Databend is an open-source Elastic and Workload-Aware modern cloud data warehouse.
Databend uses the latest techniques in vectorized query processing to allow you to do blazing-fast data analytics on object storage(S3, Azure Blob or MinIO).
Databend completely separates storage from compute, which allows you easily scale up or scale down based on your application's needs.
Databend leverages data-level parallelism(Vectorized Query Execution) and instruction-level parallelism(SIMD) technology, offering blazing performance data analytics.
Databend stores data with snapshots. It's easy to query, clone, and restore historical data in tables.
Support for Semi-Structured Data
Databend supports ingestion of semi-structured data in various formats like CSV, JSON, and Parquet, which are located in the cloud or your local file system; Databend also supports semi-structured data types: ARRAY, MAP, JSON, which is easy to import and operate on semi-structured.
Databend is ANSI SQL compliant and MySQL/ClickHouse wire protocol compatible, making it easy to connect with existing tools(MySQL Client, ClickHouse Client, Vector, DBeaver, Jupyter, JDBC, etc.).
Easy to Use
Databend has no indexes to build, no manual tuning required, no manual figuring out partitions or shard data, it’s all done for you as data is loaded into the table.
The fastest way to try Databend, Databend Cloud
Prepare the image (once) from Docker Hub (this will download about 170 MB data):
docker pull datafuselabs/databend
To run Databend quickly:
docker run --net=host datafuselabs/databend
Databend is an open source project, you can help with ideas, code, or documentation, we appreciate any efforts that help us to make the project better! Once the code is merged, your name will be stored in the system.contributors table forever.
To get started with contributing:
For general help in using Databend, please refer to the official documentation. For additional help, you can use one of these channels to ask a question:
Source code: https://github.com/datafuselabs/databend
License: Apache-2.0 license
In today’s market reliable data is worth its weight in gold, and having a single source of truth for business-related queries is a must-have for organizations of all sizes. For decades companies have turned to data warehouses to consolidate operational and transactional information, but many existing data warehouses are no longer able to keep up with the data demands of the current business climate. They are hard to scale, inflexible, and simply incapable of handling the large volumes of data and increasingly complex queries.
These days organizations need a faster, more efficient, and modern data warehouse that is robust enough to handle large amounts of data and multiple users while simultaneously delivering real-time query results. And that is where hybrid cloud comes in. As increasing volumes of data are being generated and stored in the cloud, enterprises are rethinking their strategies for data warehousing and analytics. Hybrid cloud data warehouses allow you to utilize existing resources and architectures while streamlining your data and cloud goals.
#cloud #data analytics #business intelligence #hybrid cloud #data warehouse #data storage #data management solutions #master data management #data warehouse architecture #data warehouses
A multi-cloud approach is nothing but leveraging two or more cloud platforms for meeting the various business requirements of an enterprise. The multi-cloud IT environment incorporates different clouds from multiple vendors and negates the dependence on a single public cloud service provider. Thus enterprises can choose specific services from multiple public clouds and reap the benefits of each.
Given its affordability and agility, most enterprises opt for a multi-cloud approach in cloud computing now. A 2018 survey on the public cloud services market points out that 81% of the respondents use services from two or more providers. Subsequently, the cloud computing services market has reported incredible growth in recent times. The worldwide public cloud services market is all set to reach $500 billion in the next four years, according to IDC.
By choosing multi-cloud solutions strategically, enterprises can optimize the benefits of cloud computing and aim for some key competitive advantages. They can avoid the lengthy and cumbersome processes involved in buying, installing and testing high-priced systems. The IaaS and PaaS solutions have become a windfall for the enterprise’s budget as it does not incur huge up-front capital expenditure.
However, cost optimization is still a challenge while facilitating a multi-cloud environment and a large number of enterprises end up overpaying with or without realizing it. The below-mentioned tips would help you ensure the money is spent wisely on cloud computing services.
Most organizations tend to get wrong with simple things which turn out to be the root cause for needless spending and resource wastage. The first step to cost optimization in your cloud strategy is to identify underutilized resources that you have been paying for.
