Towards Data Science

NOVEMBER 4, 2023

Introduction to Apache Iceberg Tables Simplified data integrations Managed solutions like Fivetran and Stitch were built to manage third-party API integrations with ease. These days many companies choose this approach to simplify data interactions with their external data sources.

Data Engineering 98

Data Engineering Data Engineer Engineering BI 98

Knowledge Hut

NOVEMBER 3, 2023

Skills Required for MongoDB for Data Science To excel in MongoDB for data science, you need a combination of technical and analytical skills: Database Querying: It is necessary to know how to write sophisticated queries using the query language of MongoDB. Quickly pull (fetch), filter, and reduce data.

MongoDB 52

MongoDB Data Science NoSQL ETL Tools 52

Databand.ai

JULY 19, 2023

The extracted data is often raw and unstructured and may come in various formats such as text, images, audio, or video. The extraction process requires careful planning to ensure data integrity. It’s crucial to understand the source systems and their structure, as well as the type and quality of data they produce.

Data Cleanse 52

Data Cleanse Data Storage Raw Data Data Warehouse 52

Knowledge Hut

MARCH 22, 2024

Versatility: The versatile nature of MongoDB enables it to easily deal with a broad spectrum of data types , structured and unstructured, and therefore, it is perfect for modern applications that need flexible data schemas. Good Hold on MongoDB and data modeling. Experience with ETL tools and data integration techniques.

MongoDB 52

MongoDB Amazon Web Services Computer Science Education 52

phData: Data Engineering

SEPTEMBER 27, 2024

You might implement this using a tool like Apache Kafka or Amazon Kinesis, creating that immutable record of all customer interactions. Data Activation : To put all this customer data to work, you might use a tool like Hightouch or Census. Launched a new loyalty program? Just add it to your ontology! Those days are gone!

Data 52

Data Raw Data Data Lake Architecture 52