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Essential data engineering tools for 2023: Empowering for management and analysis

Data Science Dojo

Data engineering tools are software applications or frameworks specifically designed to facilitate the process of managing, processing, and transforming large volumes of data. Spark offers a rich set of libraries for data processing, machine learning, graph processing, and stream processing.

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Here’s Why Automation For Data Lakes Could Be Important

Smart Data Collective

Data Lakes are among the most complex and sophisticated data storage and processing facilities we have available to us today as human beings. Analytics Magazine notes that data lakes are among the most useful tools that an enterprise may have at its disposal when aiming to compete with competitors via innovation.

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Comparing Tools For Data Processing Pipelines

The MLOps Blog

If you will ask data professionals about what is the most challenging part of their day to day work, you will likely discover their concerns around managing different aspects of data before they get to graduate to the data modeling stage. This ensures that the data is accurate, consistent, and reliable.

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Find Your AI Solutions at the ODSC West AI Expo

ODSC - Open Data Science

Cloudera Cloudera is a cloud-based platform that provides businesses with the tools they need to manage and analyze data. They offer a variety of services, including data warehousing, data lakes, and machine learning. The platform includes several features that make it easy to develop and test data pipelines.

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Data architecture strategy for data quality

IBM Journey to AI blog

The first generation of data architectures represented by enterprise data warehouse and business intelligence platforms were characterized by thousands of ETL jobs, tables, and reports that only a small group of specialized data engineers understood, resulting in an under-realized positive impact on the business.

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Data science vs data analytics: Unpacking the differences

IBM Journey to AI blog

By analyzing datasets, data scientists can better understand their potential use in an algorithm or machine learning model. The data science lifecycle Data science is iterative, meaning data scientists form hypotheses and experiment to see if a desired outcome can be achieved using available data.

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How to use foundation models and trusted governance to manage AI workflow risk

IBM Journey to AI blog

It includes processes that trace and document the origin of data, models and associated metadata and pipelines for audits. How to scale AL and ML with built-in governance A fit-for-purpose data store built on an open lakehouse architecture allows you to scale AI and ML while providing built-in governance tools.

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