Real Time API delivering data @ Scale
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This post talks about various architectural decision and their driving reasons which was taken to build an REST API which need to deliver large amount of reporting data.
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Data Con LA 2020
Description
Apache Druid is a cloud-native open-source database that enables developers to build highly-scalable, low-latency, real-time interactive dashboards and apps to explore huge quantities of data. This column-oriented database provides the microsecond query response times required for ad-hoc queries and programmatic analytics. Druid natively streams data from Apache Kafka (and more) and batch loads just about anything. At ingestion, Druid partitions data based on time so time-based queries run significantly faster than traditional databases, plus Druid offers SQL compatibility. Druid is used in production by AirBnB, Nielsen, Netflix and more for real-time and historical data analytics. This talk provides an introduction to Apache Druid including: Druid's core architecture and its advantages, Working with streaming and batch data in Druid, Querying data and building apps on Druid and Real-world examples of Apache Druid in action
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Matt Sarrel, Imply Data, Developer Evangelist
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Organisations are becoming Event Driven based on streaming technologies and adopting Data Mesh and Event Mesh architectures. As this becomes pervasive, so do the challenges around runtime governance and lifecycle management. For example, do you know what streams exist, who is producing and consuming them? What is the effect of upstream changes? How is this information kept up to date, and how do people collaborate efficiently across distributed teams and environments? Ever wish you had a way to view and visualize graphically the relationships between schemas, topics and applications? In this talk we will show you how to do that and get more value from your Kafka Streaming infrastructure using an Event Portal - an API portal specialized for event streams and publish/subscribe patterns. Join us to see how you can discover event streams from your Kafka clusters, import them to a catalog to see alongside other enterprise event streams and leverage code gen capabilities to ease development.
[WSO2Con EU 2017] Open Interoperability of WSO2 Analytics Platform
This document discusses how WSO2's analytics platform meets key expectations for interoperability. It outlines the typical components of an analytics solution, including collecting data from various sources using different protocols and formats, analyzing the data through integration with existing data stores and models, and communicating results through multiple transports and formats for alerting and storage. The document then provides examples of real-world use cases demonstrating interoperability in areas like receiving data from different sources, integrating with existing systems and data stores, and extending capabilities. Overall, the document promotes WSO2's analytics platform as being interoperable through its ability to easily integrate at various steps of the analytics process.
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Van Oord, a 150 year old family owned business, build windmill parks in the sea, lay cables on sea surface, dredging, as well as infrastructure (Dike, etc) operates world-wide, often facilitating self-owned specialized vessels. A well-known prestigious project is the creation of the palm island at the coast of Dubai.
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Data owners are publishing an information product, i.e. a set of Kafka topics to communicate change (a la CDC) and topics for sharing state of a data source (Kafka tables). The information product owner is responsible for granting access, assuring data quality, data linage and governance. The set of all information products forms the enterprise data model.
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The document summarizes Patrick Di Loreto's presentation on modernizing a data platform with microservices and fast data. Some key points:
- The platform processes large amounts of data (160TB daily) in real-time from various sources to support millions of simultaneous customers.
- Omnia is the distributed data management platform built on reactive principles with Chronos, Fates, NeoCortex and Hermes layers to ingest, store, process and serve data.
- Chronos collects streaming data and stores it in Kafka. Fates builds timelines and views using batch processing. NeoCortex performs real-time analytics using Spark, Akka streams or lambdas. Hermes serves the data
Server Sent Events using Reactive Kafka and Spring Web flux | Gagan Solur Ven...
