StarRocks, an open-source distributed columnar engine for real-time analytics, is carving its niche in the big data landscape. Its ability to handle high-velocity data streams and deliver blazing-fast query responses makes it a compelling choice for modern analytics workloads. Let's delve into the intricacies of real-time analytics with StarRocks and explore its capabilities.
Data Ingestion:
The journey begins with ingesting data into StarRocks. It supports a variety of real-time data sources, including Kafka, Pulsar, and custom streaming protocols. These integrations allow seamless data flow from streaming sources to StarRocks, ensuring minimal latency.
Stream Processing and Storage:
StarRocks employs a hybrid architecture for real-time processing. Incoming data streams are first processed by lightweight stream engines like Flink or Spark. These engines perform initial aggregations and transformations, preparing the data for efficient storage in StarRocks' columnar format. This format facilitates rapid data retrieval and filtering, crucial for real-time querying.
Real-time Querying:
The true power of StarRocks lies in its real-time query engine. Once data lands in StarRocks, users can leverage SQL-like queries to analyze it with minimal lag. StarRocks optimizes queries by exploiting its columnar storage and parallel processing capabilities. This enables sub-second response times for even complex queries, empowering users to gain immediate insights from their data.
Advanced Features:
StarRocks packs several features that further enhance its real-time analytics prowess. Materialized views act as pre-computed summaries of data, accelerating frequently used queries. Additionally, StarRocks' automatic tiered storage seamlessly migrates less frequently accessed data to cost-effective storage solutions, optimizing resource utilization.
Build User-Facing Analytics Application That Scales Using StarRocks (DLH).pdfAlbert Wong
This article isn't just about building an application, it's about empowering your users to unleash the full potential of their data. We'll delve into the world of StarRocks, a cutting-edge engine for real-time analytics, and guide you through the process of constructing user-facing applications that deliver fast, insightful, and visually captivating experiences.
Dive into the Why:
First, we'll explore the compelling reasons to choose StarRocks. We'll demystify its ability to handle high-velocity data streams and deliver sub-second query responses, making it ideal for applications where real-time insights are king.
Laying the Foundation:
Next, we'll embark on a step-by-step journey through the application development process. We'll discuss:
Data Ingestion: Learn how to seamlessly integrate real-time data sources like Kafka and Pulsar into your application, ensuring a constant flow of fresh insights.
Building the Core: We'll explore various options for structuring your application's backend, showcasing the pros and cons of different frameworks and libraries.
Querying Made Easy: Delve into StarRocks' intuitive SQL interface and discover how to craft powerful queries that unlock the treasure trove of data hiding within.
Visualization Wonderland: Explore the vast landscape of data visualization tools compatible with StarRocks. We'll showcase ways to transform complex data into interactive dashboards, compelling charts, and captivating reports that resonate with your users.
Beyond the Basics:
We'll push the boundaries by exploring advanced features like:
Materialized Views: Discover how to pre-compute data summaries for even faster querying.
Security and Access Control: Learn how to implement robust security measures to protect sensitive data while granting appropriate access levels to different user groups.
Customization and Scalability: Explore options for tailoring the application to your specific needs and ensuring it can gracefully handle growing data volumes and user traffic.
Conclusion:
By the end of this article, you'll be equipped with the knowledge and tools to build user-facing analytics applications that empower your users to harness the power of real-time data and make confident, data-driven decisions. This journey isn't just about building an application, it's about transforming how your users interact with data, unlocking a world of actionable insights and limitless possibilities.
So, are you ready to conquer complexity and unleash the power of StarRocks? Dive into this article and start building your next game-changing user-facing analytics application!
Introduction of streaming data, difference between batch processing and stream processing, Research issues in streaming data processing, Performance evaluation metrics , tools for stream processing.
TidalScale has created a software defined computer.
At TidalScale, we have created a simple cost-effective way for a data scientist, an analyst, an engineer, a scientist, a database administrator, or a software developer to access a group of servers through a single operating system instance as if it were a single supercomputer. This dramatically simplifies development, while reducing software scaling complexity not to mention a dramatic cost saving in hardware and software.
We configure hosted hardware into one or more TidalPods. Each TidalPod is a virtual supercomputer comprising a set of commodity servers configured with the TidalScale HyperKernel. What the user sees is standard Linux, FreeBSD or Windows running with the sum of all memory, processors, networks, and I/O. The secret sauce is the HyperKernel that fools the guest OS into thinking it’s running directly on a huge, expensive machine when in fact it’s running on a set of smaller, less expensive servers.
We offer an incredibly simple user experience.
• Define the computer size you want (Number of CPU, Amount of Memory), boot the virtual machine, then login to the computer…
Thus, we enable a simple cost-effective way for a data scientist, an analyst, an engineer, a scientist, a database administrator, or a software developer to access a group of servers in a Datacenter through a single operating system instance as if it were a single supercomputer. This dramatically simplifies development, while reducing software scaling complexity not to mention a dramatic cost saving in hardware and software.
Big Data Berlin v8.0 Stream Processing with Apache Apex Apache Apex
This document discusses Apache Apex, an open source stream processing framework. It provides an overview of stream data processing and common use cases. It then describes key Apache Apex capabilities like in-memory distributed processing, scalability, fault tolerance, and state management. The document also highlights several customer use cases from companies like PubMatic, GE, and Silver Spring Networks that use Apache Apex for real-time analytics on data from sources like IoT sensors, ad networks, and smart grids.
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.
ADV Slides: Comparing the Enterprise Analytic SolutionsDATAVERSITY
Data is the foundation of any meaningful corporate initiative. Fully master the necessary data, and you’re more than halfway to success. That’s why leverageable (i.e., multiple use) artifacts of the enterprise data environment are so critical to enterprise success.
Build them once (keep them updated), and use again many, many times for many and diverse ends. The data warehouse remains focused strongly on this goal. And that may be why, nearly 40 years after the first database was labeled a “data warehouse,” analytic database products still target the data warehouse.
