Apache Hive vs. Apache Spark vs. Oracle SQL Developer

Software work execution is on a large scale, it is good to use for new projects or organizational changes, data lineage mapping has always been dubious but this one has had good results. You can store and synchronize data from different departments, the storage process can be manual but it is best automated.

Incentivized

Well suited: To most of the local run of datasets and non-prod systems - scalability is not a problem at all. Including data from multiple types of data sources is an added advantage. MLlib is a decently nice built-in library that can be used for most of the ML tasks. Less appropriate: We had to work on a RecSys where the music dataset that we used was around 300+Gb in size. We faced memory-based issues. Few times we also got memory errors. Also the MLlib library does not have support for advanced analytics and deep-learning frameworks support. Understanding the internals of the working of Apache Spark for beginners is highly not possible.

Incentivized

Almost all development activities (the tool is called "SQL Developer", not "DBA Toolset") can be done easily and quick with [Oracle] SQL Developer. From data model creation (tables, views) to development (creation of procedures, functions, packages) and then testing (SQL Developer includes an easy to use debugger), all tasks can be performed in a single tool.

It may not be as complete as other solutions for DBA tasks like instance monitoring, but it is usually OK for development and testing environments if you want to do some basic troubleshooting.

Incentivized

• Apache Hive allows use to write expressive solutions to complex problems thanks to its SQL-like syntax.
• Relatively easy to set up and start using.
• Very little ramp-up to start using the actual product, documentation is very thorough, there is an active community, and the code base is constantly being improved.

Incentivized

• Rich APIs for data transformation making for very each to transform and prepare data in a distributed environment without worrying about memory issues
• Faster in execution times compare to Hadoop and PIG Latin
• Easy SQL interface to the same data set for people who are comfortable to explore data in a declarative manner
• Interoperability between SQL and Scala / Python style of munging data

Incentivized

• Object Browser in SQL Developer allows you to explore the contents of your database using the connection tree.
• The SQL Worksheet is an editor that allows for execution of SQL statements, scripts, and PL/SQL anonymous blocks. SELECT statements can be executed to return results in a spreadsheet-like 'grid' or can be executed as a script such to emulate SQL*Plus behavior and output
• DBA Console allows users with administrative privileges to access DBA features such as database init file configuration, RMAN backup, storage, etc.

Incentivized

• Some queries, particularly complex joins, are still quite slow and can take hours
• Previous jobs and queries are not stored sometimes
• Switching to Impala can sometimes be time-consuming (i.e. the system hangs, or is slow to respond).
• Sometimes, directories and tables don't load properly which causes confusion

Incentivized

• Memory management. Very weak on that.
• PySpark not as robust as scala with spark.
• spark master HA is needed. Not as HA as it should be.
• Locality should not be a necessity, but does help improvement. But would prefer no locality

Incentivized

• Inability to run multiple queries on the same database. You can only run one query on a given database.
• Analytical models created from complex tables isn't accurate, and needs work.
• Inability to view multiple tables of a database side-by-side. When trying to find correlations between tables, it would help to be able to see them at once on the same page.

Incentivized

Since I do not know the second data warehouse solution that integrate with HDFS as well as Hive.

Capacity of computing data in cluster and fast speed.

We had already thought of changing to TOAD, but we decided to stick with Oracle SQL Developer until the end.

Incentivized

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.

Incentivized

If the team looking to use Apache Spark is not used to debug and tweak settings for jobs to ensure maximum optimizations, it can be frustrating. However, the documentation and the support of the community on the internet can help resolve most issues. Moreover, it is highly configurable and it integrates with different tools (eg: it can be used by dbt core), which increase the scenarios where it can be used

Incentivized

Oracle SQL Developer is very easy to use and there are a wide range of courses available which can help you get started just within a day. Data can be exported in multiple formats based on user requirements. Organizational data can be stored and management effectively using Oracle SQL Developer. All the data, tables, sequences, indexes can be easily created and updated in Oracle SQL Developer.

Incentivized

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.

Incentivized

1. It integrates very well with scala or python. 2. It's very easy to understand SQL interoperability. 3. Apache is way faster than the other competitive technologies. 4. The support from the Apache community is very huge for Spark. 5. Execution times are faster as compared to others. 6. There are a large number of forums available for Apache Spark. 7. The code availability for Apache Spark is simpler and easy to gain access to. 8. Many organizations use Apache Spark, so many solutions are available for existing applications.

Large user community support

Incentivized

Just download and uncompress!

Incentivized

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

Incentivized

Spark in comparison to similar technologies ends up being a one stop shop. You can achieve so much with this one framework instead of having to stitch and weave multiple technologies from the Hadoop stack, all while getting incredibility performance, minimal boilerplate, and getting the ability to write your application in the language of your choosing.

Incentivized

I have started to use Toad for Oracle recently because it is easier to sort and filter results, due to their memory sort feature that puts the results from your query in memory so that you don't have to rerun your query. I have used SQL Developer to easily update records in tables that I need to fix. I haven't found an easy way to do this in Toad other than writing SQL insert statements.

Incentivized

• Apache hive is secured and scalable solution that helps in increasing the overall organization productivity.
• Apache hive can handle and process large amount of data in a sufficient time manner.
• It simplifies writing SQL queries, hence helping the organization as most companies use SQL for all query jobs.

Incentivized

• Business leaders are able to take data driven decisions
• Business users are able access to data in near real time now . Before using spark, they had to wait for at least 24 hours for data to be available
• Business is able come up with new product ideas

Incentivized

• It gives 100% return on investment as it is free of cost.
• No need to have multiple tools for each database
• Considering the employee training, so one can save money on training, as it is not very hard to use so still savings.

Incentivized