule Engine
• Applies aggregate functions and filters on streaming data.
Integration
• Accepts data from any source based on industry-standard protocols.
Visualization
• Built-in portals with smart widgets.
12. Big Data for IT Monitoring
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• Retain detailed data forever.
• Collect statistics at high-frequency, for example every 15 seconds.
• Consolidate performance statistics from all systems into one database: facilities, network,
storage, servers, applications, databases, transactions, service providers, user activity etc.
• Monitor infrastructure based on abnormal deviations instead of manual thresholds.
• Apply statistical formulas to predict outages.
• Take advantage of schema-less database to collect data from any source.
13. Big Data for Developers
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• Support for annotation-style instrumentation.
• Alternative to byte-code instrumentation and
file logging.
• Collect detailed performance and usage
statistics for reporting and analytics, without
writing custom monitors.
14. Big Data for Operations
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• Gather and analyze statistical data generated by the various systems and sensors.
• Analytics that can support decision control systems.
• Allows for better real‐time operations decision‐support.
• Generate accurate forecasts of upcoming issues:
• Delays
• Scheduled maintenance based on product usage and sensor data instead of warranty
periods
• Improved customer service times and standards.
15. ATSD Architecture
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• ATSD architecture combines database,
analytics and reporting tools into one
complete product.
• Data locality makes analytics run faster.
• Application server layer is simplified to
provide core shared services
16. ATSD Components
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• Pluggable driver provides support for
different storage engines
• Compute, persistence and data
collection layers scaled independently
17. Fault Tolerance
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• ATSD is a distributed system,
with high fault tolerance.
• Each data sample is
automatically replicated 3
times for recovery.
18. ATSD Scalability
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• ATSD is a distributed, non-relational database with high throughput, fault tolerance and reading
speed.
• ATSD can collect billions of metrics per day and store petabytes of data.
• ATSD supports millisecond resolution and sampling intervals of up to several measurements per
second. The data is stored without losing accuracy.
• Additional nodes can be added at runtime to handle increasing volumes. ATSD automatically
distributes the table across active nodes.
• New nodes can be added in remote data centers to minimize network traffic.
19. Supported Data Types
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• Two types of data ingestion: push and pull.
• ATSD supports numeric values, log messages and properties (collection of key-values).
• ATSD uses collectors for retrieving structured and unstructured data from remote sources.
• Support for standard protocols: Telnet, ICMP, CSV/TSV, FILE, JMX, HTTP, and JSON.
20. Data Collection
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• Collection is agentless; data is pushed by external systems into ATSD.
• New metrics are auto-registered. No need to update schema or restart any server components.
• Existing monitoring tools can be instrumented to stream data into ATSD.
• Each data sample can be tagged (key = value) at source for subsequent querying, aggregations,
and roll-ups.
21. Data Storage
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• Built-in data compression provides 70%-80% disk space savings over raw data.
• No data needs to be deleted. Seek time is almost linear regardless of the dataset size.
• Data storage is sparse and efficient. ATSD stores only what is collected instead of long rows with
NULLs or zeros, as is the case in relational model.
• VMware VMFS-attached disks are sufficient for small to medium clusters.
• Direct attached disks with JBOD are recommended for larger clusters.
• JBOD alternatives to minimize node recovery time are available from leading storage vendors,
such as NetApp E-Series.
22. Built-in Instruments
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Unlike conventional data warehouses, ATSD comes with a set of built-in tools for data analysis:
• Analytical Rule Engine
• Forecasting
• Visualization
23. Analytical Rule Engine
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• Evaluates incoming data in memory based on statistical rules.
• Statistical rules are applied to the incoming data stream before data is
stored on disk.
• As data is ingested by ATSD server, a subset of samples that match rule
queries are routed to the rule engine for processing.
• Rule Engine supports both time- and count- based data windows.
• Rule expressions and filters can reference not just numeric values but also
tags such as system type, location, priority to ensure that alerts are raised
only for critical issues.
• Multiple metrics and entities can be correlated within the same rule.
