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Ask HN: What are some ways to efficiently store and analyze large datasets?(hn.user)

1 point by bigdata_enthusiast 1 year ago flag hide 10 comments

user1 1 year ago next
One way to efficiently store and analyze large datasets is to use a distributed file system such as Hadoop HDFS or a cloud-based storage solution like Amazon S3. These systems can handle huge amounts of data and provide parallel processing capabilities.
- user3 1 year ago next
  That's true, Hadoop and S3 are great for scalability and parallel processing. Do you have any experience with using Spark for data analysis? I've heard good things about it.
  user6 1 year ago next
  Yes, Spark is a powerful tool for big data processing and analysis. It integrates well with Hadoop and can work with various data storage formats like Parquet, Avro, and ORC.
  user9 1 year ago next
  When using Spark, what's the recommended way to handle streaming data? Should we persist the data in a database or keep it in memory?
  user7 1 year ago prev next
  When it comes to data analysis, you might also want to consider using a cloud-based data analytics platform like Google BigQuery or AWS Redshift. These platforms offer fully managed data warehousing solutions that are optimized for large-scale data analysis.
- user4 1 year ago prev next
  I've used Parquet with Hive and it worked well. It's important to note that columnar formats like Parquet are better for read-intensive query workloads, while row-based formats are better for write-intensive workloads.
user2 1 year ago prev next
Another option is to use a columnar storage format such as Parquet or ORC. These formats are optimized for big data workloads and can significantly improve query performance compared to traditional row-based formats.
- user5 1 year ago next
  ORC is similar to Parquet in terms of performance, but with some added benefits like better support for complex data types. It's worth evaluating both options to see which one works best for your use case.
  user8 1 year ago next
  Thanks for the tip about ORC. I'll definitely consider it for my use case. Do you know if it has good support for UDFs (User Defined Functions) like Parquet does?
- user10 1 year ago prev next
  In addition to Parquet and ORC, there's also the option of using Avro for big data storage and analysis. Avro has good support for schema evolution and can be used with a variety of tools and languages, including Hive, Spark, and Python.

user1 1 year ago next
One way to efficiently store and analyze large datasets is to use a distributed file system such as Hadoop HDFS or a cloud-based storage solution like Amazon S3. These systems can handle huge amounts of data and provide parallel processing capabilities.
- user3 1 year ago next
  That's true, Hadoop and S3 are great for scalability and parallel processing. Do you have any experience with using Spark for data analysis? I've heard good things about it.
  user6 1 year ago next
  Yes, Spark is a powerful tool for big data processing and analysis. It integrates well with Hadoop and can work with various data storage formats like Parquet, Avro, and ORC.
  user9 1 year ago next
  When using Spark, what's the recommended way to handle streaming data? Should we persist the data in a database or keep it in memory?
  user7 1 year ago prev next
  When it comes to data analysis, you might also want to consider using a cloud-based data analytics platform like Google BigQuery or AWS Redshift. These platforms offer fully managed data warehousing solutions that are optimized for large-scale data analysis.
- user4 1 year ago prev next
  I've used Parquet with Hive and it worked well. It's important to note that columnar formats like Parquet are better for read-intensive query workloads, while row-based formats are better for write-intensive workloads.
user2 1 year ago prev next
Another option is to use a columnar storage format such as Parquet or ORC. These formats are optimized for big data workloads and can significantly improve query performance compared to traditional row-based formats.
- user5 1 year ago next
  ORC is similar to Parquet in terms of performance, but with some added benefits like better support for complex data types. It's worth evaluating both options to see which one works best for your use case.
  user8 1 year ago next
  Thanks for the tip about ORC. I'll definitely consider it for my use case. Do you know if it has good support for UDFs (User Defined Functions) like Parquet does?
- user10 1 year ago prev next
  In addition to Parquet and ORC, there's also the option of using Avro for big data storage and analysis. Avro has good support for schema evolution and can be used with a variety of tools and languages, including Hive, Spark, and Python.