Estimating the Total Costs of Your Cloud Analytics PlatformDATAVERSITY
Organizations today need a broad set of enterprise data cloud services with key data functionality to modernize applications and utilize machine learning. They need a platform designed to address multi-faceted needs by offering multi-function Data Management and analytics to solve the enterprise’s most pressing data and analytic challenges in a streamlined fashion. They need a worry-free experience with the architecture and its components.
Build User-Facing Analytics Application That Scales Using StarRocks (DLH).pdfAlbert Wong
This article isn't just about building an application, it's about empowering your users to unleash the full potential of their data. We'll delve into the world of StarRocks, a cutting-edge engine for real-time analytics, and guide you through the process of constructing user-facing applications that deliver fast, insightful, and visually captivating experiences.
Dive into the Why:
First, we'll explore the compelling reasons to choose StarRocks. We'll demystify its ability to handle high-velocity data streams and deliver sub-second query responses, making it ideal for applications where real-time insights are king.
Laying the Foundation:
Next, we'll embark on a step-by-step journey through the application development process. We'll discuss:
Data Ingestion: Learn how to seamlessly integrate real-time data sources like Kafka and Pulsar into your application, ensuring a constant flow of fresh insights.
Building the Core: We'll explore various options for structuring your application's backend, showcasing the pros and cons of different frameworks and libraries.
Querying Made Easy: Delve into StarRocks' intuitive SQL interface and discover how to craft powerful queries that unlock the treasure trove of data hiding within.
Visualization Wonderland: Explore the vast landscape of data visualization tools compatible with StarRocks. We'll showcase ways to transform complex data into interactive dashboards, compelling charts, and captivating reports that resonate with your users.
Beyond the Basics:
We'll push the boundaries by exploring advanced features like:
Materialized Views: Discover how to pre-compute data summaries for even faster querying.
Security and Access Control: Learn how to implement robust security measures to protect sensitive data while granting appropriate access levels to different user groups.
Customization and Scalability: Explore options for tailoring the application to your specific needs and ensuring it can gracefully handle growing data volumes and user traffic.
Conclusion:
By the end of this article, you'll be equipped with the knowledge and tools to build user-facing analytics applications that empower your users to harness the power of real-time data and make confident, data-driven decisions. This journey isn't just about building an application, it's about transforming how your users interact with data, unlocking a world of actionable insights and limitless possibilities.
So, are you ready to conquer complexity and unleash the power of StarRocks? Dive into this article and start building your next game-changing user-facing analytics application!
Introduction of streaming data, difference between batch processing and stream processing, Research issues in streaming data processing, Performance evaluation metrics , tools for stream processing.
TidalScale has created a software defined computer.
At TidalScale, we have created a simple cost-effective way for a data scientist, an analyst, an engineer, a scientist, a database administrator, or a software developer to access a group of servers through a single operating system instance as if it were a single supercomputer. This dramatically simplifies development, while reducing software scaling complexity not to mention a dramatic cost saving in hardware and software.
We configure hosted hardware into one or more TidalPods. Each TidalPod is a virtual supercomputer comprising a set of commodity servers configured with the TidalScale HyperKernel. What the user sees is standard Linux, FreeBSD or Windows running with the sum of all memory, processors, networks, and I/O. The secret sauce is the HyperKernel that fools the guest OS into thinking it’s running directly on a huge, expensive machine when in fact it’s running on a set of smaller, less expensive servers.
We offer an incredibly simple user experience.
• Define the computer size you want (Number of CPU, Amount of Memory), boot the virtual machine, then login to the computer…
Thus, we enable a simple cost-effective way for a data scientist, an analyst, an engineer, a scientist, a database administrator, or a software developer to access a group of servers in a Datacenter through a single operating system instance as if it were a single supercomputer. This dramatically simplifies development, while reducing software scaling complexity not to mention a dramatic cost saving in hardware and software.
Big Data Berlin v8.0 Stream Processing with Apache Apex Apache Apex
This document discusses Apache Apex, an open source stream processing framework. It provides an overview of stream data processing and common use cases. It then describes key Apache Apex capabilities like in-memory distributed processing, scalability, fault tolerance, and state management. The document also highlights several customer use cases from companies like PubMatic, GE, and Silver Spring Networks that use Apache Apex for real-time analytics on data from sources like IoT sensors, ad networks, and smart grids.
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.
ADV Slides: Comparing the Enterprise Analytic SolutionsDATAVERSITY
Data is the foundation of any meaningful corporate initiative. Fully master the necessary data, and you’re more than halfway to success. That’s why leverageable (i.e., multiple use) artifacts of the enterprise data environment are so critical to enterprise success.
Build them once (keep them updated), and use again many, many times for many and diverse ends. The data warehouse remains focused strongly on this goal. And that may be why, nearly 40 years after the first database was labeled a “data warehouse,” analytic database products still target the data warehouse.
Estimating the Total Costs of Your Cloud Analytics PlatformDATAVERSITY
Organizations today need a broad set of enterprise data cloud services with key data functionality to modernize applications and utilize machine learning. They need a platform designed to address multi-faceted needs by offering multi-function Data Management and analytics to solve the enterprise’s most pressing data and analytic challenges in a streamlined fashion. They need a worry-free experience with the architecture and its components.
Sql azure cluster dashboard public.pptQingsong Yao
This document discusses building a centralized dashboard to monitor SQL Azure clusters in real-time. Key points:
- The goal was to provide a single place to view cluster status and detect issues early through telemetry data analysis.
- Lessons included choosing efficient data techniques, building resilience into the data pipeline, and monitoring pipeline performance.
- The dashboard helped transition monitoring from reactive to proactive by enabling new alert detection based on real-time trend analysis across clusters.
Data Engineer's Lunch #85: Designing a Modern Data StackAnant Corporation
What are the design considerations that go into architecting a modern data warehouse? This presentation will cover some of the requirements analysis, design decisions, and execution challenges of building a modern data lake/data warehouse.
Data Con LA 2022 - Making real-time analytics a reality for digital transform...Data Con LA
Fadi Azhari, VP of Marketing, StarRocks
- Enterprises are facing an imperative to grow their business to gain competitive advantage at breakneck speed. They need to achieve that by adding new value services efficiently and effectively.
