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Cloud Dataflow:
Dataflow is a cloud-based data analysis solution that aims for large-scale data input and low-latency processing via fast parallel execution of analytics pipelines. Dataflow offers an advantage over Airflow in that it supports various languages, including Java, Python, and SQL, as well as engines like as Flink and Spark. However, Juan cautions that the high cost of Dataflow may be a negative for others. -
Cloud Engineering:
In this strategy, we employ a mechanism to have autonomous pipeline segments that execute on separate servers created by a message like Apache Kafka. These systems necessitate a large number of servers, and distributed teams require frequent access to data. There are numerous private cloud providers, including AWS (Amazon Web Services), Microsoft Azure, and Google Cloud, which are the most widely used tools for building and developing distributed systems. -
Big data tools:
Hadoop, distributed file systems such as HDFS, search engines such as Elasticsearch, ETL, and installation software: Apache Spark analytics engine for big data computation, Apache Drill SQL query engine with big data execution functionality, Apache Beam framework and software development kit for designing and building and running pipelines on distributed processing backends in parallel are all big data technologies that a data engineer should be able to use (or at least be aware of).