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We’ve just wrapped up our first-ever DataEngineering Summit. If you weren’t able to make it, don’t worry, you can watch the sessions on-demand and keep up-to-date on essential dataengineering tools and skills. It also addresses the strategies and best practices for implementing a data mesh.
Data Mesh More data management systems in 2023 will also shift toward a data mesh architecture. This decentralized architecture breaks datalakes into smaller domains specific to a given team or department. Automation and artificial intelligence (AI) will see particular growth in the realm of observability.
For example, data catalogs have evolved to deliver governance capabilities like managing data quality and data privacy and compliance. It uses metadata and data management tools to organize all data assets within your organization.
Alignment to other tools in the organization’s tech stack Consider how well the MLOps tool integrates with your existing tools and workflows, such as data sources, dataengineering platforms, code repositories, CI/CD pipelines, monitoring systems, etc. This provides end-to-end support for dataengineering and MLOps workflows.
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