- To achieve growth from these new services, enterprises need new insights instantly from their constantly changing data.
- Unfortunately, current data infrastructure solutions offer sub-optimal solutions that leave customers wrestling with to achieve their business goals.
Why is real-time analytics so difficult?
- Data freshness and fast responsiveness are both important and present technical challenges of their own.
- User-facing analytics and operational analytics mean supporting thousands of users simultaneously.
- You have to do a lot of unnecessary de-normalized tables (de-normalization jobs) in streaming pipelines. It is very difficult to build and maintain.
- You can't easily update the data in realtime to analyze business changes.
StarRocks re-invents real-time analytics with the only platform uniquely designed for the next generation real-time Enterprise, unleashing the power of business intelligence to help organizations accelerate their digital transformation. StarRocks makes real-time analytics a reality with the fastest, easy-to-use analytics platform on the planet.
Streamlio and IoT analytics with Apache PulsarStreamlio
To keep up with fast-moving IoT data, you need technology that can collect, process and store data with performance and scalability. This presentation from Data Day Texas looks at the technology requirements and how Apache Pulsar can help to meet them.
To understand an application’s performance, first you have to know what to measure. That’s the easy part. How do you take those measurements? Store them? Analyze them? Get them to the people who need them? Well, that’s where things get complicated, especially in the high-traffic distributed systems of the modern web! Like careful scientists, we must observe our subjects without altering them, and we must report our findings quickly so that we have the data necessary to make smart choices about the health and growth of the system.
Let’s explore the lessons learned by engineers at one of the world’s top web companies in their quest to find meaning at 5 MB/s. We’ll discuss the tools and techniques that enable the collection, indexing, and analysis of billions or more datapoints each hour, and learn how these same approaches can empower your applications and your business, no matter the scale.
Data Pipelines with Spark & DataStax EnterpriseDataStax
This document discusses building data pipelines for both static and streaming data using Apache Spark and DataStax Enterprise (DSE). For static data, it recommends using optimized data storage formats, distributed and scalable technologies like Spark, interactive analysis tools like notebooks, and DSE for persistent storage. For streaming data, it recommends using scalable distributed technologies, Kafka to decouple producers and consumers, and DSE for real-time analytics and persistent storage across datacenters.
Engineering Machine Learning Data Pipelines Series: Streaming New Data as It ...Precisely
This document discusses engineering machine learning data pipelines and addresses five big challenges: 1) scattered and difficult to access data, 2) data cleansing at scale, 3) entity resolution, 4) tracking data lineage, and 5) ongoing real-time changed data capture and streaming. It presents DMX Change Data Capture as a solution to capture changes from various data sources and replicate them in real-time to targets like Kafka, HDFS, databases and data lakes to feed machine learning models. Case studies demonstrate how DMX-h has helped customers like a global hotel chain and insurance and healthcare companies build scalable data pipelines.
AI as a Service, Build Shared AI Service Platforms Based on Deep Learning Tec...Databricks
I will share the vision and the production journey of how we build enterprise shared AI As A Service platforms with distributed deep learning technologies. Including those topics:
1) The vision of Enterprise Shared AI As A Service and typical AI services use cases at FinTech industry
2) The high level architecture design principles for AI As A Service
3) The technical evaluation journey to choose an enterprise deep learning framework with comparisons, such as why we choose Deep learning framework based on Spark ecosystem
4) Share some production AI use cases, such as how we implemented new Users-Items Propensity Models with deep learning algorithms with Spark,improve the quality , performance and accuracy of offer and campaigns design, targeting offer matching and linking etc.
5) Share some experiences and tips of using deep learning technologies on top of Spark , such as how we conduct Intel BigDL into a real production.
The document discusses the need for a model management framework to ease the development and deployment of analytical models at scale. It describes how such a framework could capture and template models created by data scientists, enable faster model iteration through a brute force approach, and visually compare models. The framework would reduce complexity for data scientists and allow business analysts to participate in modeling. It is presented as essential for enabling predictive modeling on data from thousands of sensors in an Internet of Things platform.
Search on the fly: how to lighten your Big Data - Simona Russo, Auro Rolle - ...Codemotion
The talk presents a new technique of realtime single entity information extraction and investigation. The technique eliminates regular refresh and persistence of data within the search engine (ETL), providing real-time access to source data and improving response times using in-memory data techniques. The solution presented is a concrete solution with live customers, based upon real business needs. I will explain the architectural overview, the technology stack used based on Apache Lucene library, the accomplished results and how to scale out the solution.
This document discusses analytics and IoT. It covers key topics like data collection from IoT sensors, data storage and processing using big data tools, and performing descriptive, predictive, and prescriptive analytics. Cloud platforms and visualization tools that can be used to build end-to-end IoT and analytics solutions are also presented. The document provides an overview of building IoT solutions for collecting, analyzing, and gaining insights from sensor data.
The document discusses several data storage solutions from HGST, including:
- The Ultrastar Data102 and Data60 hybrid storage platforms, which can store up to 1.2PB and 720TB respectively in a 4U form factor, and feature vibration isolation and efficient cooling technologies.
- The ActiveScale X100 and P100 object storage systems, which provide scalable, durable storage for applications such as analytics, media and entertainment, and backup.
- The IntelliFlash all-flash and hybrid storage arrays, which combine flash performance with data management capabilities to accelerate a wide variety of workloads while maintaining high density and compelling economics.
Batch Processing vs Stream Processing Differencejeetendra mandal
Batch processing involves processing large batches of data together, and has higher latency measured in minutes or hours. Stream processing processes continuous data in real-time with lower latency measured in milliseconds or seconds. The key differences are that batch processing handles large batches of data while stream processing handles individual records or micro-batches, and batch processing has higher latency while stream processing has lower latency.
ADV Slides: The Evolution of the Data Platform and What It Means to Enterpris...DATAVERSITY
Thirty years is a long time for a technology foundation to be as active as relational databases. Are their replacements here?