Server-Sent Events (SSE) is a server push technology where clients receive automatic server updates through the secure http connection. SSE can be used in apps like live stock updates, that use one way data communications and also helps to replace long polling by maintaining a single connection and keeping a continuous event stream going through it. We used a simple Kafka producer to publish messages onto Kafka topics and developed a reactive Kafka consumer by leveraging Spring Webflux to read data from Kafka topic in non-blocking manner and send data to clients that are registered with Kafka consumer without closing any http connections. This implementation allows us to send data in a fully asynchronous & non-blocking manner and allows us to handle a massive number of concurrent connections. We’ll cover:
•Push data to external or internal apps in near real time
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•Handle multiple third-party apps integrations
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The technology world is rapidly moving towards microservices and there are well documented best practices on how to do so. However, data persistence has always been a challenge for most brownfield or greenfield microservices initiatives. Concepts such as ACID properties need to be considered when moving to a decentralized model. Data consistency is often a challenge that affects the overall service. This presentation takes a pragmatic look at a decentralized data architecture and how it aides a move towards a true microservices model. We also look at some of the latest data initiatives such as streaming data for persistence
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This document discusses testing event-driven architectures. It begins by defining common event-driven architecture patterns like event notifications and event sourcing. It then discusses brokering the complexity of event-driven architectures by describing how events are communicated between producers and consumers via channels. The document outlines what information should be included in events like payloads and headers. It also discusses the difference between orchestration and choreography in event-driven systems. It provides an example of how events can be used to mediate changes within a system using order validation. Finally, it demonstrates how to test event-driven architectures using specifications and discusses accelerating API quality through testing tools that support multiple protocols and definitions.
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(Dmitry Milman + Ankur Kaneria, Express Scripts) Kafka Summit SF 2018
Building cloud-based microservices can be a challenge when the system of record is a relational database residing on an on-premise mainframe. The challenge lies in the ability to efficiently and cost-effectively access the ever-increasing amount of data. Express Scripts is reimagining its data architecture to bring best-in-class user experience and provide the foundation of next-generation applications.
This talk will showcase how Kafka plays a key role within Express Scripts’ transformation from mainframe to a microservice-based ecosystem, ensuring data integrity between two worlds. It will discuss how change data capture (CDC) is leveraged to stream data changes to Kafka, allowing us to build a low-latency data sync pipeline. We will describe how we achieve transactional consistency by collapsing all events that belong together onto a single topic, yet have the ability to scale out to meet the real time SLAs and low-latency requirements through means of partitions. We will share our Kafka Streams configuration to handle the data transformation workload. We will discuss our overall Kafka cluster footprint, configuration and security measures.
Express Scripts Holding Company is an American Fortune 100 company. As of 2018, the company is the 25th largest in the U.S. as well as one of the largest pharmacy benefit management organizations in the U.S. Customers rely on 24/7 access to our services, and need the ability to interact with our systems in real time via various channels such as web and mobile. Sharing our mainframe t0 microservices migration journey, our experiences and lessons learned would be beneficial to other companies venturing on a similar path.
Hybrid Streaming Analytics for Apache Kafka Users | Firat Tekiner, Google
The data that organizations are required to analyze in order to make informed decisions is growing at an unprecedented rate. Companies have to capture the window of opportunity and become not just data driven, but event driven. In this talk, we will talk around addressing these issues and look into ways to bridge the on-premise kafka deployments with GCP stack for different use cases and personas. This will be followed by architecture examples on How do you deploy kafka and integrate with the rest of the GCP stack.
Accelerating Innovation with Apache Kafka, Heikki Nousiainen | Heikki Nousiai...
Being a pioneer in the interactive gaming industry, SONY PlayStation has played a vital role in implementing technological advancements thus help bringing global video gaming community together. With the recent launch of next generation console PS-5 into the market by partnering with thousands of game developers and millions of video gamers across the globe, humongous volumes of data generation in playstation servers is quite inevitable. This presentation talks about how we leveraged big data technologies along with Apache Kafka to solve some of the realtime data analytical problems. Two important case studies we carryout recently are: ""Competitive pricing analysis of game titles across online video game marketplaces"" & ""understand the gamers sentiment by streaming data from social feeds and perform NLP""
Along with Apache Kafka, the technologies that we have used to architect the solution are: REST API, ZooKeeper, D3.js visualization, DoMo, Python, SQL, NLP, AWS Cloud & JSON.
Why Kafka Works the Way It Does (And Not Some Other Way) | Tim Berglund, Conf...
Studying the ""how"" of Kafka makes you better at using Kafka, but studying its ""whys"" makes you better at so much more. In looking at the tradeoffs behind a system like Kafka, we learn to reason more clearly about distributed systems and to make high-stakes technology adoption decisions more effectively. These are skills we all want to improve!
In this talk, we'll examine trade-offs on which our favorite distributed messaging system takes opinionated positions:
- Whether to store data contiguously or using an index
- How many storage tiers are best?
- Where should metadata live?
- And more.
It's always useful to dissect a modern distributed system with the goal of understanding it better, and it's even better to learn to deeper architectural principles in the process. Come to this talk for a generous helping of both.
