Apache Impala vs Stroom
Overview
Share your Stack
Help developers discover the tools you use. Get visibility for your team's tech choices and contribute to the community's knowledge.
CLI (Node.js)
Manual
Detailed Comparison
Impala is a modern, open source, MPP SQL query engine for Apache Hadoop. Impala is shipped by Cloudera, MapR, and Amazon. With Impala, you can query data, whether stored in HDFS or Apache HBase – including SELECT, JOIN, and aggregate functions – in real time. | It is a data processing, storage and analysis platform. It is scalable - just add more CPUs / servers for greater throughput. It is suitable for processing high volume data such as system logs, to provide valuable insights into IT performance and usage. |
Do BI-style Queries on Hadoop;Unify Your Infrastructure;Implement Quickly;Count on Enterprise-class Security;Retain Freedom from Lock-in;Expand the Hadoop User-verse | Receive and store large volumes of data such as native format logs. Ingested data is always available in its raw form; Create sequences of XSL and text operations, in order to normalise or export data in any format. It is possible to enrich data using lookups and reference data; Easily add new data formats and debug the transformations if they don't work as expected; Create multiple indexes with different retention periods. These can be sharded across your cluster; Run queries against your indexes or statistics and view the results within custom visualisations; Record counts or values of items over time |
Statistics | |
GitHub Stars 34 | GitHub Stars 452 |
GitHub Forks 33 | GitHub Forks 62 |
Stacks 145 | Stacks 1 |
Followers 301 | Followers 3 |
Votes 18 | Votes 0 |
Pros & Cons | |
Pros
| No community feedback yet |
Integrations | |
What are some alternatives to Apache Impala, Stroom?
Papertrail
Papertrail helps detect, resolve, and avoid infrastructure problems using log messages. Papertrail's practicality comes from our own experience as sysadmins, developers, and entrepreneurs.
Logmatic
Get a clear overview of what is happening across your distributed environments, and spot the needle in the haystack in no time. Build dynamic analyses and identify improvements for your software, your user experience and your business.
Loggly
It is a SaaS solution to manage your log data. There is nothing to install and updates are automatically applied to your Loggly subdomain.
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.
Logentries
Logentries makes machine-generated log data easily accessible to IT operations, development, and business analysis teams of all sizes. With the broadest platform support and an open API, Logentries brings the value of log-level data to any system, to any team member, and to a community of more than 25,000 worldwide users.
Logstash
Logstash is a tool for managing events and logs. You can use it to collect logs, parse them, and store them for later use (like, for searching). If you store them in Elasticsearch, you can view and analyze them with Kibana.
Graylog
Centralize and aggregate all your log files for 100% visibility. Use our powerful query language to search through terabytes of log data to discover and analyze important information.
Presto
Distributed SQL Query Engine for Big Data
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.
Sematext
Sematext pulls together performance monitoring, logs, user experience and synthetic monitoring that tools organizations need to troubleshoot performance issues faster.
