Amazon Quantum Ledger Database (QLDB) vs. Google BigQuery

One of the most important aspects while working with data warehousing solutions and analytics is the ability to handle large datasets. Google BigQuery is the best in business for that particular aspect. It is ridiculously fast while handling large data sets. Another aspect where it is well suited is the ability to integrate it with data visualization tools like Data Studio. It is fast, easy to use, and very reliable. The only aspect where I feel it is less appropriate where you have to pay more of inefficient scripts and that can hamper the growth of the company a bit.

Incentivized

• BigQuery is ridiculously fast and has the ability to query absurdly large data sets to return results immediately.
• BigQuery allows for storage of a massive amount of data for relatively low prices.
• Easy to learn. BiqQuery uses SQL-like queries and is easy to transfer your existing skills to use.

Incentivized

• One issue with Google Cloud Storage is its price. For one to have that premium Google Cloud Storage, for the purpose of massive storage, he/she must have adequate cash. Otherwise, Google Cloud Storage is a safe and perfect online storage platform.
• The only thing that can come to mind that would be annoying with this software was that sometimes when trying to share files on the Cloud with coworkers, it would just not share at all, or there would be a massive delay in when I shared them and when they received them. Other than that though, everything is perfect with this.

Incentivized

web UI is easy and convenient. Many RDBMS clients such as aqua data studio, Dbeaver data grid, and others connect. Range of well-documented APIs available. The range of features keeps expanding, increasing similar features to traditional RDBMS such as Oracle and DB2

It’s Google, they’re big and well organized, the documentation is abundant and the scalability is amazing. The UX is good too, considering it’s a professional tool expected to be used by people with a specific technical background. Overall, it makes me feels good and secure that we know where to store the data, how to use that data and that the data is handled with utmost security and performance practices.

Incentivized

Spinning up, provisioning, maintaining and debugging a Hadoop solution can be non-trivial, painful. I'm talking about both GCE based or HDInsight clusters. It requires expertise (+ employee hire, costs). With BigQuery if someone has a good SQL knowledge (and maybe a little programming), can already start to test and develop. All of the infrastructure and platform services are taken care of. Google BigQuery is a magnitudes simpler to use than Hadoop, but you have to evaluate the costs. BigQuery billing is dependent on your data size and how much data your query touches.

Incentivized

None so far. Very satisfied with the transparency on contract terms and pricing model.

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.

• Google BigQuery has had enormous impact in terms of ROI to our business, as it has allowed us to ease our dependence on our physical servers, which we pay for monthly from another hosting service. We have been able to run multiple enterprise scale data processing applications with almost no investment
• Since our business is highly client focused, Google Cloud Platform, and BigQuery specifically, has allowed us to get very granular in how our usage should be attributed to different projects, clients, and teams.
• Plain and simple, I believe the meager investments that we have made in Google BigQuery have paid themselves back hundreds of times over.

Incentivized