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What are the alternatives to Hadoop?

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Apache Spark, cloud data warehouses like Google BigQuery and Snowflake, and specialized platforms like Databricks.
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Alternatives to Hadoop include: 1. Apache Spark: In-memory processing, faster than Hadoop for certain workloads. Supports batch and real-time processing. 2. Apache Flink: Real-time stream processing. Fault-tolerant and low-latency. 3. Apache Storm: Real-time stream processing. Suitable...
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Alternatives to Hadoop include: 1. Apache Spark: In-memory processing, faster than Hadoop for certain workloads. Supports batch and real-time processing. 2. Apache Flink: Real-time stream processing. Fault-tolerant and low-latency. 3. Apache Storm: Real-time stream processing. Suitable for low-latency requirements. 4. Google BigQuery: Managed service for big data analytics, serverless. High performance, scales automatically. 5. Amazon EMR: Managed Hadoop/Spark clusters on AWS. Scalable and cost-effective. 6. Dask: Python-based framework for parallel computing. Scales from single machines to clusters. Each has strengths depending on data type, processing needs, and scalability. read less
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Hadoop alternatives include platforms like Apache Spark, Google BigQuery, Snowflake, Databricks, and Amazon Redshift, offering different advantages for big data processing and analytics. These alternatives can address specific needs in terms of speed, scalability, ease of use, and cloud-based features,...
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Hadoop alternatives include platforms like Apache Spark, Google BigQuery, Snowflake, Databricks, and Amazon Redshift, offering different advantages for big data processing and analytics. These alternatives can address specific needs in terms of speed, scalability, ease of use, and cloud-based features, providing options beyond Hadoop's traditional approach read less
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