Alibaba Cloud Data Lake Analytics (DLA) is an end-to-end, on-demand, serverless data lake analytics and computing service. It offers a cost-effective platform to run Extract-Transform-Load (ETL), machine learning, streaming, and interactive analytics workloads. DLA can be used with various data sources, such as Object Storage Service (OSS) and databases.
Data Lake Analytics(DLA) is a serverless cloud-native interactive cloud-native query and analytics service. For enterprise users edition users, the service is made available on billing method is Pay-As-You-Go. Due to its basis serverless architecture, DLA also completely removes the need for server maintenance requirements with ZERO maintenance effort. Key benefits of DLA, include but are not limited to, but not limited to instant startup, transparent upgrades, and elastic Quality of Service (QoS).
DLA offers a standard SQL interface, that has outstanding SQL compatibility and comprehensive built-in functions. DLA employs standard Java Database Connectivity (JDBC) and Open Database Connectivity (ODBC) technologies to provide users quick service access while lowering application transformation and migration costs. Based on JDBC/ODBC connectivity, users are granted fast and convenient service access, and low migration cost. Meanwhile, compatibility with Business Intelligence (BI) tools integration with BI product enables DLA to turn big data into consumable data insights and visualizations. With its strong database experience integration capabilities, DLA helps can help customers accelerate their cloud-based migration data analytics processes.
DLA enables complex analytics analysis of different data sources and formats. Not only can data coming from different sources with various formats. users Not only the user can leverage DLA to analyze data stored on Alibaba cloud Cloud OSS and Table Store respectively, but they can also integrate, they can join these services analysis results and thus generate new data insights.
DLA Fully leverages Massive Parallel Processing (MPP) and Directed Acyclic Graph (DAG) architecture, providing to provide vectorized execution optimization, pipelined operator pipelined execution optimization, multi-tenancy resource allocation resource isolation, and priority scheduling.
You can store time-series data, pipeline data, logs, and post-extract transform, and load (ETL) data on Table Store. DLA can run queries in a single Table Store table or perform association analysis for multiple tables.
You can store time-series data, pipeline data, logs, and post-extract transform, and load (ETL) data on Table Store. DLA can run queries in a single Table Store table or perform association analysis for multiple tables.
DLA can perform association analysis on multiple heterogeneous data sources from OSS and Table Store, and generate multiple data sources into consolidated insights.
Alibaba Cloud Data Lake Analytics (DLA) is an interactive analytics service that utilizes serverless architecture. As a ready-to-use service, DLA does not require any prior setup of infrastructure or upfront management costs. You do not need to maintain instances in DLA, and service is billed based on actual use and needs. DLA uses SQL interfaces to interact with user service clients, which means it complies with standard SQL syntax and provides a variety of similar functions. DLA allows you to retrieve and analyze data from multiple data sources or locations such as OSS and Table Store for optimal data processing, analytics, and visualization to give better insights and ultimately guide better decision making.
Data Lake Analytics (DLA) is an interactive cloud-native query and analytics service. With DLA, you can run queries across multiple data sources with a variety of formats using standard JDBC.
Data Lake Analytics does not require any ETL tools. This service allows you to use standard SQL syntax and business intelligence (BI) tools to efficiently analyze your data stored in the cloud with extremely low costs. Through the introduction and usage of the product, this course enables learners to quickly get started with Data Lake Analytics.
The concept of data lakes has recently become a hot topic. There are currently heated discussions among frontline personnel on the best way to build a data lake. Does Alibaba Cloud have a mature data lake solution? If so, has this solution been applied in actual scenarios? What is a data lake? What are the differences between a data lake and a big data platform? What data lake solutions are provided by major players in the field of cloud computing? This article attempts to answer these questions and provide deep insights into the concept of data lakes. I would like to thank Nanjing for compiling the cases in Section 5.1 of this article and thank Xibi for his review.
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