Event-Driven Microservices with Apache Kafka, Kafka Streams and KSQL
Building Event-Driven Microservices with Stateful Streams - Apache Kafka, Kafka Streams, KSQL, and more…
Event Driven Services come in many shapes and sizes from tiny event-driven functions that dip into an event stream, right through to heavy, stateful services which can facilitate request response. This talk makes the case for building this style of system using Stream Processing tools, defining a microservices architecture and leveraging Apache Kafka ecosystem including Kafka Streams and KSQL. We also walk through a number of patterns for how we actually put these things together to enable independent teams and autonomous development of microservices.
Kudos to my colleagues Ben and Jay who created many of the slides.
SnapLogic- iPaaS (Elastic Integration Cloud and Data Integration)
Especially this document provide very useful and meaningful concepts about SnapLogic. Also this document will be more useful for beginner/intermediate level SnapLogic learners.
Kafka Summit NYC 2017 - The Rise of the Streaming Platform
The document discusses the rise of streaming platforms and Apache Kafka. It describes how Fortune 500 companies and major global banks, insurance, and telecom companies are adopting streaming platforms. It then discusses the technical capabilities of streaming platforms, including their abilities to store, process, and publish/subscribe to data in real-time at large scales. Finally, it envisions the future of streaming platforms and their potential to support a wide range of applications from databases and key-value stores to monitoring, search, data warehousing, Hadoop, stream processing, and real-time analytics on a single, open platform.
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Defining Asynchronous APIs and sharing them with your developer community is the most effective way for internal app developers and partners to create new services using real-time event streams. But how do you do it? What specification do you use to define the APIs? What are the best practices for sharing them with the developer community? What framework can you use to code? And what’s next? How do you manage the lifecycle of these APIs? In this talk, Fran Mendez, founder of AsyncAPI and Jonathan Schabowsky, Solace CTO Architect will introduce you to the AsyncAPI specification and show you two different methods to define and share your event APIs, quickly get up to speed, and more. You will learn how to create a Kafka application using asynchronous APIs in minutes!
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Data Mess to Data Mesh | Jay Kreps, CEO, Confluent | Kafka Summit Americas 20...
Companies are increasingly becoming software-driven, requiring new approaches to software architecture and data integration. The "data mesh" architectural pattern decentralizes data management by organizing it around domain experts and treating data as products that can be accessed on-demand. This helps address issues with centralized data warehouses by evolving data modeling with business needs, avoiding bottlenecks, and giving autonomy to domain teams. Key principles of the data mesh include domain ownership of data, treating data as self-service products, and establishing federated governance to coordinate the decentralized system.
IIoT with Kafka and Machine Learning for Supply Chain Optimization In Real Ti...
I did a webinar with Confluent's partner Expero about "Apache Kafka and Machine Learning for Real Time Supply Chain Optimization". This is a great example for anybody in automation industry / Industrial IoT (IIoT) like automotive, manufacturing, logistics, etc.
We explain how a real time event streaming platform can integrate in real time with the legacy world and proprietary IIoT protocols (like Siemens S7, Modbus, Beckhoff ADS, OPC-UA, et al). You can process the data at scale and then ingest it into a modern database (like AWS S3, Snowflake or MongoDB) or analytic / machine learning framework (like TensorFlow, PyTorch or Azure Machine Learning Service).
What's hot (20)
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[WSO2Con EU 2017] Open Interoperability of WSO2 Analytics Platform
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What does an event mean? Manage the meaning of your data! | Andreas Wombacher...
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[WSO2Con EU 2018] Decentralized Data Architectures
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Scalable Data Management for Kafka and Beyond | Dan Rice, BigID
Monitoreo sencillo de la infraestructura, de la ingesta a la visualización
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Testing Event Driven Architectures: How to Broker the Complexity | Frank Kilc...
Digital Transformation in Healthcare with Kafka—Building a Low Latency Data P...
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Accelerating Innovation with Apache Kafka, Heikki Nousiainen | Heikki Nousiai...
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Why Kafka Works the Way It Does (And Not Some Other Way) | Tim Berglund, Conf...
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Event-Driven Microservices with Apache Kafka, Kafka Streams and KSQL
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SnapLogic- iPaaS (Elastic Integration Cloud and Data Integration)
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Kafka Summit NYC 2017 - The Rise of the Streaming Platform
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Data Mess to Data Mesh | Jay Kreps, CEO, Confluent | Kafka Summit Americas 20...
