Apache Pinot vs Azure HDInsight
Overview
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CLI (Node.js)
Manual
Detailed Comparison
It is a cloud-based service from Microsoft for big data analytics that helps organizations process large amounts of streaming or historical data. | Apache Pinot is a fast, scalable real-time analytics database. It is a column-oriented distributed Online Analytics Processing (OLAP) database designed for high concurrency and low latency. It can scan petabyte-scale data and produce results even as fast as single-digit milliseconds. |
Fully managed;
Full-spectrum;
Open-source analytics service in the cloud for enterprises | Real-time ingestion (Kafka, Kinesis, Pulsar);
Real-time upserts;
Batch ingestion (Flink, Hadoop, Spark);
SQL ingestion (Snowflake,
BigQuery);
Ingestion time pre-processing (transforms, flattening, rollups);
Flexible indexing types (star-tree, Bloom filter, forward, inverted,
geospatial, JSON, range, text, timestamp);
Automatic data replication and partitioning;
Encryption (on disk; transport);
Easy table management (backfills, dynamic re-indexing, minions for dynamic data layout changes);
Schema evolution;
Nested columns |
Statistics | |
GitHub Stars - | GitHub Stars 5.9K |
GitHub Forks - | GitHub Forks 1.4K |
Stacks 29 | Stacks 5 |
Followers 138 | Followers 3 |
Votes 0 | Votes 0 |
Integrations | |
What are some alternatives to Azure HDInsight, Apache Pinot?
Google BigQuery
Run super-fast, SQL-like queries against terabytes of data in seconds, using the processing power of Google's infrastructure. Load data with ease. Bulk load your data using Google Cloud Storage or stream it in. Easy access. Access BigQuery by using a browser tool, a command-line tool, or by making calls to the BigQuery REST API with client libraries such as Java, PHP or Python.
Apache Spark
Spark is a fast and general processing engine compatible with Hadoop data. It can run in Hadoop clusters through YARN or Spark's standalone mode, and it can process data in HDFS, HBase, Cassandra, Hive, and any Hadoop InputFormat. It is designed to perform both batch processing (similar to MapReduce) and new workloads like streaming, interactive queries, and machine learning.
Amazon Redshift
It is optimized for data sets ranging from a few hundred gigabytes to a petabyte or more and costs less than $1,000 per terabyte per year, a tenth the cost of most traditional data warehousing solutions.
Qubole
Qubole is a cloud based service that makes big data easy for analysts and data engineers.
Presto
Distributed SQL Query Engine for Big Data
Amazon EMR
It is used in a variety of applications, including log analysis, data warehousing, machine learning, financial analysis, scientific simulation, and bioinformatics.
Amazon Athena
Amazon Athena is an interactive query service that makes it easy to analyze data in Amazon S3 using standard SQL. Athena is serverless, so there is no infrastructure to manage, and you pay only for the queries that you run.
Apache Flink
Apache Flink is an open source system for fast and versatile data analytics in clusters. Flink supports batch and streaming analytics, in one system. Analytical programs can be written in concise and elegant APIs in Java and Scala.
lakeFS
It is an open-source data version control system for data lakes. It provides a “Git for data” platform enabling you to implement best practices from software engineering on your data lake, including branching and merging, CI/CD, and production-like dev/test environments.
Druid
Druid is a distributed, column-oriented, real-time analytics data store that is commonly used to power exploratory dashboards in multi-tenant environments. Druid excels as a data warehousing solution for fast aggregate queries on petabyte sized data sets. Druid supports a variety of flexible filters, exact calculations, approximate algorithms, and other useful calculations.
