AWS Big Data

JULY 28, 2023

Data mapping involves identifying and documenting the flow of personal data in an organization. It helps organizations understand how personal data moves through their systems, where it is stored, and how it is processed. Note that putting a comprehensive data strategy in place is not in scope for this post.

Snapshot 85

Snapshot Metadata Measurement Data Warehouse 85

AWS Big Data

FEBRUARY 1, 2023

With data volumes exhibiting a double-digit percentage growth rate year on year and the COVID pandemic disrupting global logistics in 2021, it became more critical to scale and generate near-real-time data. You can visually create, run, and monitor extract, transform, and load (ETL) pipelines to load data into your data lakes.

Optimization 52

Optimization Forecasting Data Lake Metadata 52

AWS Big Data

JULY 25, 2023

For more information, refer to Granting access to monitor queries. This gives a visual representation of peaks and lows in your consumption over your chosen period of time. For a complete list of system views and their uses, refer to Monitoring views. For more information, refer to WLM query monitoring rules.

Metrics 82

Metrics Data Warehouse Dashboards Snapshot 82

AWS Big Data

AUGUST 2, 2023

The data architecture diagram below shows an example of how you could use AWS services to calculate and visualize an organization’s estimated carbon footprint. Customers have the flexibility to choose the services in each stage of the data pipeline based on their use case. usage_therms", "gasutilization"."usage_scf"

Data Lake 74

Data Lake Measurement Visualization Data Architecture 74

AWS Big Data

JULY 19, 2023

We show how to perform extract, transform, and load (ELT), an integration process focused on getting the raw data from a data lake into a staging layer to perform the modeling. Stage the source data Before we can create and load the dimensions table, we need source data. There are seven different dimension types.

Modeling 71

Modeling Sales Data Warehouse Snapshot 71

AWS Big Data

MARCH 3, 2023

Building data lakes from continuously changing transactional data of databases and keeping data lakes up to date is a complex task and can be an operational challenge. You can then apply transformations and store data in Delta format for managing inserts, updates, and deletes. with Apache Spark version 3.3.0)

Data Lake 85

Data Lake Dashboards Metrics Metadata 85

Cloudera

MARCH 23, 2022

For example, a Jupyter notebook in CML, can use Spark or Python framework to directly access an Iceberg table to build a forecast model, while new data is ingested via NiFi flows, and a SQL analyst monitors revenue targets using Data Visualization. 2: Open formats. Financial regulation. Reproducibility for ML Ops.

Metadata 88

Metadata Data Architecture Machine Learning Cost-Benefit 88

AWS Big Data

JUNE 21, 2023

When setting out to build a data warehouse, it’s a common pattern to have a data lake as the source of the data warehouse. The data lake in this context serves a number of important functions: It acts as a central source for multiple applications, not just exclusively for data warehousing purposes.

Data Warehouse 83

Data Warehouse Data Lake OLAP Cost-Benefit 83