Remove Accessible Remove Cloud Storage Remove Document Remove Unstructured Data
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Discover and Explore Data Faster with the CDP DDE Template

Cloudera

DDE also makes it much easier for application developers or data workers to self-service and get started with building insight applications or exploration services based on text or other unstructured data (i.e. data best served through Apache Solr). Stores source documents. What does DDE entail?

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Migrate Hive data from CDH to CDP public cloud

Cloudera

Using easy-to-define policies, Replication Manager solves one of the biggest barriers for the customers in their cloud adoption journey by allowing them to move both tables/structured data and files/unstructured data to the CDP cloud of their choice easily. In our example we use the following External Account.

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Do You Know Where All Your Data Is?

Cloudera

The top-line benefits of a hybrid data platform include: Cost efficiency. A hybrid data platform enables the preservation of existing investments in legacy applications and workloads without modifying them. Improved scalability and agility. Flexibility. This minimizes upfront disruption while reducing maintenance costs over time.

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The Future of Database Management in 2023

Knowledge Hut

These databases use RAM-based data storage, which offers quicker access and response times than disk-based storage. Multi-model Databases Another innovative solution that supports many data models, including document, graph, and key-value stores, is multi-model databases.

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Data Warehouse vs Data Lake vs Data Lakehouse: Definitions, Similarities, and Differences

Monte Carlo

With pre-built functionalities and robust SQL support, data warehouses are tailor-made to enable swift, actionable querying for data analytics teams working primarily with structured data. Storage can utilize S3, Google Cloud Storage, Microsoft Azure Blob Storage, or Hadoop HDFS. Or maybe both.)

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Data Architect: Role Description, Skills, Certifications and When to Hire

AltexSoft

What’s more, investing in data products, as well as in AI and machine learning was clearly indicated as a priority. This suggests that today, there are many companies that face the need to make their data easily accessible, cleaned up, and regularly updated. This privacy law must be kept in mind when building data architecture.

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What Is Data Engineering And What Does A Data Engineer Do? 

Meltano

Data engineering is the process of designing systems for collecting, storing, and analyzing large volumes of data. Put simply, it is the process of making raw data usable and accessible to data scientists, business analysts, and other team members who rely on data. Why Is Data Engineering Important?