In this webinar, we look at this foundational technology for modern Data Management and show how it evolved to meet the workloads of today, as well as when other platforms make sense for enterprise data.
The document discusses streaming data analytics and Hitachi's Streaming Data Platform (HSDP). HSDP is a real-time streaming software solution that can analyze massive amounts of data as it is generated in real-time. It allows businesses to gain valuable insights from data in motion and make timely decisions. HSDP is highly scalable, adaptable across industries, and can integrate with open source technologies. It enables use cases like real-time analytics, capturing perishable insights, and analyzing data distributed across locations and time.
The document discusses streaming data analytics and Hitachi's Streaming Data Platform (HSDP). HSDP is a real-time streaming software solution that can analyze massive amounts of data as it is generated in real-time. It allows businesses to gain valuable insights from data in motion and make timely decisions. HSDP is highly scalable, adaptable across industries, and can integrate with open source technologies. It enables use cases like real-time analytics, capturing perishable insights, and distributed analysis across geo-locations as data is born distributed.
The document discusses streaming data analytics and Hitachi's Streaming Data Platform (HSDP). HSDP is a real-time streaming software solution that can analyze massive amounts of data as it is generated in real-time. It allows businesses to gain valuable insights from data in motion and make timely decisions. HSDP is highly scalable, adaptable across industries, and can integrate with open source technologies. It enables use cases like real-time analytics, capturing perishable insights, and distributed analysis across geo-locations as data is born distributed.
The document provides guidance on leveling up a company's data infrastructure and analytics capabilities. It recommends starting by acquiring and storing data from various sources in a data warehouse. The data should then be transformed into a usable shape before performing analytics. When setting up the infrastructure, the document emphasizes collecting user requirements, designing the data warehouse around key data aspects, and choosing technology that supports iteration, extensibility and prevents data loss. It also provides tips for creating effective dashboards and exploratory analysis. Examples of implementing this approach for two sample companies, MESI and SalesGenomics, are discussed.
Event-Driven Architecture Masterclass: Engineering a Robust, High-performance...ScyllaDB
Discover how to avoid common pitfalls when shifting to an event-driven architecture (EDA) in order to boost system recovery and scalability. We cover Kafka Schema Registry, in-broker transformations, event sourcing, and more.
Measures in SQL (SIGMOD 2024, Santiago, Chile)Julian Hyde
SQL has attained widespread adoption, but Business Intelligence tools still use their own higher level languages based upon a multidimensional paradigm. Composable calculations are what is missing from SQL, and we propose a new kind of column, called a measure, that attaches a calculation to a table. Like regular tables, tables with measures are composable and closed when used in queries.
SQL-with-measures has the power, conciseness and reusability of multidimensional languages but retains SQL semantics. Measure invocations can be expanded in place to simple, clear SQL.
To define the evaluation semantics for measures, we introduce context-sensitive expressions (a way to evaluate multidimensional expressions that is consistent with existing SQL semantics), a concept called evaluation context, and several operations for setting and modifying the evaluation context.
A talk at SIGMOD, June 9–15, 2024, Santiago, Chile
Authors: Julian Hyde (Google) and John Fremlin (Google)
https://doi.org/10.1145/3626246.3653374
Sql azure cluster dashboard public.pptQingsong Yao
This document discusses building a centralized dashboard to monitor SQL Azure clusters in real-time. Key points:
- The goal was to provide a single place to view cluster status and detect issues early through telemetry data analysis.
- Lessons included choosing efficient data techniques, building resilience into the data pipeline, and monitoring pipeline performance.
- The dashboard helped transition monitoring from reactive to proactive by enabling new alert detection based on real-time trend analysis across clusters.
Data Engineer's Lunch #85: Designing a Modern Data StackAnant Corporation
What are the design considerations that go into architecting a modern data warehouse? This presentation will cover some of the requirements analysis, design decisions, and execution challenges of building a modern data lake/data warehouse.
Data Con LA 2022 - Making real-time analytics a reality for digital transform...Data Con LA
Fadi Azhari, VP of Marketing, StarRocks
- Enterprises are facing an imperative to grow their business to gain competitive advantage at breakneck speed. They need to achieve that by adding new value services efficiently and effectively.
- To achieve growth from these new services, enterprises need new insights instantly from their constantly changing data.
- Unfortunately, current data infrastructure solutions offer sub-optimal solutions that leave customers wrestling with to achieve their business goals.
Why is real-time analytics so difficult?
- Data freshness and fast responsiveness are both important and present technical challenges of their own.
- User-facing analytics and operational analytics mean supporting thousands of users simultaneously.
- You have to do a lot of unnecessary de-normalized tables (de-normalization jobs) in streaming pipelines. It is very difficult to build and maintain.
- You can't easily update the data in realtime to analyze business changes.
StarRocks re-invents real-time analytics with the only platform uniquely designed for the next generation real-time Enterprise, unleashing the power of business intelligence to help organizations accelerate their digital transformation. StarRocks makes real-time analytics a reality with the fastest, easy-to-use analytics platform on the planet.
Streamlio and IoT analytics with Apache PulsarStreamlio
To keep up with fast-moving IoT data, you need technology that can collect, process and store data with performance and scalability. This presentation from Data Day Texas looks at the technology requirements and how Apache Pulsar can help to meet them.
To understand an application’s performance, first you have to know what to measure. That’s the easy part. How do you take those measurements? Store them? Analyze them? Get them to the people who need them? Well, that’s where things get complicated, especially in the high-traffic distributed systems of the modern web! Like careful scientists, we must observe our subjects without altering them, and we must report our findings quickly so that we have the data necessary to make smart choices about the health and growth of the system.
Let’s explore the lessons learned by engineers at one of the world’s top web companies in their quest to find meaning at 5 MB/s. We’ll discuss the tools and techniques that enable the collection, indexing, and analysis of billions or more datapoints each hour, and learn how these same approaches can empower your applications and your business, no matter the scale.
