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How Snowflake Enhanced GTM Efficiency with Data Sharing and Outreach Customer Engagement Data

Snowflake

To improve go-to-market (GTM) efficiency, Snowflake created a bi-directional data share with Outreach that provides consistent access to the current version of all our customer engagement data. In this blog, we’ll take a look at how Snowflake is using data sharing to benefit our SDR teams and marketing data analysts.

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Druid Deprecation and ClickHouse Adoption at Lyft

Lyft Engineering

In this particular blog post, we explain how Druid has been used at Lyft and what led us to adopt ClickHouse for our sub-second analytic system. Druid at Lyft Apache Druid is an in-memory, columnar, distributed, open-source data store designed for sub-second queries on real-time and historical data.

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Complete Guide to Data Transformation: Basics to Advanced

Ascend.io

Data transformation helps make sense of the chaos, acting as the bridge between unprocessed data and actionable intelligence. You might even think of effective data transformation like a powerful magnet that draws the needle from the stack, leaving the hay behind.

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Using other CDP services with Cloudera Operational Database

Cloudera

In the previous blog post , we looked at some of the application development concepts for the Cloudera Operational Database (COD). In this blog post, we’ll see how you can use other CDP services with COD. Integrated across the Enterprise Data Lifecycle . Cloudera Data Engineering to ingest bulk data and data from mainframes.

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Tips to Build a Robust Data Lake Infrastructure

DareData

In this blog post, we aim to share practical insights and techniques based on our real-world experience in developing data lake infrastructures for our clients - let's start! The Data Lake acts as the central repository for aggregating data from diverse sources in its raw format.

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What is a Data Pipeline (and 7 Must-Have Features of Modern Data Pipelines)

Striim

This interconnected approach enables teams to create, manage, and automate data pipelines with ease and minimal intervention. In contrast, traditional data pipelines often require significant manual effort to integrate various external tools for data ingestion , transfer, and analysis.

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Machine Learning with Python, Jupyter, KSQL and TensorFlow

Confluent

The blog posts How to Build and Deploy Scalable Machine Learning in Production with Apache Kafka and Using Apache Kafka to Drive Cutting-Edge Machine Learning describe the benefits of leveraging the Apache Kafka ® ecosystem as a central, scalable and mission-critical nervous system. For now, we’ll focus on Kafka.