Ascend.io

SEPTEMBER 14, 2023

Python for Data Engineering Use Cases Data engineering, at its core, is about preparing “big data” for analytical processing. It’s an umbrella that covers everything from gathering raw data to processing and storing it efficiently. csv') data_excel = pd.read_excel('data2.xlsx')

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

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

Monte Carlo

FEBRUARY 15, 2023

It’s possible to use a database meant for OLTP as a data warehouse, but as your data grows and the queries become more complex, operations start to slow down, ultimately resulting in deadlocks and missed data. Cleaning Bad data can derail an entire company, and the foundation of bad data is unclean data.

Data Warehouse 52

Data Warehouse Unstructured Data AWS Business Intelligence 52

ProjectPro

FEBRUARY 8, 2023

But this data is not that easy to manage since a lot of the data that we produce today is unstructured. In fact, 95% of organizations acknowledge the need to manage unstructured raw data since it is challenging and expensive to manage and analyze, which makes it a major concern for most businesses. Why Use AWS Glue?

AWS 98

AWS Scala Metadata Data Lake 98

ProjectPro

JULY 27, 2021

Non-relational databases are ideal if you need flexibility for storing the data since you cannot create documents without having a fixed schema. E.g. PostgreSQL, MySQL, Oracle, Microsoft SQL Server. E.g. Redis, MongoDB, Cassandra, HBase , Neo4j, CouchDB What is data modeling? How did you go about resolving this?

Data Engineering 40

Data Engineering Data Engineer Engineering Hadoop 40