Data Pipelines with Spark & DataStax EnterpriseDataStax
This document discusses building data pipelines for both static and streaming data using Apache Spark and DataStax Enterprise (DSE). For static data, it recommends using optimized data storage formats, distributed and scalable technologies like Spark, interactive analysis tools like notebooks, and DSE for persistent storage. For streaming data, it recommends using scalable distributed technologies, Kafka to decouple producers and consumers, and DSE for real-time analytics and persistent storage across datacenters.
Engineering Machine Learning Data Pipelines Series: Streaming New Data as It ...Precisely
This document discusses engineering machine learning data pipelines and addresses five big challenges: 1) scattered and difficult to access data, 2) data cleansing at scale, 3) entity resolution, 4) tracking data lineage, and 5) ongoing real-time changed data capture and streaming. It presents DMX Change Data Capture as a solution to capture changes from various data sources and replicate them in real-time to targets like Kafka, HDFS, databases and data lakes to feed machine learning models. Case studies demonstrate how DMX-h has helped customers like a global hotel chain and insurance and healthcare companies build scalable data pipelines.
AI as a Service, Build Shared AI Service Platforms Based on Deep Learning Tec...Databricks
I will share the vision and the production journey of how we build enterprise shared AI As A Service platforms with distributed deep learning technologies. Including those topics:
1) The vision of Enterprise Shared AI As A Service and typical AI services use cases at FinTech industry
2) The high level architecture design principles for AI As A Service
3) The technical evaluation journey to choose an enterprise deep learning framework with comparisons, such as why we choose Deep learning framework based on Spark ecosystem
4) Share some production AI use cases, such as how we implemented new Users-Items Propensity Models with deep learning algorithms with Spark,improve the quality , performance and accuracy of offer and campaigns design, targeting offer matching and linking etc.
5) Share some experiences and tips of using deep learning technologies on top of Spark , such as how we conduct Intel BigDL into a real production.
The document discusses the need for a model management framework to ease the development and deployment of analytical models at scale. It describes how such a framework could capture and template models created by data scientists, enable faster model iteration through a brute force approach, and visually compare models. The framework would reduce complexity for data scientists and allow business analysts to participate in modeling. It is presented as essential for enabling predictive modeling on data from thousands of sensors in an Internet of Things platform.
Search on the fly: how to lighten your Big Data - Simona Russo, Auro Rolle - ...Codemotion
The talk presents a new technique of realtime single entity information extraction and investigation. The technique eliminates regular refresh and persistence of data within the search engine (ETL), providing real-time access to source data and improving response times using in-memory data techniques. The solution presented is a concrete solution with live customers, based upon real business needs. I will explain the architectural overview, the technology stack used based on Apache Lucene library, the accomplished results and how to scale out the solution.
This document discusses analytics and IoT. It covers key topics like data collection from IoT sensors, data storage and processing using big data tools, and performing descriptive, predictive, and prescriptive analytics. Cloud platforms and visualization tools that can be used to build end-to-end IoT and analytics solutions are also presented. The document provides an overview of building IoT solutions for collecting, analyzing, and gaining insights from sensor data.
The document discusses several data storage solutions from HGST, including:
- The Ultrastar Data102 and Data60 hybrid storage platforms, which can store up to 1.2PB and 720TB respectively in a 4U form factor, and feature vibration isolation and efficient cooling technologies.
- The ActiveScale X100 and P100 object storage systems, which provide scalable, durable storage for applications such as analytics, media and entertainment, and backup.
- The IntelliFlash all-flash and hybrid storage arrays, which combine flash performance with data management capabilities to accelerate a wide variety of workloads while maintaining high density and compelling economics.
Batch Processing vs Stream Processing Differencejeetendra mandal
Batch processing involves processing large batches of data together, and has higher latency measured in minutes or hours. Stream processing processes continuous data in real-time with lower latency measured in milliseconds or seconds. The key differences are that batch processing handles large batches of data while stream processing handles individual records or micro-batches, and batch processing has higher latency while stream processing has lower latency.
ADV Slides: The Evolution of the Data Platform and What It Means to Enterpris...DATAVERSITY
Thirty years is a long time for a technology foundation to be as active as relational databases. Are their replacements here?
In this webinar, we look at this foundational technology for modern Data Management and show how it evolved to meet the workloads of today, as well as when other platforms make sense for enterprise data.
The document discusses streaming data analytics and Hitachi's Streaming Data Platform (HSDP). HSDP is a real-time streaming software solution that can analyze massive amounts of data as it is generated in real-time. It allows businesses to gain valuable insights from data in motion and make timely decisions. HSDP is highly scalable, adaptable across industries, and can integrate with open source technologies. It enables use cases like real-time analytics, capturing perishable insights, and analyzing data distributed across locations and time.
The document discusses streaming data analytics and Hitachi's Streaming Data Platform (HSDP). HSDP is a real-time streaming software solution that can analyze massive amounts of data as it is generated in real-time. It allows businesses to gain valuable insights from data in motion and make timely decisions. HSDP is highly scalable, adaptable across industries, and can integrate with open source technologies. It enables use cases like real-time analytics, capturing perishable insights, and distributed analysis across geo-locations as data is born distributed.
The document discusses streaming data analytics and Hitachi's Streaming Data Platform (HSDP). HSDP is a real-time streaming software solution that can analyze massive amounts of data as it is generated in real-time. It allows businesses to gain valuable insights from data in motion and make timely decisions. HSDP is highly scalable, adaptable across industries, and can integrate with open source technologies. It enables use cases like real-time analytics, capturing perishable insights, and distributed analysis across geo-locations as data is born distributed.
The document provides guidance on leveling up a company's data infrastructure and analytics capabilities. It recommends starting by acquiring and storing data from various sources in a data warehouse. The data should then be transformed into a usable shape before performing analytics. When setting up the infrastructure, the document emphasizes collecting user requirements, designing the data warehouse around key data aspects, and choosing technology that supports iteration, extensibility and prevents data loss. It also provides tips for creating effective dashboards and exploratory analysis. Examples of implementing this approach for two sample companies, MESI and SalesGenomics, are discussed.
