Remove Metadata Remove NoSQL Remove Structured Data
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Taking Charge of Tables: Introducing OpenHouse for Big Data Management

LinkedIn Engineering

Open source data lakehouse deployments are built on the foundations of compute engines (like Apache Spark, Trino, Apache Flink), distributed storage (HDFS, cloud blob stores), and metadata catalogs / table formats (like Apache Iceberg, Delta, Hudi, Apache Hive Metastore). Tables are governed as per agreed upon company standards.

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Top 10 Hadoop Tools to Learn in Big Data Career 2024

Knowledge Hut

NoSQL This database management system has been designed in a way that it can store and handle huge amounts of semi-structured or unstructured data. NoSQL databases can handle node failures. Different databases have different patterns of data storage. Pros: Avro stores data in a compact and efficient manner.

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Powering SQL Draw with Rockset, Retool and dbt

Rockset

As a key-value NoSQL database, storing and retrieving individual records are its bread and butter. For those unfamiliar, DynamoDB makes database scalability a breeze, but with some major caveats.

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Hadoop vs Spark: Main Big Data Tools Explained

AltexSoft

HDFS master-slave structure. A HDFS Master Node, called a NameNode , keeps metadata with critical information about system files (like their names, locations, number of data blocks in the file, etc.) and keeps track of storage capacity, a volume of data being transferred, etc. Data storage options.

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Data Lakehouse: Concept, Key Features, and Architecture Layers

AltexSoft

In a nutshell, the lakehouse system leverages low-cost storage to keep large volumes of data in its raw formats just like data lakes. At the same time, it brings structure to data and empowers data management features similar to those in data warehouses by implementing the metadata layer on top of the store.

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Data Collection for Machine Learning: Steps, Methods, and Best Practices

AltexSoft

From the perspective of data science, all miscellaneous forms of data fall into three large groups: structured, semi-structured, and unstructured. Key differences between structured, semi-structured, and unstructured data. They can be accumulated in NoSQL databases like MongoDB or Cassandra.

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100+ Big Data Interview Questions and Answers 2023

ProjectPro

This process involves data collection from multiple sources, such as social networking sites, corporate software, and log files. Data Storage: The next step after data ingestion is to store it in HDFS or a NoSQL database such as HBase. Data Processing: This is the final step in deploying a big data model.