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Amazon Athena vs. Google BigQuery vs. SQL Server Integration Services (SSIS)

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    Overview
    ProductRatingMost Used ByProduct SummaryStarting Price

    Amazon Athena

    Score7.5 out of 10
    N/AAmazon Athena is an interactive query service that makes it easy to analyze data directly in Amazon S3 using standard SQL. With a few clicks in the AWS Management Console, customers can point Athena at their data stored in S3 and begin using standard SQL to run ad-hoc queries and get results in seconds. Athena is serverless, so there is no infrastructure to setup or manage, and customers pay only for the queries they run. You can use Athena to process logs, perform ad-hoc analysis, and run…

    $5

    per TB of Data Scanned

    Google BigQuery

    Score8.8 out of 10
    N/AGoogle's BigQuery is part of the Google Cloud Platform, a database-as-a-service (DBaaS) supporting the querying and rapid analysis of enterprise data.

    $6.25

    per TiB (after the 1st 1 TiB per month, which is free)

    SSIS

    Score8 out of 10
    N/AMicrosoft's SQL Server Integration Services (SSIS) is a data integration solution.N/A
    Pricing
    Amazon AthenaGoogle BigQuerySSIS
    Editions & Modules
    Price per Query
    $5.00
    per TB of Data Scanned
    Standard edition
    $0.04 / slot hour
    Enterprise edition
    $0.06 / slot hour
    Enterprise Plus edition
    $0.10 / slot hour
    No answers on this topic
    Offerings
    Pricing Offerings
    Amazon AthenaGoogle BigQuerySSIS
    Free Trial
    NoYesNo
    Free/Freemium Version
    NoYesNo
    Premium Consulting/Integration Services
    NoNoNo
    Entry-level Setup FeeNo setup feeNo setup feeNo setup fee
    Additional Details
    More Pricing Information
    Community Pulse
    Amazon AthenaGoogle BigQuerySSIS
    Considered Multiple Products
    Amazon AWS
    Chose Amazon Athena
    - Super Cost-Effective - Well integrated with the AWS ecosystem - Easy setup with multiple formats.
    Incentivized
    Google
    Chose Google BigQuery
    Compared to every other analytics DB solution I've used, Google BigQuery was by far the easiest to set up and maintain, and scale.
    The price was also much lower for our use case (internal data analysis).
    Incentivized
    Chose Google BigQuery
    There are some areas in which this product is better while there are some in which others do better. It's not like Google BigQuery surpasses them in every metric. For a holistic view, I will say we use this because of - scalability, performance, ease of use, and seamless …
    Incentivized
    Chose Google BigQuery
    BigQuery has a simpler and more intuitive user experience (as is the case with most of its products) compared to AWS, which has a more technical and complex profile, so it was the first tool we used. It's still my go-to option for handling SQL queries, though it doesn't detract …
    Incentivized
    Chose Google BigQuery
    We based our analysis primarily on [BigQuery vs. Redshift vs. Athena] and BigQuery proved to be the best solution for us.
    Incentivized
    Microsoft
    No answer on this topic
    Key User Insights
    Would buy again
    No answers on this topic
    98%
    Would buy again
    64 Answers
    84%
    Would buy again
    21 Answers
    Delivers good value for the price
    No answers on this topic
    97%
    Delivers good value for the price
    56 Answers
    96%
    Delivers good value for the price
    24 Answers
    Happy with the feature set
    No answers on this topic
    97%
    Happy with the feature set
    63 Answers
    88%
    Happy with the feature set
    22 Answers
    Lived up to sales and marketing promises
    No answers on this topic
    100%
    Lived up to sales and marketing promises
    42 Answers
    100%
    Lived up to sales and marketing promises
    10 Answers
    Implementation went as expected
    No answers on this topic
    100%
    Implementation went as expected
    59 Answers
    95%
    Implementation went as expected
    19 Answers
    Features
    Amazon AthenaGoogle BigQuerySSIS
    Database-as-a-Service
    Comparison of Database-as-a-Service features of Amazon Athena and Google BigQuery and SQL Server Integration Services (SSIS)
    Feature
    Amazon Athena
    8.6
    4 Ratings
    2% above category average
    Google BigQuery
    8.5
    80 Ratings
    1% above category average
    SQL Server Integration Services (SSIS)
    -
    Ratings
    Automatic software patching8.22 Ratings8.017 Ratings00 Ratings
    Database scalability9.03 Ratings9.079 Ratings00 Ratings
    Automated backups7.73 Ratings8.524 Ratings00 Ratings
    Database security provisions9.22 Ratings8.873 Ratings00 Ratings
    Monitoring and metrics8.04 Ratings8.675 Ratings00 Ratings
    Automatic host deployment9.22 Ratings8.013 Ratings00 Ratings
    Data Source Connection
    Comparison of Data Source Connection features of Amazon Athena and Google BigQuery and SQL Server Integration Services (SSIS)
    Feature
    Amazon Athena
    -
    Ratings
    Google BigQuery
    -
    Ratings
    SQL Server Integration Services (SSIS)