Event-Driven Architecture Masterclass: Engineering a Robust, High-performance...ScyllaDB
Discover how to avoid common pitfalls when shifting to an event-driven architecture (EDA) in order to boost system recovery and scalability. We cover Kafka Schema Registry, in-broker transformations, event sourcing, and more.
Similar to Real-Time Analytics With StarRocks (DWH+DL).pdf (20)
Measures in SQL (SIGMOD 2024, Santiago, Chile)Julian Hyde
SQL has attained widespread adoption, but Business Intelligence tools still use their own higher level languages based upon a multidimensional paradigm. Composable calculations are what is missing from SQL, and we propose a new kind of column, called a measure, that attaches a calculation to a table. Like regular tables, tables with measures are composable and closed when used in queries.
SQL-with-measures has the power, conciseness and reusability of multidimensional languages but retains SQL semantics. Measure invocations can be expanded in place to simple, clear SQL.
To define the evaluation semantics for measures, we introduce context-sensitive expressions (a way to evaluate multidimensional expressions that is consistent with existing SQL semantics), a concept called evaluation context, and several operations for setting and modifying the evaluation context.
A talk at SIGMOD, June 9–15, 2024, Santiago, Chile
Authors: Julian Hyde (Google) and John Fremlin (Google)
https://doi.org/10.1145/3626246.3653374
A Comprehensive Guide on Implementing Real-World Mobile Testing Strategies fo...kalichargn70th171
In today's fiercely competitive mobile app market, the role of the QA team is pivotal for continuous improvement and sustained success. Effective testing strategies are essential to navigate the challenges confidently and precisely. Ensuring the perfection of mobile apps before they reach end-users requires thoughtful decisions in the testing plan.
Enhanced Screen Flows UI/UX using SLDS with Tom KittPeter Caitens
Join us for an engaging session led by Flow Champion, Tom Kitt. This session will dive into a technique of enhancing the user interfaces and user experiences within Screen Flows using the Salesforce Lightning Design System (SLDS). This technique uses Native functionality, with No Apex Code, No Custom Components and No Managed Packages required.
Odoo releases a new update every year. The latest version, Odoo 17, came out in October 2023. It brought many improvements to the user interface and user experience, along with new features in modules like accounting, marketing, manufacturing, websites, and more.
The Odoo 17 update has been a hot topic among startups, mid-sized businesses, large enterprises, and Odoo developers aiming to grow their businesses. Since it is now already the first quarter of 2024, you must have a clear idea of what Odoo 17 entails and what it can offer your business if you are still not aware of it.
This blog covers the features and functionalities. Explore the entire blog and get in touch with expert Odoo ERP consultants to leverage Odoo 17 and its features for your business too.
An Overview of Odoo ERP
Odoo ERP was first released as OpenERP software in February 2005. It is a suite of business applications used for ERP, CRM, eCommerce, websites, and project management. Ten years ago, the Odoo Enterprise edition was launched to help fund the Odoo Community version.
When you compare Odoo Community and Enterprise, the Enterprise edition offers exclusive features like mobile app access, Odoo Studio customisation, Odoo hosting, and unlimited functional support.
Today, Odoo is a well-known name used by companies of all sizes across various industries, including manufacturing, retail, accounting, marketing, healthcare, IT consulting, and R&D.
The latest version, Odoo 17, has been available since October 2023. Key highlights of this update include:
Enhanced user experience with improvements to the command bar, faster backend page loading, and multiple dashboard views.
Instant report generation, credit limit alerts for sales and invoices, separate OCR settings for invoice creation, and an auto-complete feature for forms in the accounting module.
Improved image handling and global attribute changes for mailing lists in email marketing.
A default auto-signature option and a refuse-to-sign option in HR modules.
Options to divide and merge manufacturing orders, track the status of manufacturing orders, and more in the MRP module.
Dark mode in Odoo 17.
Now that the Odoo 17 announcement is official, let’s look at what’s new in Odoo 17!
What is Odoo ERP 17?
Odoo 17 is the latest version of one of the world’s leading open-source enterprise ERPs. This version has come up with significant improvements explained here in this blog. Also, this new version aims to introduce features that enhance time-saving, efficiency, and productivity for users across various organisations.
Odoo 17, released at the Odoo Experience 2023, brought notable improvements to the user interface and added new functionalities with enhancements in performance, accessibility, data analysis, and management, further expanding its reach in the market.
Project Management: The Role of Project Dashboards.pdfKarya Keeper
Project management is a crucial aspect of any organization, ensuring that projects are completed efficiently and effectively. One of the key tools used in project management is the project dashboard, which provides a comprehensive view of project progress and performance. In this article, we will explore the role of project dashboards in project management, highlighting their key features and benefits.
Unveiling the Advantages of Agile Software Development.pdfbrainerhub1
Learn about Agile Software Development's advantages. Simplify your workflow to spur quicker innovation. Jump right in! We have also discussed the advantages.
A neural network is a machine learning program, or model, that makes decisions in a manner similar to the human brain, by using processes that mimic the way biological neurons work together to identify phenomena, weigh options and arrive at conclusions.
The Key to Digital Success_ A Comprehensive Guide to Continuous Testing Integ...kalichargn70th171
In today's business landscape, digital integration is ubiquitous, demanding swift innovation as a necessity rather than a luxury. In a fiercely competitive market with heightened customer expectations, the timely launch of flawless digital products is crucial for both acquisition and retention—any delay risks ceding market share to competitors.
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.
Everything You Need to Know About X-Sign: The eSign Functionality of XfilesPr...XfilesPro
Wondering how X-Sign gained popularity in a quick time span? This eSign functionality of XfilesPro DocuPrime has many advancements to offer for Salesforce users. Explore them now!
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
Unlock the Secrets to Effortless Video Creation with Invideo: Your Ultimate G...The Third Creative Media
"Navigating Invideo: A Comprehensive Guide" is an essential resource for anyone looking to master Invideo, an AI-powered video creation tool. This guide provides step-by-step instructions, helpful tips, and comparisons with other AI video creators. Whether you're a beginner or an experienced video editor, you'll find valuable insights to enhance your video projects and bring your creative ideas to life.
