Apache Spark vs. Oracle Exadata

Overview
ProductRatingMost Used ByProduct SummaryStarting Price
Apache Spark
Score 8.9 out of 10
N/A
N/AN/A
Oracle Exadata
Score 9.3 out of 10
N/A
Oracle Exadata is an enterprise database platform that runs Oracle Database workloads of any scale and criticality with high performance, availability, and security. Exadata’s scale-out design employs optimizations that let transaction processing, analytics, machine learning, and mixed workloads run faster. Consolidating diverse Oracle Database workloads on Exadata platforms in enterprise data centers, Oracle Cloud Infrastructure (OCI), and multicloud environments helps organizations increase…
$2.90
Per Unit
Pricing
Apache SparkOracle Exadata
Editions & Modules
No answers on this topic
Database Server
$2.9032
Per Unit
Quarter Rack
$14.5162
Per Unit
Offerings
Pricing Offerings
Apache SparkOracle Exadata
Free Trial
NoNo
Free/Freemium Version
NoNo
Premium Consulting/Integration Services
NoNo
Entry-level Setup FeeNo setup feeNo setup fee
Additional Details
More Pricing Information
Features
Apache SparkOracle Exadata
Access Control and Security
Comparison of Access Control and Security features of Product A and Product B
Apache Spark
-
Ratings
Oracle Exadata
10.0
2 Ratings
13% above category average
Multi-User Support (named login)00 Ratings10.02 Ratings
Multiple Access Permission Levels (Create, Read, Delete)00 Ratings10.02 Ratings
Single Sign-On (SSO)00 Ratings10.01 Ratings
Data Warehouse
Comparison of Data Warehouse features of Product A and Product B
Apache Spark
-
Ratings
Oracle Exadata
9.3
2 Ratings
19% above category average
High-Volume Data Processing00 Ratings10.02 Ratings
Data Warehouse Management00 Ratings10.02 Ratings
Administrative Automation00 Ratings7.02 Ratings
Self-Optimization00 Ratings10.02 Ratings
Best Alternatives
Apache SparkOracle Exadata
Small Businesses

No answers on this topic

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Score 8.7 out of 10
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Cloudera Enterprise Data Hub
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Score 9.0 out of 10
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IBM Analytics Engine
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Score 7.8 out of 10
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User Ratings
Apache SparkOracle Exadata
Likelihood to Recommend
9.3
(24 ratings)
10.0
(23 ratings)
Likelihood to Renew
10.0
(1 ratings)
-
(0 ratings)
Usability
8.7
(4 ratings)
10.0
(2 ratings)
Support Rating
8.7
(4 ratings)
-
(0 ratings)
User Testimonials
Apache SparkOracle Exadata
Likelihood to Recommend
Apache
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.
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Oracle
  • First, get the database on Oracle. If you are in an Oracle stack, it would be much better to use the Oracle products. If you are driving a Ferrari, you wouldn’t put a Mercedes engine in it. If you are writing a query, you cannot rely on other brands. Since I'm an architect, when I look for a product, I look for performance.
  • The installation is easy because it comes out-of-the-box and you just start using it.
  • Previous to Oracle Exadata, we were using a normal Oracle RAC service. We were just waiting for this product to come out.
  • I'm currently writing a data warehouse on Exadata. Before this solution, we were aiming for this to be completed by 8 a.m., when our ETLs would finish. With the help of Exadata's special features, this was reduced to 3 a.m. This solution allows us to bring more data within the same time period. It provides us with more subject areas that provide more reports to our users. Our ETL times reduced to 65%, then to 50%.
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Pros
Apache
  • 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
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Oracle
  • High speed of SQL operations due to a unique design of Exadata with offloading of SQL processing to storage cells
  • Built-in High Availability of a DB server due to it's base architecture of a multi-node Oracle Real Application Cluster
  • High overall sever performance due to its use of a proprietary "Smart Cache" feature utilizing a high speed flash memory
  • Excellent scalability of a DB server by adding cluster nodes as well as expanding it into a network of serially connected clusters
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Cons
Apache
  • 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
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Oracle
  • Integrating with different types of databases can be a challenge
  • Having a hardware platform optimized to run specific database software surely comes with a price
  • Single vendor to support same hardware and software will make migrating to different hardware and/or software become a hassle
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Likelihood to Renew
Apache
Capacity of computing data in cluster and fast speed.
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Oracle
No answers on this topic
Usability
Apache
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
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Oracle
Excellent machine for your database needs . Don’t have to think twice if you have the budget to own it
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Support Rating
Apache
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.
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Oracle
No answers on this topic
Alternatives Considered
Apache
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.
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Oracle
Oracle Exadata Database Machine had the best performance overall hands down. It clearly beat the competition and we were seeing 1000X improvement on SAP HANA. Oracle Exadata Database Machine beat that without us refactoring our code. To achieve that in HANA, we had to refactor the code somewhat. Now this was for our limited POC of 5 use cases. Given the large number of stored procedures we had in Sybase, we need to capture more production metrics but we are seeing incredible performance.
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Return on Investment
Apache
  • 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
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Oracle
  • Single support from a single vendor with both machine and database from Oracle, which is costing us less.
  • With Exadata, we need less technical manpower and less technical support. A business transaction with the integrated and centralized database helps us focus on other business needs.
  • We don't need to buy additional licenses and Hardware for the next 3 to 5 years.
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