How to Real-Time Polling

Carl shows a technique using SignalR to let mobile users answer poll questions

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4 Real-Time Data Analytics Predictions for 2021

Data management, analytics, data science, and real-time systems will converge this year enabling new automated and self-learning solutions for real-time business operations.

The global pandemic of 2020 has upended social behaviors and business operations. Working from home is the new normal for many, and technology has accelerated and opened new lines of business. Retail and travel have been hit hard, and tech-savvy companies are reinventing e-commerce and in-store channels to survive and thrive. In biotech, pharma, and healthcare, analytics command centers have become the center of operations, much like network operation centers in transport and logistics during pre-COVID times.

While data management and analytics have been critical to strategy and growth over the last decade, COVID-19 has propelled these functions into the center of business operations. Data science and analytics have become a focal point for business leaders to make critical decisions like how to adapt business in this new order of supply and demand and forecast what lies ahead.

In the next year, I anticipate a convergence of data, analytics, integration, and DevOps to create an environment for rapid development of AI-infused applications to address business challenges and opportunities. We will see a proliferation of API-led microservices developer environments for real-time data integration, and the emergence of data hubs as a bridge between at-rest and in-motion data assets, and event-enabled analytics with deeper collaboration between data scientists, DevOps, and ModelOps developers. From this, an ML engineer persona will emerge.

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How can I create a Poll on Facebook?

How do I start or create or post a Poll on Facebook? Know the ways to add options or make a poll on Facebook Page or Messenger.

make a poll on Facebook
add options to Facebook Poll

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Apache Hudi: How Uber Gets Data a Ride to its Destination

Apache Hudi provides tools to ingest data into HDFS or cloud storage, and is designed to get data into the hands of users and analysts quickly.

At a busy, data-intensive enterprise such as Uber, the volumes of real-time data that need to move through its systems on a minute-by-minute basis reaches epic proportions. This calls for a data lake extraordinaire, in which data can immediately be extracted and leveraged across a range of functions, from back-end business applications to front-end mobile apps. Uber depends on up-to-the-minute bookings and alerts as part of its appeal to customers, so its reliance on real-time data streaming platforms is off-the-charts. It has turned to Apache Hudi, an emerging platform that brings stream processing to big data, providing fresh data while being an order of magnitude efficient over traditional batch processing.

I recently had the opportunity to moderate a webcast about Apache Hudi with Nishith Agarwal and Sivabalan Narayanan, both engineers with Uber. Both Agarwal and Narayanan are active members of the Hudi programming committee.

The Hudi data lake project was originally developed at Uber in 2016, open-sourced in 2017, and submitted to the Apache Incubator in January 2019. Apache Hudi data lake technology enables stream processing on top of Apache Hadoop compatible cloud stores and distributed file systems. The solution provides tools to ingest data onto HDFS or cloud storage, as well as provide an incremental approach to resource-intensive ETL, Hive, or Spark jobs. It is designed to get data into the hands of users and analysts much quicker.

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