WWDC 2024 Keynote Review: For CocoaCoders AustinPatrick Weigel
Overview of WWDC 2024 Keynote Address.
Covers: Apple Intelligence, iOS18, macOS Sequoia, iPadOS, watchOS, visionOS, and Apple TV+.
Understandable dialogue on Apple TV+
On-device app controlling AI.
Access to ChatGPT with a guest appearance by Chief Data Thief Sam Altman!
App Locking! iPhone Mirroring! And a Calculator!!
Preparing Non - Technical Founders for Engaging a Tech AgencyISH Technologies
Preparing non-technical founders before engaging a tech agency is crucial for the success of their projects. It starts with clearly defining their vision and goals, conducting thorough market research, and gaining a basic understanding of relevant technologies. Setting realistic expectations and preparing a detailed project brief are essential steps. Founders should select a tech agency with a proven track record and establish clear communication channels. Additionally, addressing legal and contractual considerations and planning for post-launch support are vital to ensure a smooth and successful collaboration. This preparation empowers non-technical founders to effectively communicate their needs and work seamlessly with their chosen tech agency.Visit our site to get more details about this. Contact us today www.ishtechnologies.com.au
2. Agenda
● Trends and challenges in today's real-time
analytics
● How StarRocks solves the challenges of
real-time analytics
● Case studies of how StarRocks is being
used for real-time analytics
3. Community User Quote
“Real-time analytics is like trying to drink
from a fire hose. The data is constantly
flowing, and it can be difficult to keep up.
But if you can do it, the insights you gain can
be invaluable.”
Profiting off of near real time knowledge - Battle of Waterloo, Nathan Rothschild and London Stock Exchange.
4. Trends in real-time analytics
1 Make better decisions 2 Increased user
engagement
3
Staying ahead of the
competition
Real-time analytics is the process of collecting, processing, and analyzing data as it is
generated, in order to gain insights into the present state of a system or process. This can lead
to a number of benefits, such as:
Key trends in real-time analytics:
Rise of streaming data Growth of Edge Computing Increasing use of machine
learning
Democratization of real-time
analytics
5. Trends in OLAP databases
1 Cloud Native 2
A
Sub-second vs. Second/Minute
Query Response Time
3
Data Warehouse vs. Data Lake vs.
Data Lakehouse
Online analytical processing (OLAP) databases are evolving rapidly to meet the demands of
modern data analytics. Here are some of the key trends in OLAP databases:
2
B Streaming vs. Batch Data
2
C Mutable vs. Immutable Data
2
D
Remote (Object) Storage vs. Local
(SSD) Storage
2
E
Open Table Format vs. Product
Native Storage Format
6. Proprietary / Hybrid Open
Open Storage
Trends in OLAP databases
Compute
Table Format
Storage Format
Open Lakehouse vs Proprietary / Hybrid Lakehouse
7. Challenges in real-time analytics
1
Ingestion speed and data updates:
Real-time analytics requires high-quality
data that is available in real time. The logic
and mathematics required to handle
unbounded data sets is different than static.
2
Query Response Time: Real-time analytics
needs to be able to deliver insights with
sub-second response times versus seconds
or minutes long query times.
3
Scalability: Real-time analytics solutions
need to be scalable to handle large volumes
of data and high concurrency. Performance
and cost are always put to the test as a data
set grows over time.
4
Cost: Real-time analytics solutions can be
expensive to implement and maintain.
Real-Time Analytics (RTA) is a powerful tool that can help businesses deliver insights to their
users, but it is not without its challenges. Here are some of the key challenges with RTA:
9. StarRocks was designed to address the challenges of
real-time analytics, including the need to support high
concurrency, low latency, a wide range of analytical
workloads.
StarRocks enables:
● Directly query data on data lake and get data warehouse performance.
● Sub-second joins and aggregations on billions of rows.
● Serving hundreds of thousands of concurrent end-user requests.
● No need for external data transformation tool for denormalization or pre-aggregation
● Compatibility with standard sql protocol and Trino dialect.
● Perform significantly more queries per second at lower latency on less hardware.
● Increase the flexibility and capability of your data lake or analytics system.
● Reduced cost and complexity by eliminating the need for costly data transformation pipelines.
10. Low Latency Queries Solution
Sub-second query with thousands of QPS and beyond
11. High Concurrency
Sub-second query with thousands of QPS and beyond
IO Bound Workload
● Less complex queries such as point
queries.
● Very High QPS.
● Typically requires Latency in the low 10s
of ms on massive amount of data
(billions of rows).
CPU Bound Workload
● Complex OLAP-style queries with JOINs
and AGGs.
● Interactive analytics.
● Reports, dashboards, etc.
12. High Concurrency Solution
IO Bound Workload
Scan the least amount of data possible
1. Bucketing: Fine-grained control over how the
data is distributed in your cluster.
● Avoid data skew.
● Choose a high cardinality column that
often appears in your `WHERE` clauses.
2. Indexing: Use prefix index (order key), Bitmap,
Bloom Filter index to minimize disk access.
3. IO-optimized Hardware:
● Configure (multiple) high-performance
SSD drives.
● Good network in terms of latency and
throughput.
CPU Bound Workload
Pre-computation or reusing previous results to take load off
your CPUs
1. Intelligent Caching:
● Final Result Cache: For the folks who keep
hitting the same button just for fun.
● Query Cache (Intermediate Result Cache):
Reuse partial results even if the queries are not
identical.
1. Pre-computation:
● Denormalization through Column Partial
Update.
● Pre-aggregation through MVs and Aggregate
Table.
● Generated Column (3.1): Materialize a column.
13. Fresh Mutable Data Mutable data is important!!
Existing solutions relies on merge on read – Huge
compromise on query performance.
14. Fresh Mutable Data Solution
Delete and insert mechanism with Primary Key index.
Support Real-Time mutable data with no compromise on query
performance.
