Sat.Aug 01, 2020 - Fri.Aug 07, 2020

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How Tencent PCG Uses Apache Kafka to Handle 10 Trillion+ Messages Per Day

Confluent

As one of the world’s biggest internet-based platform companies, Tencent uses technology to enrich the lives of users and assist the digital upgrade of enterprises. An example product is the […].

Kafka 139
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A Practical Introduction To Graph Data Applications

Data Engineering Podcast

Summary Finding connections between data and the entities that they represent is a complex problem. Graph data models and the applications built on top of them are perfect for representing relationships and finding emergent structures in your information. In this episode Denise Gosnell and Matthias Broecheler discuss their recent book, the Practitioner’s Guide To Graph Data, including the fundamental principles that you need to know about graph structures, the current state of graph suppor

NoSQL 100
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Architecting for Today’s Hybrid Analytic Ecosystem

Teradata

A modern analytic ecosystem embraces a hybrid approach and leverages the right technologies to meet the needs at the right cost/value ratio. Read more.

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Building a Sync Engine

Grouparoo

So you have data in your product database and you need to synchronize it with something else. Maybe you need to update a CRM or email system like Mailchimp , HubSpot , or Braze. Maybe it is more of an ETL thing and you need to move the data into Redshift or Snowflake. In all cases, what we have here is a need for a sync engine. A sync engine monitors a source (your product database) for changes in order to process them in some way (update an external system).

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Get Better Network Graphs & Save Analysts Time

Many organizations today are unlocking the power of their data by using graph databases to feed downstream analytics, enahance visualizations, and more. Yet, when different graph nodes represent the same entity, graphs get messy. Watch this essential video with Senzing CEO Jeff Jonas on how adding entity resolution to a graph database condenses network graphs to improve analytics and save your analysts time.

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What’s New in Apache Kafka 2.6

Confluent

On behalf of the Apache Kafka® community, it is my pleasure to announce the release of Apache Kafka 2.6.0. This another exciting release with many new features and improvements. We’ll […].

Kafka 136
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Handling Slow Queries in MongoDB - Part 1: Investigation

Rockset

One of the most essential factors of performance in any application is latency. Faster application response times have been proven to increase user interaction and engagement as systems appear more natural and fluid with lower latencies. As data size, query complexity, and application load increase, continuing to deliver the low data and query latencies required by your application can become a serious pain point.

MongoDB 40

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Build A StackOverflow Dashboard (Part 1): Connecting Superset to BigQuery

Preset

Learn how to connect BigQuery to Superset.

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Announcing ksqlDB 0.11.0

Confluent

We’re pleased to announce ksqlDB 0.11.0, which takes a big step forward toward improved production stability. This is becoming increasingly important as companies like Bolt and PushOwl use ksqlDB for […].

Process 98
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Project Metamorphosis Month 4: Confluent Cloud for Apache Kafka Available Everywhere

Confluent

We launched Project Metamorphosis in April with the goal of making it dramatically easier for organizations to leverage an event streaming platform for their business needs. By reimagining event streaming […].

Project 97
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Announcing AWS PrivateLink Support in Confluent Cloud

Confluent

We’re happy to announce that Confluent Cloud, our fully managed event streaming service powered by Apache Kafka®, now supports AWS PrivateLink for secure network connectivity, in addition to the existing […].

AWS 83
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Understanding User Needs and Satisfying Them

Speaker: Scott Sehlhorst

We know we want to create products which our customers find to be valuable. Whether we label it as customer-centric or product-led depends on how long we've been doing product management. There are three challenges we face when doing this. The obvious challenge is figuring out what our users need; the non-obvious challenges are in creating a shared understanding of those needs and in sensing if what we're doing is meeting those needs.

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Why Object Storage Is Essential for Analytics

Teradata

Companies now have choice as to the storage tiers they can use for analytics. Use these guidelines to pick the right mix of storage tiers for your use case.

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