Overview
Cloud computing is one of the most important trends in the current software industry. Cloud possesses unique technical and business characteristics, enabling new approaches to software design and new usage models. In this chapter we present the key characteristics of the cloud systems, and discuss opportunities and challenges traditional database systems face when deployed on this new platform. Using a set of existing systems, we demonstrate various approaches to building cloud-based SQL Big Data solutions.
Cloud
Cloud computing is possibly the largest shift in computing since the client-server model became popular. In recent years, we see companies of all sizes embrace it, often for very different reasons. Architecturally, it introduces a lot of previously unavailable features, that provide amazing opportunities to system and application developers. At the same time, careful (re)design of software is needed to take full advantage of them.
The term “Cloud” tends to be used in...
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References
Amazon EC2: Storage. http://docs.aws.amazon.com/AWSEC2/latest/UserGuide/Storage.html
Amazon Redshift. https://aws.amazon.com/redshift
Amazon Redshift Spectrum. https://aws.amazon.com/redshift/spectrum/
Amazone Athena. https://aws.amazon.com/athena
BigQuery under the hood. https://cloud.google.com/blog/big-data/2016/01/bigquery-under-the-hood
Chen Y et al (2009) Partial join order optimization in the paraccel analytic database In: Proceedings of SIGMOD
Dageville B et al (2016) The snowflake elastic data warehouse In: Proceedings of SIGMOD
DATAllegro. https://en.wikipedia.org/wiki/DATAllegro
Fikes A Storage architecture and challenges. https://cloud. google.com/files/storage_architecture_and_challe nges.pdf
Google BigQuery. https://cloud.google.com/bigquery
Google Cloud Platform: Storage Options. https://cloud.google.com/compute/docs/disks/
Gupta A et al (2015) Amazon redshift and the case for simpler data warehouses In: Proceedings of SIGMOD
IBM dashDB. https://www.ibm.com/ms-en/marketplace/cloud-data-warehouse
Introducing Snowflake. https://www.snowflake.net/pro duct/
Introduction to Microsoft Azure Storage. https://docs.microsoft.com/en-us/azure/storage/common/storage-introduction
Magic Quadrant for Cloud Infrastructure as a Service, Worldwide. https://www.gartner.com/doc/reprints?id=1-2G2O5FC&ct=150519
Melnik S, Gubarev A, Long JJ, Romer G, Shivakumar S, Tolton M, Vassilakis T (2010) Dremel: interactive analysis of web-scale datasets. In: Proceedings of VLDB
Microsoft Azure SQL Data Warehouse. https://azure.microsoft.com/en-us/services/sql-data-warehouse
Oracle Autonomous Data Warehouse Cloud. https://cloud.oracle.com/en_US/datawarehouse
Parallel Data Warehouse overview. https://docs.microsoft.com/en-us/sql/analytics-platform-system/parallel-data-warehouse-overview
Pivotal Greenplum on Amazon Web Services. https://pivotal.io/partners/aws/pivotal-greenplum
Stonebraker M (1985) The case for shared nothing. In: Proceedings of HPTS
Teradata IntelliCloud. https://www.teradata.com/products-and-services/intellicloud
Whats New in Vertica 9.0: Eon Mode Beta. https://my.vertica.com/blog/whats-new-vertica-9-0-eon-mode-beta
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Zukowski, M. (2018). Cloud-based SQL Solutions for Big Data. In: Sakr, S., Zomaya, A. (eds) Encyclopedia of Big Data Technologies. Springer, Cham. https://doi.org/10.1007/978-3-319-63962-8_318-1
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DOI: https://doi.org/10.1007/978-3-319-63962-8_318-1
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