24. Analytical Rule Engine – Rule Examples
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Type Window Example Description
threshold none value > 75 Raise an alert if last metric value exceeds threshold
range none value > 50 AND value <= 75 Raise an alert if value is outside of specified range
statistical-count count(10) avg(value) > 75 Raise an alert if average value of the last 10 samples exceeds threshold
statistical-time time('15 min') avg(value) > 75 Raise an alert if average value for the last 15 minutes exceeds threshold
statistical-deviation time('15 min') avg(value) / avg(value(time: '1 hour')) >
1.25
Raise an alert if 15-minute average exceeds 1-hour average by more than 25%
statistical-ungrouped time('15 min') avg(value) > 75 Raise an alert if 15-minute average values for all entities in the group exceeds threshold
metric correlation time('15 min') avg(value) > 75 AND avg(value(metric:
'loadavg.1m')) > 0.5
Raise an alert if average values for two separate metrics for the last 15 minutes exceed predefined
thresholds
entity correlation time('15 min') avg(value) > 75 AND avg(value(entity:
'host2')) > 75
Raise an alert if average values for two entities for the last 15 minutes exceed thresholds
threshold override time('15 min') avg(value) >= entity.groupTag('cpu
_avg').min()
Raise an alert if 15-minute average value exceeds minimum threshold specified for groups to which
the entity belongs
cpu forecast deviation time('5 min') abs(forecast_deviation(wavg())) > 2 Raise an alert if 5-minute average deviates from forecast by more than two standard deviations
cpu forecast diff time('10 min') abs(wavg() - forecast()) > 25 Raise alert if absolute forecast deviates from average by more than specified value
disk threshold time('15 min') new_maximum() &&
threshold_linear_time(99) < 120
Raise alert if last value is the highest observed and linear threshold is expected to violate the 99%
threshold in less than 120 minutes
27. Forecasting
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• Customers have a growing need to predict problems before they occur. The accuracy of
predictions and the percentage of false positives/negatives highly depends on the frequency of
data collection, the retention interval, and algorithms.
• The use of built-in autoregressive time-series extrapolation algorithms (Holt-Winters, ARIMA,
etc.) in ATSD allows predicting of system failures at early stages.
• The forecasting process is resource intensive and is most effective in a clustered system with
data locality such as ATSD.
• Dynamic predictions eliminate the need to set manual thresholds.
30. Forecast Settings
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• ATSD selects the most accurate
forecasting algorithm for each
time-series separately based on a
ranking system.
• The winning algorithm is used to
compute forecast for the next day,
week or month.
• Pre-computed forecasts can be
used in rule engine.
32. Visualization
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• ATSD can be integrated with Axibase Enterprise Reporting using the ATSD adapter
• ATSD comes with a wide variety of widgets for creating interactive portals directly in ATSD.
• ATSD widgets are designed from the ground-up to handle large data sets and calculations on the
client.
• ATSD visualization is supported on mobile devices and Smart TVs.
34. Search
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• Implemented in ATSD is log file search system to detect problems in distributed systems for the
purposes of security, audit and change control.
Notifications
• Supports standard notification mechanisms: email, console, web service, and notification in the
environment.
• For example, Axibase LED lighting system - the "Data Cube", which changes colors depending on
the status of IT services.
35. ATSD Benefits
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• Enables customers to extract value from data that already exists in their operational and IT
infrastructures.
• Delivers preemptive monitoring through identification of abnormal behaviors in production
systems.
• Eliminates most manually-defined rules from the customer’s monitoring catalog.
• Serves as a centralized repository for historical data.
• Directly supported by AER for Dashboards, Reports, Capacity Planning
36. System Requirements
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• Operating Systems:
• Red Hat Enterprise Linux 5.6+
• Ubuntu 12.04+
• Suse Linux Enterprise Server 10+
• Computing Hardware:
Edition Community - FREE Standard Enterprise
ATSD Nodes 1 1 + 1 > 5
Processors 2 vCPU, 2+ GHz 4 vCPU, 2+ GHz 4 vCPU, 2+ GHz
Memory 4 GB (2GB for JVM) 16 GB (8GB for JVM) 16 GB (8GB for JVM)
37. Use Cases
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• ITM long-term history extension
• nmon reporting for AIX, Linux and Solaris
• Minimize exceptions in monitoring catalog
• Collect environmental data from SCADA
• Predictive Maintenance – based on sensors
38. ITM History Extension
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• ITM can be instrumented to write streaming data into CSV files.
• CSV can be instantly uploaded into ATSD using inotify utility and wget.
• Example: private history streaming in ITM
• KHD_CSV_OUTPUT_ACTIVATE = Y
39. ITM History Extension
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• Warehouse Proxy Agent is setup to save history data to CSV file
on the local machine.
• ATSD ingests the CSV files for analytics and long-term storage.
• ATSD converts the data using built in parsers.
40. nmon Reporting
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• Consolidate trusted statistics from UNIX systems in one database
• ATSD is able to collect, parse and analyze nmon files
• Analyze nmon data with forecasting algorithms
• Capitalize on nmon data with two predefined visualization portals or easily create your own
portals using built-in HTML5 widgets
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Contact Axibase
Axibase Contact Details:
• General - 408.973.7897
• Fax - 408.725.8885
• Email - sales@axibase.com
Our headquarters are located in Cupertino, Silicon Valley:
• 19925 Stevens Creek Blvd. Cupertino, CA 95014 USA