    7.0
    56 Ratings
    18% below category average
    Connect to traditional data sources00 Ratings00 Ratings9.056 Ratings
    Connecto to Big Data and NoSQL00 Ratings00 Ratings5.043 Ratings
    Data Transformations
    Comparison of Data Transformations features of Amazon Athena and Google BigQuery and SQL Server Integration Services (SSIS)
    Feature
    Amazon Athena
    -
    Ratings
    Google BigQuery
    -
    Ratings
    SQL Server Integration Services (SSIS)
    6.8
    56 Ratings
    18% below category average
    Simple transformations00 Ratings00 Ratings8.956 Ratings
    Complex transformations00 Ratings00 Ratings4.755 Ratings
    Data Modeling
    Comparison of Data Modeling features of Amazon Athena and Google BigQuery and SQL Server Integration Services (SSIS)
    Feature
    Amazon Athena
    -
    Ratings
    Google BigQuery
    -
    Ratings
    SQL Server Integration Services (SSIS)
    7.5
    54 Ratings
    6% below category average
    Data model creation00 Ratings00 Ratings9.028 Ratings
    Metadata management00 Ratings00 Ratings6.035 Ratings
    Business rules and workflow00 Ratings00 Ratings7.145 Ratings
    Collaboration00 Ratings00 Ratings9.140 Ratings
    Testing and debugging00 Ratings00 Ratings6.451 Ratings
    Data Governance
    Comparison of Data Governance features of Amazon Athena and Google BigQuery and SQL Server Integration Services (SSIS)
    Feature
    Amazon Athena
    -
    Ratings
    Google BigQuery
    -
    Ratings
    SQL Server Integration Services (SSIS)
    5.4
    43 Ratings
    40% below category average
    Integration with data quality tools00 Ratings00 Ratings6.238 Ratings
    Integration with MDM tools00 Ratings00 Ratings4.638 Ratings
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    Amazon AthenaGoogle BigQuerySSIS
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    Score7.8 out of 10
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    Score7.8 out of 10
    Skyvia
    Score10 out of 10
    Medium-sized Companies
    Azure Database
    Score8.8 out of 10
    Azure Database
    Score8.8 out of 10
    IBM InfoSphere Information Server
    Score10 out of 10
    Enterprises
    Google Cloud SQL
    Score8.7 out of 10
    Google Cloud SQL
    Score8.7 out of 10
    SolarWinds Task Factory
    Score8.3 out of 10
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    User Ratings
    Amazon AthenaGoogle BigQuerySSIS
    Likelihood to Recommend
    10.0
    (4 ratings)
    9.0
    (79 ratings)
    8.0
    (56 ratings)
    Likelihood to Renew
    -
    (0 ratings)
    8.1
    (5 ratings)
    9.0
    (4 ratings)
    Usability
    10.0
    (1 ratings)
    6.6
    (6 ratings)
    8.0
    (11 ratings)
    Availability
    -
    (0 ratings)
    7.3
    (1 ratings)
    -
    (0 ratings)
    Performance
    -
    (0 ratings)
    6.4
    (1 ratings)
    8.8
    (6 ratings)
    Support Rating
    -
    (0 ratings)
    4.8
    (11 ratings)
    8.0
    (8 ratings)
    Implementation Rating
    -
    (0 ratings)
    -
    (0 ratings)
    10.0
    (2 ratings)
    Configurability
    -
    (0 ratings)
    6.4
    (1 ratings)
    -
    (0 ratings)
    Contract Terms and Pricing Model
    -
    (0 ratings)
    10.0
    (1 ratings)
    -
    (0 ratings)
    Ease of integration
    -
    (0 ratings)
    7.3
    (1 ratings)
    -
    (0 ratings)
    Product Scalability
    -
    (0 ratings)
    7.3
    (1 ratings)
    -
    (0 ratings)
    Professional Services
    -
    (0 ratings)
    8.2
    (2 ratings)
    -
    (0 ratings)
    User Testimonials
    Amazon AthenaGoogle BigQuerySSIS
    Likelihood to Recommend
    Amazon AWS
    If you are looking to take a lot of the traditional "database administration" work off someone's plate, going with Amazon Athena certainly has "no code" options to optimize lots of database tasks. I would say this option is less appropriate if you have other Microsoft things at play, such as Power BI.
    Incentivized
    Read full review
    Google
    Event-based data can be captured seamlessly from our data layers (and exported to Google BigQuery). When events like page-views, clicks, add-to-cart are tracked, Google BigQuery can help efficiently with running queries to observe patterns in user behaviour. That intermediate step of trying to "untangle" event data is resolved by Google BigQuery. A scenario where it could possibly be less appropriate is when analysing "granular" details (like small changes to a database happening very frequently).
    Incentivized
    Read full review
    Microsoft
    As I mentioned earlier SQL Server Integration Services is suitable if you want to manage data from different applications. It really helps in fetching the data and generating reports. Its automation make it very easy and time efficient. It works well with large database as well. But it doesn't work well with real time data, it will take some time to gather the real time data. I would not recommend using it in a real time/fast-paced environment.
    Incentivized
    Read full review
    Pros
    Amazon AWS
    • Nested Schemas like JSON data structure
    • Ability to adapt the data model to fit your queries better
