Ascend.io

SEPTEMBER 14, 2023

Use Case: Transforming monthly sales data to weekly averages import dask.dataframe as dd data = dd.read_csv('large_dataset.csv') mean_values = data.groupby('category').mean().compute() compute() Data Storage Python extends its mastery to data storage, boasting smooth integrations with both SQL and NoSQL databases.

Data Engineering 52

Data Engineering Data Engineer Python Engineering 52

Rockset

AUGUST 30, 2021

A Quick Primer on Indexing in Rockset Rockset allows users to connect real-time data sources — data streams (Kafka, Kinesis), OLTP databases (DynamoDB, MongoDB, MySQL, PostgreSQL) and also data lakes (S3, GCS) — using built-in connectors. You can also optionally use WHERE clauses to filter out data.

SQL 52

SQL Kafka MongoDB MySQL 52

Rockset

FEBRUARY 2, 2022

The benefit of these tools is that they’re built specifically for data analytics. They support joins and their column orientation allows you to quickly and effectively carry out aggregations. Data warehouses scale well and are well-suited to BI and advanced analytics use cases.

MongoDB 52

MongoDB NoSQL Data Warehouse BI 52

Knowledge Hut

SEPTEMBER 6, 2023

Skills acquired : Relational database concepts Retrieving data using the SQL SELECT statement. Sorting and restricting data. Using Conditional Expressions and Conversion functions Reporting Aggregated Data Using Group Functions Displaying data taken from multiple tables. Oracle Certified Professional, MySQL 8.0

Database 52

Database Certification MongoDB MySQL 52

Knowledge Hut

APRIL 19, 2023

The process of merging and summarizing data from various sources in order to generate insightful conclusions is known as data aggregation. The purpose of data aggregation is to make it easier to analyze and interpret large amounts of data. BigQuery is scalable and can handle large volumes of data.

Process 59

Process Data Mining Aggregated Data Portfolio 59

ProjectPro

FEBRUARY 8, 2023

This serverless data integration service can automatically and quickly discover structured or unstructured enterprise data when stored in data lakes in Amazon S3, data warehouses in Amazon Redshift, and other databases that are a component of the Amazon Relational Database Service.

AWS 98

AWS Scala Metadata Data Lake 98

Rockset

SEPTEMBER 13, 2022

Further, data is king, and users want to be able to slice and dice aggregated data as needed to find insights. Users don't want to wait for data engineers to provision new indexes or build new ETL chains. They want unfettered access to the freshest data available.

SQL 52

SQL Database Relational Database AWS 52