Rockset

AUGUST 30, 2021

Apache Kafka has made acquiring real-time data more mainstream, but only a small sliver are turning batch analytics, run nightly, into real-time analytical dashboards with alerts and automatic anomaly detection. The majority are still draining streaming data into a data lake or a warehouse and are doing batch analytics.

SQL 52

SQL Kafka MongoDB MySQL 52

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

ProjectPro

DECEMBER 7, 2021

Generally, data pipelines are created to store data in a data warehouse or data lake or provide information directly to the machine learning model development. Keeping data in data warehouses or data lakes helps companies centralize the data for several data-driven initiatives.

Data Pipeline 52

Data Pipeline Architecture Kafka AWS 52

DareData

JANUARY 30, 2023

Examples of relational databases include MySQL or Microsoft SQL Server. Examples of NoSQL databases include MongoDB or Cassandra. Data lakes: These are large-scale data storage systems that are designed to store and process large amounts of raw, unstructured data. Introduction to Designing Data Lakes in AWS.

Data Engineering 52

Data Engineering Data Engineer NoSQL Engineering 52

ProjectPro

AUGUST 24, 2021

Source Code: Visualize Daily Wikipedia Trends with Hive, Zeppelin, and Airflow (projectpro.io) 7) Data Aggregation Data Aggregation refers to collecting data from multiple sources and drawing insightful conclusions from it. to accumulate data over a given period for better analysis.

Data Engineering 52

Data Engineering Data Engineer Coding Project 52

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? Hadoop is highly scalable.

Data Engineering 40

Data Engineering Data Engineer Engineering Hadoop 40