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Azure Storage vs InfluxDB: What are the differences?
Azure Storage vs InfluxDB
Azure Storage and InfluxDB are two popular data storage solutions that offer distinct features and functionalities. In this comparison, we will discuss the key differences between the two.
Data Structure: Azure Storage is a general-purpose storage service that stores unstructured data such as files, blobs, and tables. In contrast, InfluxDB is a time-series database specifically designed to handle time-based data and efficiently store, retrieve, and analyze data points along with their timestamps.
Scalability and Performance: Azure Storage provides highly scalable storage that can handle large volumes of data and supports automatic scaling. It offers high availability and durability. InfluxDB, on the other hand, is optimized for high ingestion and query performance of time-series data. It utilizes an efficient indexing and storage mechanism to handle large volumes of time-stamped data efficiently.
Querying and Analysis: Azure Storage provides limited querying capabilities, primarily based on simple filters and searches within the stored data. InfluxDB, on the contrary, offers a powerful query language specifically designed for time-series data analysis. It supports complex queries, aggregations, and functions to perform advanced analysis on time-stamped data.
Integration with Ecosystem: Azure Storage integrates well with other Azure services and provides seamless data access across Azure services. It is also compatible with various programming languages and frameworks. InfluxDB, though comparatively less integrated with other services, offers a rich ecosystem of tools and integrations specifically tailored for time-series data analysis. It provides native support for popular open-source tools like Grafana and Telegraf.
Data Retention and Lifespan: Azure Storage allows indefinite data retention, providing long-term storage for historical data. InfluxDB, on the other hand, allows data retention policies to be set, defining the lifespan of data in the database. It provides efficient data retention management for time-series data, allowing the automatic deletion of old data based on defined policies.
Service Focus: Azure Storage is a multi-purpose cloud storage service that caters to various data storage needs, not limited to time-series data. InfluxDB, being a purpose-built time-series database, focuses primarily on providing efficient storage, retrieval, and analysis of time-based data. It is optimized for the specific requirements of time-series data use cases.
In summary, Azure Storage is a versatile storage service catering to a wide range of data storage needs, while InfluxDB is a specialized time-series database offering optimized storage, querying, and analysis capabilities for time-stamped data. The choice between the two depends on the specific requirements and use cases of the application.
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 Azure Storage
- All-in-one storage solution24
- Pay only for data used regardless of disk size15
- Shared drive mapping9
- Cost-effective2
- Cheapest hot and cloud storage2
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 Azure Storage
- Direct support is not provided by Azure storage2
Cons of InfluxDB
- Instability4
- Proprietary query language1
- HA or Clustering is only in paid version1
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