    • Performance Improvement
    Incentivized
    Read full review
    Google
    • Realtime integration with Google Sheets.
    • GSheet data can be linked to a BigQuery table and the data in that sheet is ingested in realtime into BigQuery. It's a live 'sync' which means it supports insertions, deletions, and alterations. The only limitation here is the schema'; this remains static once the table is created.
    • Seamless integration with other GCP products.
    • A simple pipeline might look like this:-
    • GForms -> GSheets -> BigQuery -> Looker
    • It all links up really well and with ease.
    • One instance holds many projects.
    • Separating data into datamarts or datameshes is really easy in BigQuery, since one BigQuery instance can hold multiple projects; which are isolated collections of datasets.
    Incentivized
    Read full review
    Microsoft
    • Standard ETL use cases for daily loads
    • Loading incoming data from Vendors which is placed on FTP and adding them to the SQL Warehouse
    • Creating outgoing data files and writing them to Vendor FTPs
    • Easy Active Directory integration for seamless connections to SQL Server
    • CI/CD by hosting the code on visualstudio.com
    Incentivized
    Read full review
    Cons
    Amazon AWS
    • Response caching can be improved.
    • Data Partitioning is tricky and understanding of the same could be improved.
    Incentivized
    Read full review
    Google
    • Please expand the availability of documentation, tutorials, and community forums to provide developers with comprehensive support and guidance on using Google BigQuery effectively for their projects.
    • If possible, simplify the pricing model and provide clearer cost breakdowns to help users understand and plan for expenses when using Google BigQuery. Also, some cost reduction is welcome.
    • It still misses the process of importing data into Google BigQuery. Probably, by improving compatibility with different data formats and sources and reducing the complexity of data ingestion workflows, it can be made to work.
    Incentivized
    Read full review
    Microsoft
    • Connection managers for online data sources can be tricky to configure.
    • Performance tuning is an art form and trialing different data flow task options can be cumbersome. SSIS can do a better job of providing performance data including historical for monitoring.
    • Mapping destination using OLE DB command is difficult as destination columns are unnamed.
    • Excel or flat file connections are limited by version and type.
    Incentivized
    Read full review
    Likelihood to Renew
    Amazon AWS
    No answers on this topic
    Google
    We have to use this product as its a 3rd party supplier choice to utilise this product for their data side backend so will not be likely we will move away from this product in the future unless the 3rd party supplier decides to change data vendors.
    Incentivized
    Read full review
    Microsoft
    Some features should be revised or improved, some tools (using it with Visual Studio) of the toolbox should be less schematic and somewhat more flexible. Using for example, the CSV data import is still very old-fashioned and if the data format changes it requires a bit of manual labor to accept the new data structure
    Incentivized
    Read full review
    Usability
    Amazon AWS
    Easy to use. Scalable. Gets the job of data warehousing setup done. Using the datalake on S3 has become super convenient.
    Incentivized
    Read full review
    Google
    I think overall it is easy to use. I haven't done anything from the development side but an more of an end user of reporting tables built in Google BigQuery. I connect data visualization tools like Tableau or Power BI to the BigQuery reporting tables to analyze trends and create complex dashboards.
    Incentivized
    Read full review
    Microsoft
    SSIS is a great tool for most ETL needs. It has the 90% (or more) use cases covered and even in many of the use cases where it is not ideal SSIS can be extended via a .NET language to do the job well in a supportable way for almost any performance workload.
    Incentivized
    Read full review
    Reliability and Availability
    Amazon AWS
    No answers on this topic
    Google
    I have never had any significant issues with Google Big Query. It always seems to be up and running properly when I need it. I cannot recall any times where I received any kind of application errors or unplanned outages. If there were any they were resolved quickly by my IT team so I didn't notice them.
    Incentivized
    Read full review
    Microsoft
    No answers on this topic
    Performance
    Amazon AWS
    No answers on this topic
    Google
    I think Google Big Query's performance is in the acceptable range. Sometimes larger datasets are somewhat sluggish to load but for most of our applications it performs at a reasonable speed. We do have some reports that include a lot of complex calculations and others that run on granular store level data that so sometimes take a bit longer to load which can be frustrating.
    Incentivized
    Read full review
