Coginiti vs. Azure Data Lake Analytics

Overview
ProductRatingMost Used ByProduct SummaryStarting Price
Coginiti
Score 9.3 out of 10
N/A
Coginiti (formerly Aginity), the collaborative intelligence company, empowers users to get consistent answers to any business question. The Collaborative Intelligence platform provides a unique workspace that empowers the entire organization to build, share and reuse analytics. By making quality data widely available and focusing on outcomes over pre-defined output, everyone is freed up to explore and experiment to answer business questions. By creating and sharing both building blocks and…N/A
Azure Data Lake Analytics
Score 8.3 out of 10
N/A
Microsoft's Azure Data Lake Analytics is a BI service for processing big data jobs without consideration for infrastructure.N/A
Pricing
CoginitiAzure Data Lake Analytics
Editions & Modules
No answers on this topic
No answers on this topic
Offerings
Pricing Offerings
CoginitiAzure Data Lake Analytics
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
CoginitiAzure Data Lake Analytics
Considered Both Products
Coginiti
Chose Coginiti
Aginity learning curve was smooth and we didn't need to hire a specialist to teach us the system. it also offers free trials and a free version which enabled us to use and test the system's compatibility with our organisation's needs before fully investing in one. moreover, …
Chose Coginiti
Aginity offers the best tools for data research, which are easy to use and customize. Unlike Aginity, AdvancedMiner wasn't only hard to deploy but also was highly-priced.
Chose Coginiti
Aginity is simpler than Toad to be used. The negative part is that it is not as flexible as Dbeaver as you need to have more [agility] interfaces to access different DBs, while with Dbeaver you can you the same interface. Still, sometimes Aginity is more precise when retrieving …
Chose Coginiti
I would say that what I most likely from Aginity is the simplicity and how fast you can perform small actions. Maybe that's because there are not that many features but I can say that for what we need, all the features are present and working properly. The price is also ok …
Chose Coginiti
Like all of the other various SQL querying tools that I have used, the Aginity Workbench for RedShift has a free community version. Although unlike many of the other tools that I have used, Aginity feels like a tool that I would be willing to pay for. It is feature rich and …
Chose Coginiti
To be honest, our client had already selected Aginity to be the layer over Netezza. However, I have spent a lot of time in Teradata SQL Developer and Microsoft SQL Server Management Studio. If I had to rank them in order, I would put Teradata SQL Developer first, followed by …
Chose Coginiti
I use Aginity extensively for quick data research. Examples include figuring out what the table structure look like, how the data looks like, quick 100 record sample selection, creating a DDL with a list of the column names for further manipulation and analysis. However, for …
Azure Data Lake Analytics
Chose Azure Data Lake Analytics
Azure Data Lake simplifies extensive data analysis. It runs Hadoop, HDInsight, and Data Lakes, and even complex queries run smoothly and quickly. We write queries to transform data and extract insights instead of configuring hardware. It can handle any size job by adjusting the …
Chose Azure Data Lake Analytics
Compared to Databricks which we have fully implemented and all teams use, Azure Data Lake Analytics was first pushed on our engineering team from the Data Science group pretty much from familiarity. Once we did a proof of technology, we found it to natively have the better …
Chose Azure Data Lake Analytics
We did some research about Alibaba Cloud Data Lake Analytics and even being cheaper than Azure Data Lake Analytics, we decided to go for the second one once we noticed they have more features and better documentation. Another thing we considered during this process was the fact …
Chose Azure Data Lake Analytics
ADL Analytics supports big data such as Hadoop, HDInsight, Data lakes. Usually, a traditional data warehouse stores data from various data sources, transform data into a single format and analyze for decision making. Developers use complex queries that might take longer hours …
Chose Azure Data Lake Analytics
Both of the products selected are very good at what they do, but data lake analytics is able to bundle everything else within our preexisting data lake, which is a very big [deciding] factor.
User Ratings
CoginitiAzure Data Lake Analytics
Likelihood to Recommend
9.3
(0 ratings)
8.7
(0 ratings)
User Testimonials
CoginitiAzure Data Lake Analytics
Likelihood to Recommend
Aginity is well suited for writing ad-hoc queries and data analysis, having all of the features that data analysts come to expect in a database querying tool. Aginity is also great for basic operation and management allowing an engineer or DBA to store multiple databases and credentials, see how tables are distributed or sorted, and see table sizes and row counts. However, for more advanced database management another tool or the RedShift console is still required
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For us we have an enterprise of SQL users at all skill levels, and this product is very SQL friendly and extremely fast in creation of data aggregates and analysis. If you are an Azure storage user, considering using Lake Analytics over top of your blob or any other storage just adds complementary services and functions native to your existing architecture.
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Pros
  • Explore, model and analyze data across all company platforms.
  • With Aginity, we improve team engagements where we can access and re-use colleagues' SQL.
  • Make faster data-driven decisions with BI capabilities.
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  • It combines big data.
  • Monitors and alerts are helpful.
  • Report visualization relies on analytics.
  • It is compatible with Power BI services for report generation.
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Cons
  • with Hortonworks, you have to modify manually some settings in order to correctly see the metadata (HCAT properties)
  • with reclaim, the generate only express statistics and "limit 100" on the select is a little risky if you do not pay attention.
  • Saved views cannot be retrieved with the same [syntax] you wrote them, but they become really hard to read
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  • There's a bit of bias towards cloud with ADL Analytics. Depending upon a company's infra strategy and investment plans, there are some challenges with migration and integeration.
  • Not worth the time/effort/money if the organization doesn't have "Volume" of data. Cost effective only when daily loads exceed around 1million.
  • While training materials are available online, Adoption rate - Yet to pick up.
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Alternatives Considered
Aginity learning curve was smooth and we didn't need to hire a specialist to teach us the system. it also offers free trials and a free version which enabled us to use and test the system's compatibility with our organisation's needs before fully investing in one. moreover, its ability to drag and drop tabs has made our work easier.
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Azure Data Lake simplifies extensive data analysis. It runs Hadoop, HDInsight, and Data Lakes, and even complex queries run smoothly and quickly. We write queries to transform data and extract insights instead of configuring hardware. It can handle any size job by adjusting the power. Azure's servers, networking, and data entry are fantastic. It provides security and assured data access.
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Return on Investment
  • I think Aginity really allows us to quickly dive into the data, by providing easy and quick access to our database. This allowed us to solve our business problems on an efficient and timely manner. But since it is not a critical component of our department, (a lot of people also use Toad for the same thing, since Netezza is retiring soon, and Aginity, which belongs to IBM, has had legal issues, and is no longer available to new hires of our department. It is sort of our legacy software.)
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  • Since we have implemented this solution, we have been more able to follow what is going on in our process and sells.
  • We are also sparing some money by comparing the costs now against the costs we had on-premise.
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ScreenShots