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Cloudera DataFlow Designer: The Key to Agile Data Pipeline Development

Cloudera

Once a draft has been created or opened, developers use the visual Designer to build their data flow logic and validate it using interactive test sessions. Managing drafts outside the Catalog keeps a clean distinction between phases of the development cycle, leaving only those flows that are ready for deployment published in the Catalog.

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Simplify Metrics on Apache Druid With Rill Data and Cloudera

Cloudera

Cloudera Data Warehouse). Efficient batch data processing. Complex data transformations. Support for data rollup and summarization. Highly optimized time series queries. Triton Digital, for example, uses Rill to deploy self-serve reporting for hundreds of digital media publishers with little or no training.

Metrics 82
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Cloudera Data Engineering 2021 Year End Review

Cloudera

Since the release of Cloudera Data Engineering (CDE) more than a year ago , our number one goal was operationalizing Spark pipelines at scale with first class tooling designed to streamline automation and observability. This enabled new use-cases with customers that were using a mix of Spark and Hive to perform data transformations. .

Snapshot 115
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Harnessing Streaming Data: Insights at the Speed of Life

Sisense

Let’s look at a few ways that different industries take advantage of streaming data. How industries can benefit from streaming data. Automotive: Monitoring connected, autonomous cars in real time to optimize routes to avoid traffic and for diagnosis of mechanical issues. Optimizing object storage.

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Gain insights from historical location data using Amazon Location Service and AWS analytics services

AWS Big Data

Developers can use the support in Amazon Location Service for publishing device position updates to Amazon EventBridge to build a near-real-time data pipeline that stores locations of tracked assets in Amazon Simple Storage Service (Amazon S3). This method uses GZIP compression to optimize storage consumption and query performance.

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The Ultimate Guide to Modern Data Quality Management (DQM) For An Effective Data Quality Control Driven by The Right Metrics

datapine

This means there are no unintended data errors, and it corresponds to its appropriate designation (e.g., Here, it all comes down to the data transformation error rate. In other words, it measures the time between when data is expected and the moment when it is readily available for use. date, month, and year).

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Assessing and interviewing data engineers from a distance

Insight

Having run a data engineering program at Insight for several years, we’ve identified three broad categories of data engineers: Software engineers who focus on building data pipelines. In some cases, they work to deploy data science models into production with an eye towards optimization, scalability and maintainability.