Apache Spark vs. Oracle Data Integrator (ODI)

Overview
ProductRatingMost Used ByProduct SummaryStarting Price
Apache Spark
Score 8.6 out of 10
N/A
N/AN/A
Oracle Data Integrator (ODI)
Score 8.3 out of 10
N/A
Oracle Data Integrator is an ELT data integrator designed with interoperability other Oracle programs. The program focuses on a high-performance capacity to support Big Data use within Oracle.N/A
Pricing
Apache SparkOracle Data Integrator (ODI)
Editions & Modules
No answers on this topic
No answers on this topic
Offerings
Pricing Offerings
Apache SparkOracle Data Integrator (ODI)
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
Community Pulse
Apache SparkOracle Data Integrator (ODI)
Top Pros
Top Cons
Features
Apache SparkOracle Data Integrator (ODI)
Data Source Connection
Comparison of Data Source Connection features of Product A and Product B
Apache Spark
-
Ratings
Oracle Data Integrator (ODI)
9.6
11 Ratings
14% above category average
Connect to traditional data sources00 Ratings9.911 Ratings
Connecto to Big Data and NoSQL00 Ratings9.39 Ratings
Data Transformations
Comparison of Data Transformations features of Product A and Product B
Apache Spark
-
Ratings
Oracle Data Integrator (ODI)
9.9
11 Ratings
16% above category average
Simple transformations00 Ratings9.911 Ratings
Complex transformations00 Ratings9.911 Ratings
Data Modeling
Comparison of Data Modeling features of Product A and Product B
Apache Spark
-
Ratings
Oracle Data Integrator (ODI)
9.2
11 Ratings
12% above category average
Data model creation00 Ratings9.310 Ratings
Metadata management00 Ratings9.510 Ratings
Business rules and workflow00 Ratings9.111 Ratings
Collaboration00 Ratings8.510 Ratings
Testing and debugging00 Ratings9.311 Ratings
Data Governance
Comparison of Data Governance features of Product A and Product B
Apache Spark
-
Ratings
Oracle Data Integrator (ODI)
9.1
9 Ratings
9% above category average
Integration with data quality tools00 Ratings9.59 Ratings
Integration with MDM tools00 Ratings8.77 Ratings
Best Alternatives
Apache SparkOracle Data Integrator (ODI)
Small Businesses

No answers on this topic

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Score 9.5 out of 10
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Score 9.7 out of 10
InfoSphere
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Score 8.4 out of 10
Enterprises
IBM Analytics Engine
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Score 9.3 out of 10
InfoSphere
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Score 8.4 out of 10
All AlternativesView all alternativesView all alternatives
User Ratings
Apache SparkOracle Data Integrator (ODI)
Likelihood to Recommend
9.8
(24 ratings)
8.0
(29 ratings)
Likelihood to Renew
10.0
(1 ratings)
10.0
(4 ratings)
Usability
10.0
(3 ratings)
-
(0 ratings)
Support Rating
8.7
(4 ratings)
-
(0 ratings)
Implementation Rating
-
(0 ratings)
7.0
(1 ratings)
User Testimonials
Apache SparkOracle Data Integrator (ODI)
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
Oracle Data Integrator is well suited in all the situations where you need to integrate data from and to different systems/technologies/environments or to schedule some tasks. I've used it on Oracle Database (Data Warehouses or Data Marts), with great loading and transforming performances to accomplish any kind of relational task. This is true for all Oracle applications (like Hyperion Planning, Hyperion Essbase, Hyperion Financial Management, and so on). I've also used it to manage files on different operating systems, to execute procedures in various languages and to read and write data from and to non-Oracle technologies, and I can confirm that its performances have always been very good. It can become less appropriate depending on the expenses that can be afforded by the customer since its license costs are quite high.
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Pros
Apache
  • Apache Spark makes processing very large data sets possible. It handles these data sets in a fairly quick manner.
  • Apache Spark does a fairly good job implementing machine learning models for larger data sets.
  • Apache Spark seems to be a rapidly advancing software, with the new features making the software ever more straight-forward to use.
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Oracle
  • Oracle Data Integrator nearly addresses every data issue that one can expect. Oracle Data Integrator is tightly integrated to the Oracle Suite of products. This is one of the major strengths of Oracle Data Integrator. Oracle Data Integrator is part of the Oracle Business Intelligence Applications Suite - which is highly used by various industries. This tool replaced Informatica ETL in Oracle Business Intelligence Applications Suite.
  • Oracle Data Integrator comes with many pre-written data packages. If one has to load data from Excel to Oracle Database, there is a package that is ready available for them - cutting down lot of effort on writing the code. Similarly, there are packages for Oracle to SQL, SQL to Oracle and all other possible combinations. Developers love this feature.
  • Oracle Data Integrator relies highly on the database for processing. This is actually an ELT tool rather than an ETL tool. It first loads all the data into target instance and then transforms it at the expense of database resources. This light footprint makes this tool very special.
  • The other major advantage of Oracle Data Integrator, like any other Oracle products, is a readily available developer pool. As all Oracle products are free to download for demo environments, many organizations prefer to play around with a product before purchasing it. Also, Oracle support and community is a big advantage compared to other vendors.
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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
  • ODI does not have an intuitive user interface. It is powerful, but difficult to figure out at first. There is a significant learning curve between usability, proficiency, and mastery of the tool.
  • ODI contains some frustrating bugs. It is Java based and has some caching issues, often requiring you to restart the program before you see your code changes stick.
  • ODI does not have a strong versioning process. It is not intuitive to keep an up to date repository of versioned code packages. This can create versioning issues between environments if you do not have a strong external code versioning process.
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Likelihood to Renew
Apache
Capacity of computing data in cluster and fast speed.
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Oracle
It is maturing and over time will have a good pool of resources. Each new version has addressed the issues of the previous ones. Its getting better and bigger.
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Usability
Apache
The only thing I dislike about spark's usability is the learning curve, there are many actions and transformations, however, its wide-range of uses for ETL processing, facility to integrate and it's multi-language support make this library a powerhouse for your data science solutions. It has especially aided us with its lightning-fast processing times.
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Oracle
No answers on this topic
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
All the above systems work quite well on big data transformations whereas Spark really shines with its bigger API support and its ability to read from and write to multiple data sources. Using Spark one can easily switch between declarative versus imperative versus functional type programming easily based on the situation. Also it doesn't need special data ingestion or indexing pre-processing like Presto. Combining it with Jupyter Notebooks (https://github.com/jupyter-incubator/sparkmagic), one can develop the Spark code in an interactive manner in Scala or Python
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Oracle
I have used Trifacta Google Data Prep quite a bit. We use Google Cloud Platform across our organization. The tools are very comparable in what they offer. I would say Data Prep has a slight edge in usability and a cleaner UI, but both of the tools have comparable toolsets.
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Return on Investment
Apache
  • Faster turn around on feature development, we have seen a noticeable improvement in our agile development since using Spark.
  • Easy adoption, having multiple departments use the same underlying technology even if the use cases are very different allows for more commonality amongst applications which definitely makes the operations team happy.
  • Performance, we have been able to make some applications run over 20x faster since switching to Spark. This has saved us time, headaches, and operating costs.
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Oracle
  • From a business intelligence perspective, it allows us to provide users with the necessary data and information to make informed decisions.
  • Compared with other Oracle products and licensing, I do not think the pricing was unreasonable.
  • It is part of a larger install, so for ease of use, we purchased it with other Oracle products.
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