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Centralize Your Data Processes With a DataOps Process Hub

DataKitchen

It expands beyond tools and data architecture and views the data organization from the perspective of its processes and workflows. The DataKitchen Platform is a “ process hub” that masters and optimizes those processes. Cloud computing has made it much easier to integrate data sets, but that’s only the beginning.

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Integrating Striim with BigQuery ML: Real-time Data Processing for Machine Learning

Striim

Real-time data processing in the world of machine learning allows data scientists and engineers to focus on model development and monitoring. Striim’s strength lies in its capacity to connect to over 150 data sources, enabling real-time data acquisition from virtually any location and simplifying data transformations.

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Streaming Ingestion for Apache Iceberg With Cloudera Stream Processing

Cloudera

Iceberg is a high-performance open table format for huge analytic data sets. It allows multiple data processing engines, such as Flink, NiFi, Spark, Hive, and Impala to access and analyze data in simple, familiar SQL tables. This enables you to maximize utilization of streaming data at scale.

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Complete Guide to Data Ingestion: Types, Process, and Best Practices

Databand.ai

Complete Guide to Data Ingestion: Types, Process, and Best Practices Helen Soloveichik July 19, 2023 What Is Data Ingestion? Data Ingestion is the process of obtaining, importing, and processing data for later use or storage in a database.

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Fraud Detection With Cloudera Stream Processing Part 2: Real-Time Streaming Analytics

Cloudera

In part 1 of this blog we discussed how Cloudera DataFlow for the Public Cloud (CDF-PC), the universal data distribution service powered by Apache NiFi, can make it easy to acquire data from wherever it originates and move it efficiently to make it available to other applications in a streaming fashion. Data decays!

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An AI Chat Bot Wrote This Blog Post …

DataKitchen

ChatGPT> DataOps, or data operations, is a set of practices and technologies that organizations use to improve the speed, quality, and reliability of their data analytics processes. The goal of DataOps is to help organizations make better use of their data to drive business decisions and improve outcomes.

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Building an Open Data Processing Pipeline for IoT

Cloudera

The open data processing pipeline. IoT is expected to generate a volume and variety of data greatly exceeding what is being experienced today, requiring modernization of information infrastructure to realize value. The Enterprise Data Hub. Telemetry data routed to the Cloudera Enterprise Data Hub flows into Apache Kafka.