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Rapidminer Platform Supports Entire Data Science Lifecycle

David Menninger's Analyst Perspectives

Rapidminer is a visual enterprise data science platform that includes data extraction, data mining, deep learning, artificial intelligence and machine learning (AI/ML) and predictive analytics. Rapidminer Studio is its visual workflow designer for the creation of predictive models.

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Understanding the Differences Between Data Lakes and Data Warehouses

Smart Data Collective

Data lakes and data warehouses are probably the two most widely used structures for storing data. Data Warehouses and Data Lakes in a Nutshell. A data warehouse is used as a central storage space for large amounts of structured data coming from various sources. Data Type and Processing.

Data Lake 140
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7 Key Benefits of Proper Data Lake Ingestion

Smart Data Collective

Perhaps one of the biggest perks is scalability, which simply means that with good data lake ingestion a small business can begin to handle bigger data numbers. The reality is businesses that are collecting data will likely be doing so on several levels. Proper Scalability. Stores in Raw Format. Uses Powerful Algorithms.

Data Lake 131
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MLOps and DevOps: Why Data Makes It Different

O'Reilly on Data

Let’s start by considering the job of a non-ML software engineer: writing traditional software deals with well-defined, narrowly-scoped inputs, which the engineer can exhaustively and cleanly model in the code. Not only is data larger, but modelsdeep learning models in particular—are much larger than before.

IT 364
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Interview with: Sankar Narayanan, Chief Practice Officer at Fractal Analytics

Corinium

Some of the work is very foundational, such as building an enterprise data lake and migrating it to the cloud, which enables other more direct value-added activities such as self-service. It is also important to have a strong test and learn culture to encourage rapid experimentation.

Insurance 250
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Building a Beautiful Data Lakehouse

CIO Business Intelligence

However, they do contain effective data management, organization, and integrity capabilities. As a result, users can easily find what they need, and organizations avoid the operational and cost burdens of storing unneeded or duplicate data copies. Warehouse, data lake convergence. Meet the data lakehouse.

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Build a semantic search engine for tabular columns with Transformers and Amazon OpenSearch Service

AWS Big Data

Finding similar columns in a data lake has important applications in data cleaning and annotation, schema matching, data discovery, and analytics across multiple data sources. The workflow begins with an AWS Glue job that converts the CSV files into Apache Parquet data format.

Data Lake 105