U-Next

SEPTEMBER 7, 2022

Structuring data refers to converting unstructured data into tables and defining data types and relationships based on a schema. Gen 2 Azure Data Lake Storage . Data lakes can also be organized and queried using other technologies, such as . Atlas Data Lake powered by MongoDB. .

Data Lake 52

Data Lake Data Warehouse Unstructured Data Amazon Web Services 52

DareData

JANUARY 30, 2023

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. Examples of technologies able to aggregate data in data lake format include Amazon S3 or Azure Data Lake.

Data Engineering 52

Data Engineering Data Engineer NoSQL Engineering 52

Striim

OCTOBER 11, 2024

Striim supported American Airlines by implementing a comprehensive data pipeline solution to modernize and accelerate operations. To achieve this, the TechOps team implemented a real-time data hub using MongoDB, Striim, Azure, and Databricks to maintain seamless, large-scale operations.

Data Pipeline 52

Data Pipeline MongoDB Unstructured Data Data Lake 52

Rockset

OCTOBER 4, 2022

This means users need to configure their streams to batch data ahead of loading into ClickHouse. Rockset has native connectors that ingest event streams from Kafka and Kinesis and CDC streams from databases like MongoDB, DynamoDB, Postgres and MySQL. ClickHouse has several storage engines that can pre-aggregate data.

MySQL 52

MySQL Kafka Aggregated Data Data Warehouse 52

ProjectPro

OCTOBER 28, 2015

Hadoop Sqoop and Hadoop Flume are the two tools in Hadoop which is used to gather data from different sources and load them into HDFS. Sqoop in Hadoop is mostly used to extract structured data from databases like Teradata, Oracle, etc., Sqoop does not support importing of data from non-RDBMS such as MongoDB and Cassandra.

ETL Tools 52

ETL Tools Hadoop Relational Database Unstructured Data 52

AltexSoft

MARCH 14, 2023

Also, there are NoSQL databases that can be home to all sorts of data, including unstructured and semi-structured (images, PDF files, audio, JSON, etc.) Some popular databases are Postgres and MongoDB. But this distinction has been blurred with the era of cloud data warehouses.

IT 59

IT Data Warehouse Data Governance Data Lake 59

ProjectPro

DECEMBER 7, 2021

In broader terms, two types of data -- structured and unstructured data -- flow through a data pipeline. The structured data comprises data that can be saved and retrieved in a fixed format, like email addresses, locations, or phone numbers. Step 1- Automating the Lakehouse's data intake.

Data Pipeline 52

Data Pipeline Architecture Kafka Data Lake 52

Rockset

DECEMBER 22, 2020

By using Rockset, we may have to Tokenize our search fields on ingestion however we make up for it in firstly, the simplicity of processing this data on ingestion as well as easier querying, joining, and aggregating data.

SQL 40

SQL Data MongoDB Aggregated Data 40

ProjectPro

AUGUST 24, 2021

Google BigQuery receives the structured data from workers. Finally, the data is passed to Google Data studio for visualization. to accumulate data over a given period for better analysis. MongoDB stores the processed and aggregated results. Collection happens in the Kafka topic.

Data Engineering 52

Data Engineering Data Engineer Coding Project 52

ProjectPro

JULY 27, 2021

Relational Database Management Systems (RDBMS) Non-relational Database Management Systems Relational Databases primarily work with structured data using SQL (Structured Query Language). SQL works on data arranged in a predefined schema. Non-relational databases support dynamic schema for unstructured data.

Data Engineering 40

Data Engineering Data Engineer Engineering Hadoop 40