Apache Hive is database/data warehouse software that supports data querying and analysis of large datasets stored in the Hadoop distributed file system (HDFS) and other compatible systems, and is distributed under an open source license.
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Microsoft SQL Server
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Microsoft SQL Server is a relational database.
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Apache Hive
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Apache Hive
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To query a huge, distributed dataset, Apache Hive was built by Facebook. Unlike Apache Hive, Apache Spark is an in-memory computation engine, which is why it is significantly quicker than Apache Hive at querying large amounts of data. In contrast to Apache HBase, Apache Hive is …
Community support and ease of use -not deployment.
It enables querying and analyzing large amounts of data stored in HDFS, on the petabyte scale. It has a query language called HQL that transforms SQL queries into MapReduce jobs that run on Hadoop, and it is wonderful for the …
Apache Spark is similar in the sense that it too can be used to query and process large amounts of data through its Dataframe interface. Hive is better for short-term querying while Spark is better for persistent and long-term analysis. Another product is Impala. For our …
We have used a simple but necessary function such as merging certain data tables, which although they may be from different areas, complement each other or are necessary, you can use metadata if what you need is to validate the origin of your information and what impact it has, …
Apache Hadoop is built on top of the Hadoop File system so it gives its best when integrated with Hadoop. Data analysis and query optimization become very easy when used with Hadoop to perform Extract transform load operations. As Hadoop is a big data system and handles large …
We have used the system to migrate data either for new versions or because we will use another operating program, the software helps us to synchronize programs between different operating systems, a history of information can be kept constant, it can be sent to third parties …
Queries are easy to write and interface is similar to SQL so learning overhead is reduced. Multi user and data type support is provided. Can be easily scaled for very large amount of analytics. It is very flexible in terms of using file formats.
Apache Hive is a query language developed by Facebook to query over a large distributed dataset. Apache is a query engine that runs on top of HDFS, so it utilizes the resources of HDFS Hadoop setup, while Apache Spark is an in memory compute engine, and that's why [it is] much …
Besides Hive, I have used Google BigQuery, which is costly but have very high computation speed. Amazon Redshift is the another product, I used in my recent organisation. Both Redshift and BigQuery are managed solution whereas Hive needs to be managed
Hive and Spark have the same parent company hence they share a lot of common features. Hive follows SQL syntax while Spark has support for RDD, DataFrame API. DataFrame API supports both SQL syntax and has custom functions to perform the same functionality. Spark is faster and …
One of the major advantages of using Presto or the main reason why people use Presto (Teradata) is due to that fact it can support multiple data sources - which is lacking as in the case of Apache Hive. But still, most people who come from a Structured data-based background …
Easy to understand, well supported by the community, good documentation. However, it is possible that SAP Business Warehouse could be a good fit, too, even maybe better. I did not have the chance to try it though. We selected Apache Hive because it was far less expensive and …
For storing bulk amount of data in a tabular manner, and where there's no need need of primary key, or just in case, if redundant data is received, it will not cause a problem. For small amounts of data, it does run MR, so beware. If your intention is to use it as a …
I wasn't part of the evaluation process for Apache Hive. This was already implemented when I joined the company. I have worked with other big data plaftforms and I personally thinks most of them are quite comporable to one another. It really depends on what the company is going …
Apache Pig is probably the most direct technology to compare to Hive and has several different use cases to Hive. If you want to simplify processing tasks that run using MapReduce then Apache Pig may be a better tool for the job. However if you are going to be running many …
You could consider i did use Mysql since i worked with some websites that were using a mysql database. I could not give a side by side comparision since i don't use those like i use the Microsoft SQL , but so far it worked well. I prefer Microsoft SQL due to support and info …
UI of the Microsoft SQL Server makes it easy to use and learn. The better technical support and documentation give it an extra edge over other databases. The Microsoft ecosystem provides additional advantages, as we can seamlessly use other Microsoft products, such as Power …
Microsoft SQL Server is faster and more compatible, but it does cost more, so you're paying for those features. I use the others in many other places where critical transaction processing time and compatibility aren't of great concern.
Microsoft SQL is slower than MySQL and Access but far more feature-rich and reliable. Access is almost obsolete nowadays, so not too many people are considering it, but unless budget or an open-source ethos is a factor, Microsoft SQL is superior in every way. Many commonly used …
Microsoft SQL Server providers a more user friendly experience when it comes to Microsoft SQL Server components management via its unique SQL Server management Studio. It is also a production ready, resilient, highly available and tested database management system (DBMS). The …
The first database application taught when I was in school was Microsoft SQL Server. Microsoft SQL Server was used where I first started, so I had the opportunity to improve myself in MySQL. SQL is also used in my current workplace. It is widely used in very large projects due …
We have a few different DB's in the organization, including: Pervasive, Oracle, Db2, MySQL. Many of them are of limited use for one specific application. These don't really compare to MS SQL server. Oracle is heavy and cumbersome and overkill for smaller apps. Pervasive - …
Microsoft SQL Server is a comprehensive solution as transactional database, data warehouse, analytics, reporting, and ETL. It also integrates with the cloud well (Azure). The ease of use and setup makes this better than Oracle Database because the query syntax is also different …
I think both tools are really powerful and close to each other but since I moved to Europe I realized that most of the companies have been using SQL Server which in my opinion means something. The support from Microsoft I also consider a bit better and you can also find more …
Microsoft was the original creator of the SQL database, and thus, they still rule the market and drive innovation when it comes to data warehousing systems. It's comparable in price and allows you to retain the structured datasets that you lose when you change to a NoSQL …
[Microsoft] SQL Server has a much better community and professional support and is overall just a more reliable system with Microsoft behind it. I've used MySQL in the past and SQL Server has just become more comfortable for me and is my go to RDBMS.
