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Ensuring Performance, Efficiency, and Scalability of Digital Transformation

Alex Podelko

And we still have a lot of great sessions covering other hot topics: digital transformation, security, AI, machine learning, blockchain, etc. – Performance engineering as it done at Alibaba – which emerging as a major cloud provider. Meeting of the Minds: Performance Engineering. a Panel Discussion.

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Software Testing Trends 2021 – What can we expect?

Testsigma

The usage by advanced techniques such as RPA, Artificial Intelligence, machine learning and process mining is a hyper-automated application that improves employees and automates operations in a way which is considerably more efficient than conventional automation. Blockchain Tests. billion in 2019 to $40.74 Signup for free now.

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Expanding the Cloud: Introducing the AWS Asia Pacific (Mumbai) Region

All Things Distributed

AdiMap uses Amazon Kinesis to process real-time streaming online ad data and job feeds, and processes them for storage in petabyte-scale Amazon Redshift. Advanced problem solving that connects big data with machine learning. A workflow engine to drive business decisions. We are at the cusp of a dramatic age of technology.

AWS 90
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Structural Evolutions in Data

O'Reilly

” Web3 has similarly progressed through “basic blockchain and cryptocurrency tokens” to “decentralized finance” to “NFTs as loyalty cards.” Each time, the underlying implementation changed a bit while still staying true to the larger phenomenon of “Analyzing Data for Fun and Profit.”

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Even more amazing papers at VLDB 2019 (that I didn’t have space to cover yet)

The Morning Paper

AliGraph covers Alibaba’s distributed graph engine supporting the development of new GNN applications. Their dataset has about 7B edges… Meanwhile, AnalyticDB is Alibaba’s real-time OLAP RDBMS handling 10PB of data (in excess of 100 trillion rows!). .” Could it be Analyzing efficient stream processing on modern hardware ?

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Should You Use ClickHouse as a Main Operational Database?

Percona

However, ClickHouse is super efficient for timeseries and provides “sharding” out of the box (scalability beyond one node). Although such databases can be very efficient with counts and averages, some queries will be slow or simply non existent. Inserts are efficient for bulk inserts only. Processed 8.19