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Description

Numerous customers of Amazon Web Services (AWS) seek a data storage and analytics solution that surpasses the agility and flexibility of conventional data management systems. A data lake has emerged as an innovative and increasingly favored method for storing and analyzing data, as it enables organizations to handle various data types from diverse sources, all within a unified repository that accommodates both structured and unstructured data. The AWS Cloud supplies essential components necessary for customers to create a secure, adaptable, and economical data lake. These components comprise AWS managed services designed to assist in the ingestion, storage, discovery, processing, and analysis of both structured and unstructured data. To aid our customers in constructing their data lakes, AWS provides a comprehensive data lake solution, which serves as an automated reference implementation that establishes a highly available and cost-efficient data lake architecture on the AWS Cloud, complete with an intuitive console for searching and requesting datasets. Furthermore, this solution not only enhances data accessibility but also streamlines the overall data management process for organizations.

Description

Traditional Data Lakes frequently simplify their role to merely serving as inexpensive raw data repositories, overlooking crucial elements such as data transformation, quality assurance, and security protocols. Consequently, data scientists often find themselves dedicating as much as 80% of their time to the processes of data acquisition, comprehension, and cleansing, which delays their ability to leverage their primary skills effectively. Furthermore, the establishment of traditional Data Lakes tends to occur in isolation by various departments, each utilizing different standards and tools, complicating the implementation of cohesive analytical initiatives. In contrast, Smart Data Lakes address these challenges by offering both architectural and methodological frameworks, alongside a robust toolset designed to create a high-quality data infrastructure. Essential to any contemporary analytics platform, Smart Data Lakes facilitate seamless integration with popular Data Science tools and open-source technologies, including those used for artificial intelligence and machine learning applications. Their cost-effective and scalable storage solutions accommodate a wide range of data types, including unstructured data and intricate data models, thereby enhancing overall analytical capabilities. This adaptability not only streamlines operations but also fosters collaboration across different departments, ultimately leading to more informed decision-making.

API Access

Has API

API Access

Has API

Screenshots View All

Screenshots View All

Integrations

Amazon S3
Apache Kafka
Apache Spark
Hadoop
Java
Python
Rocket Data Replicate & Sync
SQL
Scala
VE3 DataWise

Integrations

Amazon S3
Apache Kafka
Apache Spark
Hadoop
Java
Python
Rocket Data Replicate & Sync
SQL
Scala
VE3 DataWise

Pricing Details

No price information available.
Free Trial
Free Version

Pricing Details

Free
Free Trial
Free Version

Deployment

Web-Based
On-Premises
iPhone App
iPad App
Android App
Windows
Mac
Linux
Chromebook

Deployment

Web-Based
On-Premises
iPhone App
iPad App
Android App
Windows
Mac
Linux
Chromebook

Customer Support

Business Hours
Live Rep (24/7)
Online Support

Customer Support

Business Hours
Live Rep (24/7)
Online Support

Types of Training

Training Docs
Webinars
Live Training (Online)
In Person

Types of Training

Training Docs
Webinars
Live Training (Online)
In Person

Vendor Details

Company Name

Amazon

Founded

1994

Country

United States

Website

aws.amazon.com/solutions/implementations/data-lake-solution/

Vendor Details

Company Name

ELCA Group

Founded

1968

Country

Switzerland

Website

www.elca.ch/en/smart-data-lake-builder-efficient-way-lay-foundations-smarter-data-lake

Product Features

Data Analysis

Data Discovery
Data Visualization
High Volume Processing
Predictive Analytics
Regression Analysis
Sentiment Analysis
Statistical Modeling
Text Analytics

Data Management

Customer Data
Data Analysis
Data Capture
Data Integration
Data Migration
Data Quality Control
Data Security
Information Governance
Master Data Management
Match & Merge

Product Features

Alternatives

Alternatives