Microsoft SQL Server and Oracle are both extremely powerful and scalable enterprise relational database platforms. Microsoft SQL sets itself apart with its ease of use and licensing and support model. Microsoft is good company to work with and they provide clear and …
It just boils down to why learn anther product when you are going to run across it so seldom. Developers determine what database engine I am going to need so I just tend to pick products for implementation that use a well know product that has lots of support resources …
The most known and widely used competitor of Microsoft SQL is most probably the open-source MySQL. If given the choice I would personally choose MySQL over Microsoft's SQL Server, mainly because it is totally free and open source, but also because it integrates better with …
[Microsoft SQL Server] offers a full solution, Inhouse Applications and hosted application continue to use SQL as backend database. Allows easy creation of development environments and continuous feature release.
All of the platforms have their own benefit. I was not the decision maker in selecting Microsoft SQL Server, as it was already being utilized when I joined the company, 7 years ago. I can say that I feel more comfortable with utilizing this platform as opposed to the other ones.
The free version is very powerfull and easy to install and use for small companies. Going to Professional and Standard, gives you all the support and the flexibility needed. It is known within the Database Administrator crew, and you can get support very easily over the …
Native to Windows and being required for other MS apps puts it above others in terms of usage. If we were not heavily dependent on Microsoft applications or OS, we might have considered other database solutions. It's an expensive solutions but it is a solid reliable solution. …
I was not too impressed with Oracle. Following the manual prohibited installation. They did provide a phone number and explained the manual was wrong and provided me with the correct information with which I was able to install the product. This was awhile back and I do not …
Microsoft SQL Server is one of the fastest RDBMS systems available in the market. Pricing is a bit on the higher side but all the features it provides pretty much justifies it. It can be integrated with a large number of frameworks thus enabling to work on multiple frameworks …
Microsoft SQL Server is still the industry standard for the type of development we do, and the types of applications that we use. Almost every developer or analyst we hire has at least a reasonable grounding in the use of SQL servers, and it is almost universally compatible …
Apache Hive shines for ad-hoc analysis and plugging into BI tools. Its SQL-like syntax allows for ease of use not for only for engineers but also for data analysts. Through our experience, there are probably more desirable tools to use if you are planning on integrating Hive into your processing pipeline.
Microsoft SQL Server is ideal for highly available SQL workloads by using SQL Server Always On availability groups. Microsoft SQL Server might not be appropriate for solutions which require a very low resource footprint, since it requires significant CPU cores and RAM memory as well as high IOPS, always depending on the usage scenario.
I think it is unlikely that sql server has disappointed someone, it is likely that someone will come initially discouraged if the needs and problems that occur are very challenging, but definitely have a SQL oriented system means having a solid base to work and on which maintain the their data securely
Hive is a very good big data analysis and ad-hoc query platform, which supports scaling also. The BI processes can be easily integrated with Hadoop via the Hive. It can deal with a much larger data set that traditional RDBMS can not. It is a "must-have" component of the big data domain.
SQL Server mostly 'just works' or generates error messages to help you sort out the trouble. You can usually count on the product to get the job done and keep an eye on your potential mistakes. Interaction with other Microsoft products makes operating as a Windows user pretty straight forward. Digging through the multitude of dialogs and wizards can be a pain, but the answer is usually there somewhere.
Apache Hive is a FOSS project and its open source. We need not definitely comment on anything about the support of open source and its developer community. But, it has got tremendous developer support, awesome documentation. I would justify the fact that much support can be gathered from the community backup.
We managed to handle most of our problems by looking into Microsoft's official documentation that has everything explained and almost every function has an example that illustrates in detail how a particular functionality works. Just like PowerShell has the ability to show you an example of how some cmdlet works, that is the case also here, and in my opinion, it is a very good practice and I like it.
Other than SQL taking quite a bit of time to actually install there are no problems with installation. Even on hardware that has good performance SQL can still take close to an hour to install a typical server with management and reporting services.
We have used a simple but necessary function such as merging certain data tables, which although they may be from different areas, complement each other or are necessary, you can use metadata if what you need is to validate the origin of your information and what impact it has, is also feasible.
Microsoft SQL is slower than MySQL and Access but far more feature-rich and reliable. Access is almost obsolete nowadays, so not too many people are considering it, but unless budget or an open-source ethos is a factor, Microsoft SQL is superior in every way. Many commonly used tools, like Crystal Reports, support it.
Increased accuracy - We went from multiple users having different versions of an Excel spreadsheet to a single source of truth for our reporting.
Increased Efficiency - We can now generate reports at any time from a single source rather than multiple users spending their time collating data and generating reports.
Improved Security - Enterprise level security on a dedicated server rather than financial files on multiple laptop hard drives.