Remove Designing Remove Metadata Remove NoSQL Remove Structured Data
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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. Rockset is a real-time analytics database designed for sub-second queries and real-time ingest. For those unfamiliar, DynamoDB makes database scalability a breeze, but with some major caveats. For the backend, we chose Rockset.

SQL 52
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Data Engineering Glossary

Silectis

Data Architecture Data architecture is a composition of models, rules, and standards for all data systems and interactions between them. Data Catalog An organized inventory of data assets relying on metadata to help with data management. Database A collection of 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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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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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 Lake vs Data Warehouse - Working Together in the Cloud

ProjectPro

Data is collected and stored in data warehouses from multiple sources to provide insights into business data. Data warehouses store highly transformed, structured data that is preprocessed and designed to serve a specific purpose. Data from data warehouses is queried using SQL.