    Microsoft
    SQL Server Integration Services performance is dependent directly upon the resources provided to the system. In our environment, we allocated 6 nodes of 4 CPUs, 64GB each, running in parallel. Unfortunately, we had to ramp-up to such a robust environment to get the performance to where we needed it. Most of the reports are completed in a reasonable timeframe. However, in the case of slow running reports, it is often difficult if not impossible to cancel the report without killing the report instance or stopping the service.
    Incentivized
    Read full review
    Support Rating
    Amazon AWS
    No answers on this topic
    Google
    BigQuery can be difficult to support because it is so solid as a product. Many of the issues you will see are related to your own data sets, however you may see issues importing data and managing jobs. If this occurs, it can be a challenge to get to speak to the correct person who can help you.
    Incentivized
    Read full review
    Microsoft
    The support, when necessary, is excellent. But beyond that, it is very rarely necessary because the user community is so large, vibrant and knowledgable, a simple Google query or forum question can answer almost everything you want to know. You can also get prewritten script tasks with a variety of functionality that saves a lot of time.
    Incentivized
    Read full review
    Implementation Rating
    Amazon AWS
    No answers on this topic
    Google
    No answers on this topic
    Microsoft
    The implementation may be different in each case, it is important to properly analyze all the existing infrastructure to understand the kind of work needed, the type of software used and the compatibility between these, the features that you want to exploit, to understand what is possible and which ones require integration with third-party tools
    Incentivized
    Read full review
    Alternatives Considered
    Amazon AWS
    Incentivized
    Read full review
    Google
    PowerBI can connect to GA4 for example but the data processing is more complicated and it takes longer to create dashboards. Azure is great once the data import has been configured but it's not an easy task for small businesses as it is with BigQuery.
    Incentivized
    Read full review
    Microsoft
    I think SQL Server Integration Services is better suited for on-premises data movement and ADF is more suited for the cloud. Though ADF has more connectors, SQL Server Integration Services is more robust and has better functionality just because it has been around much longer
    Incentivized
    Read full review
    Contract Terms and Pricing Model
    Amazon AWS
    No answers on this topic
    Google
    None so far. Very satisfied with the transparency on contract terms and pricing model.
    Read full review
    Microsoft
    No answers on this topic
    Scalability
    Amazon AWS
    No answers on this topic
    Google
    We have continued to expand out use of Google Big Query over the years. I'd say its flexibility and scalability is actually quite good. It also integrates well with other tools like Tableau and Power BI. It has served the needs of multiple data sources across multiple departments within my company.
    Incentivized
    Read full review
    Microsoft
    No answers on this topic
    Professional Services
    Amazon AWS
    No answers on this topic
    Google
    Google Support has kindly provide individual support and consultants to assist with the integration work. In the circumstance where the consultants are not present to support with the work, Google Support Helpline will always be available to answer to the queries without having to wait for more than 3 days.
    Read full review
    Microsoft
    No answers on this topic
    Return on Investment
    Amazon AWS
    • The query speeds help us make more decisions in a day (speed).
    • If you need more horsepower for specific times in the day this option helps scale.
    • The security of your environment is well protected too.
    Incentivized
    Read full review
    Google
    • Previously, running complex queries on our on-premise data warehouse could take hours. Google BigQuery processes the same queries in minutes. We estimate it saves our team at least 25% of their time.
    • We can target our marketing campaigns very easily and understand our customer behaviour. It lets us personalize marketing campaigns and product recommendations and experience at least a 20% improvement in overall campaign performance.
    • Now, we only pay for the resources we use. Saved $1 million annually on data infrastructure and data storage costs compared to our previous solution.
    Incentivized
    Read full review
    Microsoft
    • Without this, we would have to manually update a spreadsheet of our SQL Server inventory
    • We would also have poor alerting; if an instance was down we wouldn't know until it was reported by a user
    • We only have one other person who uses SQL Server Integration Services , he's the expert. It would fall to me without him and I would not enjoy being responsible for it.
    Incentivized
    Read full review
    ScreenShots

