Azure Kusto Simplifies Data Storage and Processing

Azure Kusto is a powerful data analytics platform that simplifies data storage and processing. It's designed to handle large amounts of data and provide fast query performance.

With Azure Kusto, you can store and process data from various sources, including log data, metrics, and IoT data. This makes it an ideal choice for organizations with diverse data needs.

One of the key benefits of Azure Kusto is its ability to automatically scale to meet changing data volumes and query workloads. This ensures that your data is always available and query performance remains high.

Azure Kusto also provides a user-friendly interface for creating and managing data models, which makes it easier to get started with data analytics.

Azure Data Explorer separates storage and compute resources, allowing for independent scale out of storage and compute resources, making it easy to manage and optimize your resources.

This separation also provides accessibility to identical data across multiple compute clusters, which is useful when you need to share data with other teams or applications.

With Azure Data Explorer, data is partitioned into extents, or data shards, which are horizontal slices of the target table. Each extent is encoded and indexed independently of other extents, contributing to linear scale in ingestion throughput.

Azure Data Explorer also retains essential metadata such as table schemas and policy objects.

Azure Data Explorer can ingest terabytes of data in minutes, and query petabytes of data with results returned within milliseconds to seconds.

Azure Data Explorer's storage system is designed to handle large amounts of data efficiently. It separates storage and compute resources, allowing for independent scale out of storage and compute resources.

Data is partitioned into extents, or data shards, which are horizontal slices of the target table. Each extent can start as small as a single record and grows to encompass millions of records.

Extents are spread evenly across cluster nodes, where they're cached both on the local SSD and in memory. This distribution enhances the capacity to prepare and execute highly distributed and parallel queries.

Azure Data Explorer retains essential metadata such as table schemas and policy objects. For a list of policies, see Policies overview.

The storage system also includes a row store, which allows for the efficient intake of small portions of data and ensures this data is immediately available for query. When you enable streaming ingestion on your cluster, data is initially ingested to row store and then moved to column store extents.

Azure Data Explorer has a multi-hierarchy data cache system to ensure that the most relevant data is cached as closely as possible to the CPU. The cache system depends on the immutability of extents and works entirely with compressed data.

Data velocity is incredibly fast, with Azure Data Explorer capable of ingesting terabytes of data in minutes. This makes it perfect for handling massive amounts of data in real-time.

You can query petabytes of data in mere seconds, getting results back quickly. This is especially useful for applications where speed is crucial.

Azure Data Explorer provides high velocity, handling millions of events per second. This level of performance is unmatched in many data storage solutions.

Low latency is also a key feature, with results returned in seconds. This makes it ideal for applications that require quick data retrieval.

Ingesting data in different formats and structures is a breeze with Azure Data Explorer, allowing you to bring in data from various pipelines and sources.

The Azure SDK for Java is a powerful tool for interacting with data in Azure Kusto. It's a client library that allows you to bring data into Kusto and query information already stored in the database.

The library contains three main modules: data, ingest, and quickstart. The data module allows interaction with Kusto, including creating a connection, issuing control commands, and querying data.

The ingest module provides an easy way to bring data into Kusto. This is a big time-saver, as it simplifies the process of getting data into the database.

The quickstart module is a self-contained, configurable, and runnable sample app that makes it easy to get started with the SDK. It's perfect for beginners who want to try out the library without a lot of fuss.

Here are the main modules of the Azure SDK for Java:

Azure Data Explorer supports server-side stored functions, which enable you to perform complex data transformations on the server side.

This allows for efficient data processing without having to move data around, reducing latency and improving performance.

Continuous ingest is also supported, enabling you to continuously stream data into Azure Data Explorer from various sources.

With continuous export, you can also export data to Azure Data Lake store, making it easy to store and manage your data.

Ingestion time-mapping transformations on the server side allow you to transform data as it arrives, ensuring it's in the right format for analysis.

Update policies and precomputed scheduled aggregates with materialized views help you keep your data up-to-date and easily accessible for analysis.

Automatic Processing and Export is a game-changer for data management. Azure Data Explorer supports server-side stored functions.

These functions allow for complex data processing without having to write custom code. This can save a lot of time and effort.

Continuous ingest is also supported, enabling you to automatically bring in new data as it becomes available. This keeps your data up-to-date and ensures you have the most current information.

Ingestion time-mapping transformations can be applied on the server side, which is a huge advantage. This means you can manipulate data as it's being ingested, without having to worry about it later.

Continuous export to Azure Data Lake store is also possible, making it easy to store and manage your processed data. This is a great way to keep your data organized and easily accessible.

Update policies can be set up to ensure data is updated regularly, which is crucial for maintaining accuracy. Precomputed scheduled aggregates with materialized views can also be used to optimize data processing and reduce query time.

The get data experience in Azure Data Explorer makes the data ingestion process easy, fast, and intuitive. This is thanks to an intuitive and guided experience that helps you ramp-up quickly to start ingesting data.

You can ingest data from various sources and in various data formats, whether it's a one-time or continuous process. This flexibility is a big plus for data processing.

Table mappings and schema are auto-suggested, making it easy to modify them if needed. This saves you time and effort in setting up your database tables.

Azure Data Explorer's web UI is designed to be easy to use, allowing you to start working with your data right away.

Azure Data Explorer uses distributed data query technology for fast ad hoc analytics on large unstructured data sets.

This technology stores query-generated temporary data in aggregated RAM, which significantly speeds up query processing.

Relevant extents are marked on a query plan, providing snapshot isolation to ensure data consistency.

Fast and efficient queries are prioritized with short default timeouts to prevent queries from timing out.

Native support for cross-cluster queries minimizes inter-cluster data exchange, making it ideal for large-scale data processing.

Queries are just-in-time compiled into highly efficient machine code, using data statistics from all extents and tailored to column encoding specifics.