Data Mess to Data Mesh | Jay Kreps, CEO, Confluent | Kafka Summit Americas 20...
IIoT with Kafka and Machine Learning for Supply Chain Optimization In Real Ti...
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Apache Druid 101
Data Con LA 2020
Description
Apache Druid is a cloud-native open-source database that enables developers to build highly-scalable, low-latency, real-time interactive dashboards and apps to explore huge quantities of data. This column-oriented database provides the microsecond query response times required for ad-hoc queries and programmatic analytics. Druid natively streams data from Apache Kafka (and more) and batch loads just about anything. At ingestion, Druid partitions data based on time so time-based queries run significantly faster than traditional databases, plus Druid offers SQL compatibility. Druid is used in production by AirBnB, Nielsen, Netflix and more for real-time and historical data analytics. This talk provides an introduction to Apache Druid including: Druid's core architecture and its advantages, Working with streaming and batch data in Druid, Querying data and building apps on Druid and Real-world examples of Apache Druid in action
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Watch here: https://bit.ly/36tEThx
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InterConnect 2017 : Cognitive DevOps: Get Rid of the Guesswork to Improve Sof...
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Technology and analytics are being used to improve processes in all areas of business and the home. Software development is no exception, and the leading driver of this revolution is DevOps. As organizations shift towards digital transformation and enter the API economy, and connect business-critical z Systems applications with mobile and cloud applications to better engage with clients, the need for tools that can help us understand the application landscape along with evolving trends and actionable insights to streamline the software development and delivery process is greater than ever. Join us to learn how IBM Application Discovery and Delivery Intelligence (ADDI) can help organizations streamline their development process.
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See how organisations have economically accelerated adoption of MongoDB across the enterprise using advanced techniques, tooling and DevOps.
Transforming a Large Mission-Critical E-Commerce Platform from a Relational A...
Speaker: Dharmesh Panchmatia
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The flexibility, scale, services, and pay-as-you-go pricing options provided by modern cloud platforms—Amazon Web Services (AWS), Microsoft Azure, Google Cloud Platform, and tools like Pivotal among them—have completely changed how you architect applications and deploy infrastructure. In order to effectively manage the dynamic natures of these cloud-based applications and infrastructure, the way you monitor—and the tools you use to do so—need to change, as well.
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https://youtu.be/ApvtPNU_XzE
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Big Data Analytics: Reference Architectures and Case Studies by Serhiy Haziye...
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SendGrid Improves Email Delivery with Hybrid Data Warehousing
When you received your Uber ‘Tuesday Evening Ride Receipt’ or Spotify’s ‘This Week’s New Music’ email, did you think about how they got there?
SendGrid’s reliable email platform delivers each month over 20 Billion transactional and marketing emails on behalf of many of your favorite brands, including Uber, Airbnb, Spotify, Foursquare and NextDoor.
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AppSphere 15 - Mining the World’s Largest Healthcare Data Warehouse while Ens...
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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
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Web:- https://undressbaby.com/
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- 1. Akash Mishra Real Time API delivering Data @ Scale
- 2. Agenda API Overview Key System Requirement Big Data System Vs RDBMS Architecture Data Flow Questions?
- 3. API Overview API details REST based API Partners can request for various types of reports Each reports has data in order of T.B's Sample Request ?start-date=2012-10-01&end-date=2012-10- 29&partner=1&aggregate-by=state,city Response Zip file [Size in order of 10-30 M.B]
- 4. Key System Requirement Interactive Filtering Query – Partner can filter data on various parameter. Real Time Response – SLA of 1-3 min. Security Extremely private and confidential data. Need to go through an audit by external vendor Scalability Only more machine for more customer
- 5. Big Data System Vs Relational Data System Large Amount of Data [In order of T.B's ] Hadoop/Hive RDBMS Real Time Interactive Filtering/Querying Hadoop/Hive RDBMS Join's between large tables [ millions X millions X millions ] – Hadoop/Hive – RDBMS
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- 7. Architecture
- 8. Data Flow De-normalization on Hadoop/Hive Time: 3hrs #Records: 230m
- 9. Data Flow Dynamic partitioning on Hadoop/Hive # Buckets 15 #Records: 230m
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- 13. Questions???