    Google BigQuery Screenshots

    Screenshot of Migrating data warehouses to BigQuery - Features a streamlined migration path from Netezza, Oracle, Redshift, Teradata, or Snowflake to BigQuery using the fully managed BigQuery Migration Service.Screenshot of bringing any data into BigQuery - Data files can be uploaded from local sources, Google Drive, or Cloud Storage buckets, using BigQuery Data Transfer Service (DTS), Cloud Data Fusion plugins, by replicating data from relational databases with Datastream for BigQuery, or by leveraging Google's data integration partnerships.Screenshot of generative AI use cases with BigQuery and Gemini models - Data pipelines that blend structured data, unstructured data and generative AI models together can be built to create a new class of analytical applications. BigQuery integrates with Gemini 1.0 Pro using Vertex AI. The Gemini 1.0 Pro model is designed for higher input/output scale and better result quality across a wide range of tasks like text summarization and sentiment analysis. It can be accessed using simple SQL statements or BigQuery’s embedded DataFrame API from right inside the BigQuery console.Screenshot of insights derived from images, documents, and audio files, combined with structured data - Unstructured data represents a large portion of untapped enterprise data. However, it can be challenging to interpret, making it difficult to extract meaningful insights from it. Leveraging the power of BigLake, users can derive insights from images, documents, and audio files using a broad range of AI models including Vertex AI’s vision, document processing, and speech-to-text APIs, open-source TensorFlow Hub models, or custom models.Screenshot of event-driven analysis - Built-in streaming capabilities automatically ingest streaming data and make it immediately available to query. This allows users to make business decisions based on the freshest data. Or Dataflow can be used to enable simplified streaming data pipelines.Screenshot of predicting business outcomes AI/ML - Predictive analytics can be used to streamline operations, boost revenue, and mitigate risk. BigQuery ML democratizes the use of ML by empowering data analysts to build and run models using existing business intelligence tools and spreadsheets.