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C# Big Data Processing Using C# 14 Features

Big data is not the monopoly of Java, Scala, or Python. With C# 14 and.NET 9 now available, C# developers have a full arsenal of tools to process extremely large data sets with sophistication and performance. The article presents how the new features in C# can be put to use in big data applications with readability, performance, and scalability in mind.

Why C# for Big Data?

Several benefits are provided by C#.

  • A mature ecosystem (.NET 9)
  • High performance and JIT and AOT optimizations
  • Services and language features
  • Elastic integration with Spark (with.NET for Apache Spark)
  • Cloud-native capabilities through Azure Synapse, Data Lake, etc.

With C# 14, language features make data-heavy code written more easily and safely.

Best C# 14 Features Which Help

  • Primary Constructors: Less ceremony when initializing classes.
  • Collection Expressions: Bounded and readable collection creation.
  • Lambda Enhancements: Intrinsic types and attributes on lambdas.
  • Read-only Members Enhancements: Offers immutability for security.

Example: Processing Large Datasets with .NET for Apache Spark

Define a Data Model with a Primary Constructor

public class Transaction(string id, DateTime date, decimal amount)
{
    public string Id { get; } = id;
    public DateTime Date { get; } = date;
    public decimal Amount { get; } = amount;
}

Using Collection Expressions for Batch Queries

var highValueIds = ["txn123", "txn456", "txn789"];

var filtered = transactions
    .Where(t => highValueIds.Contains(t.Id))
    .ToList();

Lambda Improvements in Spark Mapping

var mapped = dataFrame.Map(row => new Transaction(
    row.GetAs<string>("Id"),
    row.GetAs<DateTime>("Date"),
    row.GetAs<decimal>("Amount")
));

Performance Tips

  • Parallelism: Parallel.ForEachAsync to process large data in parallel for data in memory.
  • Span** and Memory**: Great to work with large arrays and buffers.
  • Source Generators: Utilize to generate serializers/deserializers for large data structures during build-time.

Cloud Integration

  • Azure Synapse: C# can control big data pipelines and author U-SQL or T-SQL jobs.
  • Azure Data Lake: Work with data in Data Lake using .NET SDKs.
  • Databricks: Work with C# using REST APIs or with Apache Spark.NET bindings.

Conclusion

C# 14 brings forth great enhancements that allow for cleaner, more productive big data programming. Whether from Spark to log processing, or terabyte crunching of records, C# can be a first-class choice for your big data load.

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