Likelihood to Recommend We are running it to perform preparation which takes a few hours on EC2 to be running on a spark-based EMR cluster to total the preparation inside minutes rather than a few hours. Ease of utilization and capacity to select from either Hadoop or spark. Processing time diminishes from 5-8 hours to 25-30 minutes compared with the Ec2 occurrence and more in a few cases.
Read full review Google BigQuery really shines in scenarios requiring real-time analytics on large data streams and predictive analytics with its machine learning integration. Teams have been using it extensively all over. However, it may not be the best fit for organizations dealing with small datasets because of the higher costs. And also, it might not be the best fit for highly complex data transformations, where simpler or more specialized solutions could be more appropriate.
Read full review Pros EMR does well in managing the cost as it uses the task node cores to process the data and these instances are cheaper when the data is stored on s3. It is really cost efficient. No need to maintain any libraries to connect to AWS resources. EMR is highly available, secure and easy to launch. No much hassle in launching the cluster (Simple and easy). EMR manages the big data frameworks which the developer need not worry (no need to maintain the memory and framework settings) about the framework settings. It's all setup on launch time. The bootstrapping feature is great. Read full review Its serverless architecture and underlying Dremel technology are incredibly fast even on complex datasets. I can get answers to my questions almost instantly, without waiting hours for traditional data warehouses to churn through the data. Previously, our data was scattered across various databases and spreadsheets and getting a holistic view was pretty difficult. Google BigQuery acts as a central repository and consolidates everything in one place to join data sets and find hidden patterns. Running reports on our old systems used to take forever. Google BigQuery's crazy fast query speed lets us get insights from massive datasets in seconds. Read full review Cons It would have been better if packages like HBase and Flume were available with Amazon EMR. This would make the product even more helpful in some cases. Products like Cloudera provide the options to move the whole deployment into a dedicated server and use it at our discretion. This would have been a good option if available with EMR. If EMR gave the option to be used with any choice of cloud provider, it would have helped instead of having to move the data from another cloud service to S3. Read full review It is challenging to predict costs due to BigQuery's pay-per-query pricing model. User-friendly cost estimation tools, along with improved budget alerting features, could help users better manage and predict expenses. The BigQuery interface is less intuitive. A more user-friendly interface, enhanced documentation, and built-in tutorial systems could make BigQuery more accessible to a broader audience. Read full review Likelihood to Renew 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.
Read full review Usability I give Amazon EMR this rating because while it is great at simplifying running big data frameworks, providing the Amazon EMR highlights, product details, and pricing information, and analyzing vast amounts of data, it can be run slow, freeze and glitch sometimes. So overall Amazon EMR is pretty good to use other than some basic issues.
Read full review 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
Read full review Support Rating There's a vast group of trained and certified (by AWS) professionals ready to work for anyone that needs to implement, configure or fix EMR. There's also a great amount of documentation that is accessible to anyone who's trying to learn this. And there's also always the help of AWS itself. They have people ready to help you analyze your needs and then make a recommendation.
Read full review 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.
Read full review Alternatives Considered Snowflake is a lot easier to get started with than the other options.
Snowflake 's data lake building capabilities are far more powerful. Although Amazon EMR isn't our first pick, we've had an excellent experience with EC2 and S3. Because of our current API interfaces, it made more sense for us to continue with Hadoop rather than explore other options.
Read full review I have used
Snowflake and
DataGrip for data retrieval as well as Google BigQuery and can say that all these tools compete for head to head. It is very difficult to say which is better than the other but some features provided by Google BigQuery give it an edge over the others. For example, the reliability of Google is unmatchable by others. One thing that I really like is the ability to integrate Data Studio so easily with Google BigQuery.
Read full review Contract Terms and Pricing Model None so far. Very satisfied with the transparency on contract terms and pricing model.
Read full review Professional Services 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 Return on Investment It was obviously cheaper and convenient to use as most of our data processing and pipelines are on AWS. It was fast and readily available with a click and that saved a ton of time rather than having to figure out the down time of the cluster if its on premises. It saved time on processing chunks of big data which had to be processed in short period with minimal costs. EMR solved this as the cluster setup time and processing was simple, easy, cheap and fast. It had a negative impact as it was very difficult in submitting the test jobs as it lags a UI to submit spark code snippets. Read full review Pricing has been very reasonable for us. The first 10 GB of storage is free each month and costs start at 2 cents per GB per month after that. For example, if you store 1 terabyte (TB) for a month, then the cost would be $20. Streaming data inserts start at 1 cent per 200 megabytes (MBs). The first 1 TB of queries is free, with additional analysis at $5 per TB thereafter. Meta data operations are free. Big Query helps reduce the bar for data analytics, ML and AI. BQ takes care of mundane tasks and streamlines for easy data processing, consumption. The most impressive thing is the ML and AI integration as SQL functions, so the need for moving data around is minimized. The visuals of ML models is very helpful to fine tune training, model building and prediction, etc. Read full review ScreenShots Google BigQuery Screenshots