Enterprises often continue to pay for resources that have been purchased earlier but are no longer useful. Identifying such unused and unattached resources and deactivating it on a regular basis brings you one step closer to cost optimization. If needed, you can deploy automated cloud management tools that are largely helpful in providing the analytics needed to optimize the cloud spending and cut costs on an ongoing basis.
Another key cost optimization strategy is to identify the idle computing instances and consolidate them into fewer instances. An idle computing instance may require a CPU utilization level of 1-5%, but you may be billed by the service provider for 100% for the same instance.
Every enterprise will have such non-production instances that constitute unnecessary storage space and lead to overpaying. Re-evaluating your resource allocations regularly and removing unnecessary storage may help you save money significantly. Resource allocation is not only a matter of CPU and memory but also it is linked to the storage, network, and various other factors.
The key to efficient cost reduction in cloud computing technology lies in proactive monitoring. A comprehensive view of the cloud usage helps enterprises to monitor and minimize unnecessary spending. You can make use of various mechanisms for monitoring computing demand.
For instance, you can use a heatmap to understand the highs and lows in computing visually. This heat map indicates the start and stop times which in turn lead to reduced costs. You can also deploy automated tools that help organizations to schedule instances to start and stop. By following a heatmap, you can understand whether it is safe to shut down servers on holidays or weekends.
#cloud computing services #all #hybrid cloud #cloud #multi-cloud strategy #cloud spend #multi-cloud spending #multi cloud adoption #why multi cloud #multi cloud trends #multi cloud companies #multi cloud research #multi cloud market
If you accumulate data on which you base your decision-making as an organization, you should probably think about your data architecture and possible best practices.
If you accumulate data on which you base your decision-making as an organization, you most probably need to think about your data architecture and consider possible best practices. Gaining a competitive edge, remaining customer-centric to the greatest extent possible, and streamlining processes to get on-the-button outcomes can all be traced back to an organization’s capacity to build a future-ready data architecture.
In what follows, we offer a short overview of the overarching capabilities of data architecture. These include user-centricity, elasticity, robustness, and the capacity to ensure the seamless flow of data at all times. Added to these are automation enablement, plus security and data governance considerations. These points from our checklist for what we perceive to be an anticipatory analytics ecosystem.
#big data #data science #big data analytics #data analysis #data architecture #data transformation #data platform #data strategy #cloud data platform #data acquisition
Databases store data in a structured form. The structure makes it possible to find and edit data. With their structured structure, databases are used for data management, data storage, data evaluation, and targeted processing of data.
In this sense, data is all information that is to be saved and later reused in various contexts. These can be date and time values, texts, addresses, numbers, but also pictures. The data should be able to be evaluated and processed later.
The amount of data the database could store is limited, so enterprise companies tend to use data warehouses, which are versions for huge streams of data.
#data-warehouse #data-lake #cloud-data-warehouse #what-is-aws-data-lake #data-science #data-analytics #database #big-data #web-monetization
The opportunities big data offers also come with very real challenges that many organizations are facing today. Often, it’s finding the most cost-effective, scalable way to store and process boundless volumes of data in multiple formats that come from a growing number of sources. Then organizations need the analytical capabilities and flexibility to turn this data into insights that can meet their specific business objectives.
This Refcard dives into how a data lake helps tackle these challenges at both ends — from its enhanced architecture that’s designed for efficient data ingestion, storage, and management to its advanced analytics functionality and performance flexibility. You’ll also explore key benefits and common use cases.
As technology continues to evolve with new data sources, such as IoT sensors and social media churning out large volumes of data, there has never been a better time to discuss the possibilities and challenges of managing such data for varying analytical insights. In this Refcard, we dig deep into how data lakes solve the problem of storing and processing enormous amounts of data. While doing so, we also explore the benefits of data lakes, their use cases, and how they differ from data warehouses (DWHs).
This is a preview of the Getting Started With Data Lakes Refcard. To read the entire Refcard, please download the PDF from the link above.
#big data #data analytics #data analysis #business analytics #data warehouse #data storage #data lake #data lake architecture #data lake governance #data lake management