Data Solutions Kafka MongoDB 
Knowledge Hut
APRIL 25, 2024
However, advances in technology have now made it possible to store, process, and analyze big data quickly and effectively. There are a variety of big data processing technologies available, including Apache Hadoop, Apache Spark, and MongoDB. The most popular NoSQL database systems include MongoDB, Cassandra, and HBase.
ProjectPro
FEBRUARY 1, 2017
The data architecture is based on open source standards Pentaho and is used for managing, preparing and integrating data that runs through their environments including Cloudera Hadoop Distribution , HP Vertica, Flume and Kafka. v) In 2017, we might think of big data as a data fabric. The future of Hadoop is cloudy.
Cloud Academy
JANUARY 27, 2022
Microsoft SQL Server Document-oriented database: MongoDB (classified as NoSQL) The Basics of Data Management, Data Manipulation and Data Modeling This learning path focuses on common data formats and interfaces. Apache Kafka Amazon MSK and Kafka Under the Hood Apache Kafka is an open-source streaming platform.
Knowledge Hut
DECEMBER 26, 2023
Data Architects design, create and maintain database systems according to the business model requirements. In other words, they develop, maintain, and test Big Data solutions. They also make use of ETL tools, messaging systems like Kafka, and Big Data Tool kits such as SparkML and Mahout.
Edureka
FEBRUARY 9, 2023
The following are some of the fundamental foundational skills required of data engineers: A data engineer should be aware of changes in the data landscape. They should also consider how data systems have evolved and how they have benefited data professionals.
Knowledge Hut
NOVEMBER 17, 2023
Azure Data Factory, Azure Databricks, Azure Synapse Analytics, Azure Storage, Azure Data Lake, Azure Blob Storage, Azure Cosmos DB, Azure Stream Analytics, Azure HDInsight, and other Azure data services are just a few of the many Azure data services that Azure data engineers deal with.
Knowledge Hut
SEPTEMBER 26, 2023
Additionally, we must be aware of how data systems have developed and helped data professionals. Find out what makes on-premises and cloud data solutions different. A data engineer has to be well-versed in both the duties of the field and the related sector.
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