Remove Data Architecture Remove Data Preparation Remove Pipeline-centric
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Snowpark Offers Expanded Capabilities Including Fully Managed Containers, Native ML APIs, New Python Versions, External Access, Enhanced DevOps and More

Snowflake

Snowpark is our secure deployment and processing of non-SQL code, consisting of two layers: Familiar Client Side Libraries – Snowpark brings deeply integrated, DataFrame-style programming and OSS compatible APIs to the languages data practitioners like to use. Previously, tasks could be executed as quickly as 1-minute.

Python 52
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Azure Synapse vs Databricks: 2023 Comparison Guide

Knowledge Hut

Key Features of Azure Synapse Here are some of the key features of Azure Synapse: Cloud Data Service: Azure Synapse operates as a cloud-native service, residing within the Microsoft Azure cloud ecosystem. This cloud-centric approach ensures scalability, flexibility, and cost-efficiency for your data workloads.

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Building a Scalable Search Architecture

Confluent

It involves many moving parts, from data preparation to building indexing and query pipelines. Luckily, this task looks a lot like the way we tackle problems that arise when connecting data. Building an indexing pipeline at scale with Kafka Connect. Building a resilient and scalable solution is not always easy.

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Azure Synapse vs. Databricks – What Are the Differences?

Edureka

On the other hand, thanks to the Spark component, you can perform data preparation, data engineering, ETL, and machine learning tasks using industry-standard Apache Spark. By letting you query data directly in the lake without the need for movement, Synapse cuts down the storage costs and eliminates data duplication.