Amazon Redshift Re-invented

Armenatzoglou, Nikos; Basu, Sanuj; Bhanoori, Naga; Cai, Mengchu; Chainani, Naresh; Chinta, Kiran; Govindaraju, Venkatraman; Green, Todd; Gupta, Monish; Hillig, Sebastian; Hotinger, Eric; Leshinksy, Yan; Jin-tian, Liang; McCreedy, Michael; Nagel, Fabian; Pandis, Ippokratis; Parchas, Panos; Pathak, Rahul; Polychroniou, Orestis; Rahman, Foyzur; Saxena, Gaurav; Soundararajan, Gokul; Subramanian, Sriram; Terry, Douglas B.

doi:10.1145/3514221.3526045

Cited by 32 publications

(2 citation statements)

References 13 publications

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“…There are a lot of elastic data warehousing frameworks among which Amazon Redshift is the one [24]. It can be defined as a framework that is responsible for maintaining scalable data within the Amazon Web Services ecosystem.…”

Section: Amazon Redshiftmentioning

confidence: 99%

Elastic Data Warehousing: Adapting To Fluctuating Workloads With Cloud-Native Technologies

Ahmadi

2023

JKLST

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This research focuses on the development of elastic data warehousing while adapting to changing workloads with the help of cloud-based technologies. The traditional methods of data warehousing need innovative and creative strategies in order to improve their efficiency. Thus, this research focuses on analyzing innovative methods which can improve the future of data warehousing, such as machine learning, encryption, artificial intelligence, etc. Moreover, the study also focuses on specific industries that require customized solutions to data warehousing. These include the manufacturing, finance, and healthcare industries. The study uses qualitative data gained through a comprehensive review of literature. The findings reveal a great level of significance of modern data warehousing techniques that assist in improving the overall efficiency of traditional methods

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Section: Amazon Redshiftmentioning

confidence: 99%

Elastic Data Warehousing: Adapting To Fluctuating Workloads With Cloud-Native Technologies

Ahmadi

2023

JKLST

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“…Another trend in cloud data warehousing is the disaggregation of storage and compute, where the data is stored on distributed cloud object stores such as S3, and where compute power can be spawned elastically on demand. This architecture was pioneered by BigQuery and Snowflake and even systems that initially started with a horizontally partitioned, shared-nothing design like Redshift are transitioning to disaggregated storage [24].…”

Section: Introductionmentioning

confidence: 99%

BtrBlocks: Efficient Columnar Compression for Data Lakes

Maximilian

Sauerwein

Alhomssi

et al. 2023

Proc. ACM Manag. Data

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Analytics is moving to the cloud and data is moving into data lakes. These reside on object storage services like S3 and enable seamless data sharing and system interoperability. To support this, many systems build on open storage formats like Apache Parquet. However, these formats are not optimized for remotely-accessed data lakes and today's high-throughput networks. Inefficient decompression makes scans CPU-bound and thus increases query time and cost. With this work we present BtrBlocks, an open columnar storage format designed for data lakes. BtrBlocks uses a set of lightweight encoding schemes, achieving fast and efficient decompression and high compression ratios.

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