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What is data architecture? A framework to manage data

CIO Business Intelligence

Data architecture definition Data architecture describes the structure of an organizations logical and physical data assets, and data management resources, according to The Open Group Architecture Framework (TOGAF). An organizations data architecture is the purview of data architects. Cloud storage.

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How DataOps is Transforming Commercial Pharma Analytics

DataKitchen

DataOps has become an essential methodology in pharmaceutical enterprise data organizations, especially for commercial operations. Companies that implement it well derive significant competitive advantage from their superior ability to manage and create value from data. Figure 4: DataOps architecture based on the DataKitchen Platform.

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Centralize Your Data Processes With a DataOps Process Hub

DataKitchen

Data organizations often have a mix of centralized and decentralized activity. DataOps concerns itself with the complex flow of data across teams, data centers and organizational boundaries. It expands beyond tools and data architecture and views the data organization from the perspective of its processes and workflows.

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DataOps For Business Analytics Teams

DataKitchen

Data tables from IT and other data sources require a large amount of repetitive, manual work to be used in analytics. Business analysts sometimes perform data science, but usually, they integrate and visualize data and create reports and dashboards from data supplied by other groups. Some IT teams are fantastic.

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Amazon Redshift announcements at AWS re:Invent 2023 to enable analytics on all your data

AWS Big Data

In 2013, Amazon Web Services revolutionized the data warehousing industry by launching Amazon Redshift , the first fully-managed, petabyte-scale, enterprise-grade cloud data warehouse. Amazon Redshift made it simple and cost-effective to efficiently analyze large volumes of data using existing business intelligence tools.

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Introducing generative AI upgrades for Apache Spark in AWS Glue (preview)

AWS Big Data

data = [(1, "1899-12-31 23:59:59"), (2, "1900-01-01 00:00:00")] schema = StructType([ StructField("id", IntegerType(), True), StructField("timestamp", TimestampType(), True) ]) df = spark.createDataFrame(data, schema=schema) df.write.mode("overwrite").parquet("path/to/parquet_file") For example, in Spark 3.2, In Spark 3.1 In Spark 3.0,

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The Future of the Data Lakehouse – Open

CIO Business Intelligence

These lakes power mission critical large scale data analytics, business intelligence (BI), and machine learning use cases, including enterprise data warehouses. In recent years, the term “data lakehouse” was coined to describe this architectural pattern of tabular analytics over data in the data lake.