Data Engineering Podcast

JUNE 12, 2022

Summary Unstructured data takes many forms in an organization. From a data engineering perspective that often means things like JSON files, audio or video recordings, images, etc. The Ascend Data Automation Cloud provides a unified platform for data ingestion, transformation, orchestration, and observability.

Unstructured Data 130

Unstructured Data MongoDB Scala MySQL 130

Knowledge Hut

JUNE 26, 2023

Using big data, we are able to transform unstructured data, such as customer reviews, into actionable insights, which enables businesses to better understand how and why customers prefer their products or services and to make improvements to their operations as quickly as is practically possible.

Data Engineering 98

Data Engineering Data Engineer Coding Project 98

Knowledge Hut

SEPTEMBER 26, 2023

Organizations can harness the power of the cloud, easily scaling resources up or down to meet their evolving data processing demands. Supports Structured and Unstructured Data: One of Azure Synapse's standout features is its versatility in handling a wide array of data types. Key Features of Databricks 1.

Data Lake 52

Data Lake Database-centric Pipeline-centric Machine Learning 52

AltexSoft

JULY 29, 2022

To facilitate data ingestion, there are Apache Flume aggregating log data from multiple servers and Apache Sqoop designed to transport information between Hadoop and relational (SQL) databases. It lets you run MapReduce and Spark jobs on data kept in Google Cloud Storage (instead of HDFS); or.

Hadoop 59

Hadoop Big Data Google Cloud NoSQL 59

AltexSoft

MARCH 30, 2023

Databricks architecture Databricks provides an ecosystem of tools and services covering the entire analytics process — from data ingestion to training and deploying machine learning models. Besides that, it’s fully compatible with various data ingestion and ETL tools. Let’s see what exactly Databricks has to offer.

Scala 64

Scala Data Lake BI Google Cloud 64