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Hadoop vs InfluxDB: What are the differences?
Introduction
This Markdown code provides a comparison between Hadoop and InfluxDB, highlighting their key differences.
Scalability: Hadoop is designed for distributed processing of large-scale data across a cluster of commodity hardware. It provides reliable and scalable storage and processing capabilities, making it suitable for big data applications. On the other hand, InfluxDB is primarily built for time-series data, offering high write and query performance for time-based data points. It is optimized for handling high-frequency data streams efficiently.
Data Model: Hadoop follows a file-based storage model, where data is stored in the form of files and processed using the MapReduce framework. It is best suited for batch processing and long-running jobs. InfluxDB, on the other hand, uses a specialized time-series data model with a columnar store. It efficiently organizes and indexes time-stamped data, making it ideal for real-time analytics and monitoring applications.
Query Language: Hadoop primarily utilizes MapReduce programming paradigm for data processing, which requires writing complex Map and Reduce functions in languages like Java or Python. InfluxDB, on the other hand, provides a high-level query language called InfluxQL. It simplifies working with time-series data by allowing users to perform common aggregation, filtering, and transformation operations directly using SQL-like syntax.
Data Retention: Hadoop does not have built-in data retention policies and relies on external tools or manual management for data retention. InfluxDB, in contrast, offers built-in data retention policies that enable automatic deletion of old data based on specified criteria. This feature is particularly useful in time-series data scenarios where old data becomes less relevant as time progresses.
Cluster Management: Hadoop requires additional tools like Apache ZooKeeper or Apache Ambari for cluster management and coordination. These tools help in managing cluster resources, monitoring and troubleshooting, and ensuring high availability. InfluxDB, being a standalone time-series database, does not require external tools for cluster management. It can easily be deployed as a single instance or in a highly available cluster using InfluxDB Enterprise.
Secondary Indexing: Hadoop does not provide native support for secondary indexing, making it more challenging to efficiently retrieve specific data subsets from a large dataset. InfluxDB, on the other hand, offers robust support for secondary indexing, allowing users to efficiently query time-series data based on multiple dimensions or tags. This feature enhances the flexibility and performance of queries on large volumes of time-series data.
In Summary, Hadoop is a distributed processing framework designed for large-scale data processing, while InfluxDB is a specialized time-series database optimized for high-frequency time-stamped data. Hadoop uses a file-based storage model and requires complex programming for data processing, whereas InfluxDB utilizes a columnar store and provides a high-level query language for time-series data. InfluxDB offers built-in data retention and secondary indexing capabilities, simplifying management and enhancing query performance for time-series data.
For a property and casualty insurance company, we currently use MarkLogic and Hadoop for our raw data lake. Trying to figure out how snowflake fits in the picture. Does anybody have some good suggestions/best practices for when to use and what data to store in Mark logic versus Snowflake versus a hadoop or all three of these platforms redundant with one another?
for property and casualty insurance company we current Use marklogic and Hadoop for our raw data lake. Trying to figure out how snowflake fits in the picture. Does anybody have some good suggestions/best practices for when to use and what data to store in Mark logic versus snowflake versus a hadoop or all three of these platforms redundant with one another?
As i see it, you can use Snowflake as your data warehouse and marklogic as a data lake. You can add all your raw data to ML and curate it to a company data model to then supply this to Snowflake. You could try to implement the dw functionality on marklogic but it will just cost you alot of time. If you are using Aws version of Snowflake you can use ML spark connector to access the data. As an extra you can use the ML also as an Operational report system if you join it with a Reporting tool lie PowerBi. With extra apis you can also provide data to other systems with ML as source.
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 Hadoop
- Great ecosystem39
- One stack to rule them all11
- Great load balancer4
- Amazon aws1
- Java syntax1
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 Hadoop
Cons of InfluxDB
- Instability4
- Proprietary query language1
- HA or Clustering is only in paid version1
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