Remove Data Analytics Remove Data Warehouse Remove Deep Learning
article thumbnail

Differentiating Between Data Lakes and Data Warehouses

Smart Data Collective

The market for data warehouses is booming. While there is a lot of discussion about the merits of data warehouses, not enough discussion centers around data lakes. We talked about enterprise data warehouses in the past, so let’s contrast them with data lakes. Data Warehouse.

Data Lake 135
article thumbnail

The DataOps Vendor Landscape, 2021

DataKitchen

DataOps needs a directed graph-based workflow that contains all the data access, integration, model and visualization steps in the data analytic production process. It orchestrates complex pipelines, toolchains, and tests across teams, locations, and data centers. Amaterasu — is a deployment tool for data pipelines.

Testing 304
Insiders

Sign Up for our Newsletter

This site is protected by reCAPTCHA and the Google Privacy Policy and Terms of Service apply.

article thumbnail

Building a Beautiful Data Lakehouse

CIO Business Intelligence

Applying artificial intelligence (AI) to data analytics for deeper, better insights and automation is a growing enterprise IT priority. But the data repository options that have been around for a while tend to fall short in their ability to serve as the foundation for big data analytics powered by AI.

Data Lake 119
article thumbnail

Your 5-Step Journey from Analytics to AI

CIO Business Intelligence

One option is a data lake—on-premises or in the cloud—that stores unprocessed data in any type of format, structured or unstructured, and can be queried in aggregate. Another option is a data warehouse, which stores processed and refined data. Set up unified data governance rules and processes.

Analytics 115
article thumbnail

Become More Data-Driven by Evolving Analytics Workloads

CIO Business Intelligence

Organizations are increasingly trying to grow revenue by mining their data to quickly show insights and provide value. In the past, one option was to use open-source data analytics platforms to analyze data using on-premises infrastructure. Cloudera and Dell Technologies for More Data Insights.

article thumbnail

Amazon Redshift: Lower price, higher performance

AWS Big Data

times better price-performance than other cloud data warehouses on real-world workloads using advanced techniques like concurrency scaling to support hundreds of concurrent users, enhanced string encoding for faster query performance, and Amazon Redshift Serverless performance enhancements. Amazon Redshift delivers up to 4.9

article thumbnail

The Reason Many AI and Analytics Projects Fail—and How to Make Sure Yours Doesn’t

CIO Business Intelligence

And modern object storage solutions, offer performance, scalability, resilience, and compatibility on a globally distributed architecture to support enterprise workloads such as cloud-native, archive, IoT, AI, and big data analytics. Protecting the data : Cyber threats are everywhere—at the edge, on-premises and across cloud providers.

Analytics 137