Approach Pros Cons
Delete and insert
Simple, efficient for
read-heavy workloads
Can be inefficient for
write-heavy workloads
Merge on read
Can be efficient for
write-heavy workloads
More complex than delete
and insert
15. Resource Isolation
Why Resource Isolation is Needed:
● Maintaining Business-Critical Operations: Uninterrupted and undisturbed performance for key tasks.
● Quality of Service Assurance: Resource assurance for varying user groups in a multi-tenant environment.
Current Approach: Physical Isolation:
● Pros:
○ Effective isolation of resources.
● Cons:
○ Low Hardware Utilization: Results in underutilized resources due to lack of sharing or
overprovisioning.
○ High Costs: Need to meet peak demand of each user group leads to idle resources and escalated
costs, especially in large-scale operations.
○ Gets worse when you grow to tens of thousands of tenants.
16. Resource Isolation Solution
Resource Group: Support over-provisioning CPU resources.
Three types of workloads defined:
● Short query: Time sensitive queries with dedicated resource (no over-provisioning).
● Query queue: Queries that are not time sensitive but are critical (cannot be killed).
● The rest: supports customized rules to kill big queries.
17. StarRocks was designed to address the
challenges of real-time analytics, including
the need to support high concurrency, low
latency, a wide range of analytical
workloads and offers the ability to query
data directly from data lakes.
18. Reduce the time and cost of
developing data analytics projects.
● No ingestion and data copying.
● Stop paying to denormalize
data that may never be
queried.
● Keep the tools you’ve been
using.
Sub-second query while serve
millions of users.
● Multi-dimensional interactive
analytics through on-the-fly
computations.
● Single source of truth on open
data lake analytics, with no
external system for caching or
pre-computation.
Make real-time decisions in all
business scenarios, especially when
updated data, like in logistics, is
needed.
● Real-time update without
sacrificing query performance.
● Simpler real-time data pipeline
● Ditch stateful stream
processing jobs
(denormalization &
preaggregation) with efficient
on-the-fly computation.
19. Data warehouse query performance on the
data lakehouse with no data copying: Natively
integrates with open data lake including Hive,
Hudi, Iceberg, and Delta Lake.
Ditch Denormalization: Perform joins on
multiple tables with millions of rows in seconds.
No need for external data transformation tool:
Perform on-demand pre-computation (like
denormalization) within StarRocks, eliminating
another processing tool in your data pipeline.
Intelligent Query Planning
● Cost-based optimizer generates
optimized query plan.
● Global runtime filter.
Efficient query execution
● In-memory data shuffling enables fast
and scalable JOIN operation.
● C++ SIMD-optimized columnar storage
and vectorized query executions deliver
the industry's fastest query
performance.
High concurrency
● Built-in Materialized View.
● An Intelligent caching system.
● Secondary Indexes.
Ditch stream processing tools for
denormalization: StarRocks’ multi-table
performance allows you to ditch the rigid and
expensive stateful stream processing jobs for
denormalization and pre-aggregation.
Real-time updates: Through primary key table,
support real-time data updates while having no
impact on query performance.
Real-time Query Performance at scale: The
synchronized materialized view (MV) further
accelerates aggregated queries at scale for
real-time analytics.
20. Benchmark StarRocks Offers 2.2x Performance over ClickHouse and 8.9x
Performance over Apache Druid® in Wide-table Scenarios Out
of the Box using product native table format.
21. Benchmark StarRocks Delivers 5.54x Query Performance over Trino in
Multi-table Scenarios using Apache Iceberg table format with
Parquet files.
22. Use Case: Tableau
Dashboard at Airbnb
The Airbnb Tableau Dashboard project is designed to serve both
internal and external users by providing interactive dashboards. It
requires a quick response to user queries. However, the query
latency of previous solutions is over 10 mins, which is not
acceptable. This project was just suspended until StarRocks is
adopted.
StarRocks Solution:
● StarRocks can directly connect and works very well with
Tableau.
● 3 tables (0.5B rows, 6B rows, 100M rows) + 4 joins + 3
distinct count + JSON functions and regex at same time,
response time just 3.6s.
● Reduce the query response time from mins level to
sub-seconds level.
23. Use Case: Game and
User Behavior Analytics
at Tencent IEG
● 400+ game data analysis and user behavior analysis
● Operation reports need to be real-time.
● Using ClickHouse for real-time analysis and Trino for
Ad-hoc before, but they want to integrate them all.
● Using Iceberg + COS store, need better performance.
● Need elastic in ad-hoc query to deduce cost.
StarRocks Solution:
● Using StarRocks Primary key to solve update problem.
● Using compute node on k8s to auto-scaling.
● Get much more performance in ad-hoc query.
24. Use Case: Trust
Analytics at Airbnb
To enhance security, Airbnb needs a real-time fraud detection
system (Trust Analytics) to identify various attacks and take
actions ASAP. This system must support Ad-Hoc query and
real-time update.
StarRocks Solution:
● StarRocks hosts real-time updated datasets via Primary
Key.
● Dataset import from Kafka has a sub-minute delay.
● StarRocks provides second-level query latency for
complex joins.
● Alerting can be achieved by just running a SQL query
regularly.
25. History of StarRocks and CelerData
StarRocks was designed to address the challenges of real-time analytics, including the need to support
high concurrency, low latency, and a wide range of analytical workloads. StarRocks also offers a number
of features that are not available in other real-time analytics databases, such as the ability to query data
directly from data lakes.
2020
Birth of StarRocks
StarRocks is created as a commercialized fork of the
Apache Doris database. Over time, 90% of the
original codebase has been re-written.
2022
CelerData is founded
CelerData is founded as a company to develop and
commercialize StarRocks.
2023
StarRocks moves to Linux Foundation
CelerData contributes StarRocks to the Linux
Foundation and moves to Apache 2.0 license.
2023
CelerData Cloud Launched
CelerData launches its managed cloud service for
StarRocks.
2023
Benchmarks outperform competition
Latest TPC-DS and SSB benchmarks shows 2x-9x
speed performance over Trino, Clickhouse and
Apache Druid.