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Cloudera Enterprise vs InfluxDB: What are the differences?
<Cloudera Enterprise vs InfluxDB>
1. **Data Processing Model**: Cloudera Enterprise focuses on distributed data processing using tools like Hadoop and Spark, while InfluxDB is specifically designed for time-series data storage and retrieval, tailored for IoT and monitoring applications.
2. **Scalability**: Cloudera Enterprise offers scalability for large-scale data operations by utilizing Hadoop's distributed architecture, whereas InfluxDB is known for its high scalability in handling vast amounts of time-series data with high write and query throughput.
3. **Ecosystem Integration**: Cloudera Enterprise comes with a rich ecosystem of tools and connectors for various data sources and applications, facilitating an end-to-end data analytics solution. In contrast, InfluxDB has integrations with popular monitoring and visualization tools like Grafana, but may require additional connectors for other data sources.
4. **Consistency Model**: Cloudera Enterprise supports strong consistency in distributed data processing, ensuring data integrity across clusters, while InfluxDB prioritizes availability and partition tolerance in its query operations, sacrificing some level of consistency.
5. **Use Cases**: Cloudera Enterprise caters to a wide range of big data processing and analytics use cases across industries, including batch processing, machine learning, and data warehousing. On the other hand, InfluxDB is best suited for real-time monitoring, sensor data analysis, and IoT applications where time-series data plays a crucial role.
6. **Commercial Support**: Cloudera offers comprehensive commercial support packages for enterprise customers, providing services like training, consulting, and technical support, whereas InfluxDB provides open-source community support with enterprise options for those needing additional features and support.
In Summary, Cloudera Enterprise and InfluxDB differ in their focus on data processing models, scalability, ecosystem integration, consistency models, use cases, and commercial support offerings.
I have a lot of data that's currently sitting in a MariaDB database, a lot of tables that weigh 200gb with indexes. Most of the large tables have a date column which is always filtered, but there are usually 4-6 additional columns that are filtered and used for statistics. I'm trying to figure out the best tool for storing and analyzing large amounts of data. Preferably self-hosted or a cheap solution. The current problem I'm running into is speed. Even with pretty good indexes, if I'm trying to load a large dataset, it's pretty slow.
Druid Could be an amazing solution for your use case, My understanding, and the assumption is you are looking to export your data from MariaDB for Analytical workload. It can be used for time series database as well as a data warehouse and can be scaled horizontally once your data increases. It's pretty easy to set up on any environment (Cloud, Kubernetes, or Self-hosted nix system). Some important features which make it a perfect solution for your use case. 1. It can do streaming ingestion (Kafka, Kinesis) as well as batch ingestion (Files from Local & Cloud Storage or Databases like MySQL, Postgres). In your case MariaDB (which has the same drivers to MySQL) 2. Columnar Database, So you can query just the fields which are required, and that runs your query faster automatically. 3. Druid intelligently partitions data based on time and time-based queries are significantly faster than traditional databases. 4. Scale up or down by just adding or removing servers, and Druid automatically rebalances. Fault-tolerant architecture routes around server failures 5. Gives ana amazing centralized UI to manage data sources, query, tasks.
We are building an IOT service with heavy write throughput and fewer reads (we need downsampling records). We prefer to have good reliability when comes to data and prefer to have data retention based on policies.
So, we are looking for what is the best underlying DB for ingesting a lot of data and do queries easily
We had a similar challenge. We started with DynamoDB, Timescale, and even InfluxDB and Mongo - to eventually settle with PostgreSQL. Assuming the inbound data pipeline in queued (for example, Kinesis/Kafka -> S3 -> and some Lambda functions), PostgreSQL gave us a We had a similar challenge. We started with DynamoDB, Timescale and even InfluxDB and Mongo - to eventually settle with PostgreSQL. Assuming the inbound data pipeline in queued (for example, Kinesis/Kafka -> S3 -> and some Lambda functions), PostgreSQL gave us better performance by far.
Druid is amazing for this use case and is a cloud-native solution that can be deployed on any cloud infrastructure or on Kubernetes. - Easy to scale horizontally - Column Oriented Database - SQL to query data - Streaming and Batch Ingestion - Native search indexes It has feature to work as TimeSeriesDB, Datawarehouse, and has Time-optimized partitioning.
if you want to find a serverless solution with capability of a lot of storage and SQL kind of capability then google bigquery is the best solution for that.
I chose TimescaleDB because to be the backend system of our production monitoring system. We needed to be able to keep track of multiple high cardinality dimensions.
The drawbacks of this decision are our monitoring system is a bit more ad hoc than it used to (New Relic Insights)
We are combining this with Grafana for display and Telegraf for data collection
Pros of Cloudera Enterprise
- Scalability1
- Multicloud1
- Hybrid cloud1
- Easily management1
- Cheeper1
Pros of InfluxDB
- Time-series data analysis59
- Easy setup, no dependencies30
- Fast, scalable & open source24
- Open source21
- Real-time analytics20
- Continuous Query support6
- Easy Query Language5
- HTTP API4
- Out-of-the-box, automatic Retention Policy4
- Offers Enterprise version1
- Free Open Source version1
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Cons of Cloudera Enterprise
Cons of InfluxDB
- Instability4
- Proprietary query language1
- HA or Clustering is only in paid version1
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