article thumbnail

Differentiating Between Data Lakes and Data Warehouses

Smart Data Collective

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. Both data warehouses and data lakes are used when storing big data.

Data Lake 105
article thumbnail

How gaming companies can use Amazon Redshift Serverless to build scalable analytical applications faster and easier

AWS Big Data

A data hub contains data at multiple levels of granularity and is often not integrated. It differs from a data lake by offering data that is pre-validated and standardized, allowing for simpler consumption by users. Data hubs and data lakes can coexist in an organization, complementing each other.

Insiders

Sign Up for our Newsletter

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

article thumbnail

Using Artificial Intelligence to Make Sense of IoT Data

BizAcuity

There is a coherent overlap between the Internet of Things and Artificial Intelligence. IoT is basically an exchange of data or information in a connected or interconnected environment. Data is only useful when it is actionable for which it needs to be supplemented with context and creativity. Future of IoT is AI.

IoT 56
article thumbnail

Quantitative and Qualitative Data: A Vital Combination

Sisense

When these systems connect with external groups — customers, subscribers, shareholders, stakeholders — even more data is generated, collected, and exchanged. The result, as Sisense CEO Amir Orad wrote , is that every company is now a data company. This is quantitative data. Qualitative data benefits: Unlocking understanding.

article thumbnail

Interview with Dominic Sartorio, Senior Vice President for Products & Development, Protegrity

Corinium

And it’s become a hyper-competitive business, so enhancing customer service through data is critical for maintaining customer loyalty. And more recently, we have also seen innovation with IOT (Internet Of Things). In data-driven organizations, data is flowing. But I’ll give an example in favour of each.

Insurance 150
article thumbnail

Big Data Fabric Weaves Together Automation, Scalability, and Intelligence

Cloudera

Today’s data landscape is characterized by exponentially increasing volumes of data, comprising a variety of structured, unstructured, and semi-structured data types originating from an expanding number of disparate data sources located on-premises, in the cloud, and at the edge.

article thumbnail

Building Better Data Models to Unlock Next-Level Intelligence

Sisense

The reasons for this are simple: Before you can start analyzing data, huge datasets like data lakes must be modeled or transformed to be usable. According to a recent survey conducted by IDC , 43% of respondents were drawing intelligence from 10 to 30 data sources in 2020, with a jump to 64% in 2021!