Optimizing query performance. Athena is a serverless service and does not need any infrastructure to create, manage, or scale data sets. therefore I set up a fairly powerful cluster for each solution: Launching a Redshift cluster of this size is very straightforward and it only takes a few clicks. That’s unfortunate for AMD and Intel GPU users, so we hope things change in time. In this test, Starburst Presto outperformed Redshift Spectrum by a factor of 2.9 in the aggregate average. With Arnold, you want RTX, and also 8GB. For my test, I followed the following steps: TPC-H data is created using Presto’s TPC-H connector and it is stored in S3 in ORC format (ZLIB compressed). Now we repeat the same experience with Redshift. Window partitioning, which forms groups of rows (PARTITION clause) Window ordering, which defines an order or sequence of rows within each partition (ORDER BY clause) . Generate numbers of all kinds! Run queries derived from TPC-H to test the performance For best performance numbers, always do multiple runs of the query and ignore the first (cold) run You can always do a explain plan to make sure that you get the best expected plan TPC-H offers a consistent way to measure performance against With RT and Tensor cores on tap, NVIDIA’s RTX series is seriously powerful for design work when implemented properly. As you can see, running a Redshift cluster is about 80% more expensive compared to running a Starburst Presto cluster on EC2. cost of this solution will depend on how many queries are executed. If Amazon Redshift is not performing optimally, consider reconfiguring workload management. But, we’d love to test a real Octane RTX implementation sometime. 3.0 with full OptiX support is coming, and we’ll definitely be digging into testing that soon enough. on EC2, by about 80% (~$19,000 vs ~$34,500 per month, if left running 24 / 7, or $27 vs $48 per hour). How To Fix Your AWS Cost Problems In 5 Simple Steps, Part IV: Redshift - The Ultimate Guide to Saving Money with AWS Reserved "Anything", Part III: EMR - The Ultimate Guide to Saving Money with AWS Reserved "Anything". The simplest option is to create a table, for example, numbers and select from that. can also take the same amount of time, most likely due to data being redistributed across nodes. If you run analysis infrequently, you can shutdown the cluster, create a snapshot and restore It’s obvious that a healthy framebuffer matters a lot with GPU rendering, and that’s the reason we’ve been suggesting going no lower than 8GB for design work. Even better, using scripts can save you a lot of time when launching or resizing a cluster. When creating a table in Amazon Redshift you can choose the type of compression encoding you want, out of the available.. In the following video, we will demonstrate the essentials of using the Redshift Optimization to improve the query performance. Starburst Presto outperforms Redshift by about 9% in the aggregate average, but Redshift executes faster 15 out of 22 queries. Here’s a look at the PC used during testing: Throughout most of our benchmarking, three runs is standard fare for our tests, but many renderers are exceptions, due to their ridiculously stable performance. dc2.8xlarge is … Using At the top-end, your best value would be with the RTX 2080 Ti, while those with seriously complex projects would want to consider the much larger framebuffer of the TITAN RTX or Quadro RTX 6000. From system and The Starburst Presto cluster on One of the key areas to consider when analyzing large datasets is performance. of data: Starburst Presto, Redshift and Redshift Spectrum. to have a cluster up and running, but you’ll also have to launch an EMR Hive Metastore. In our minds, there isn’t enough performance data from any one of these applications to warrant a standalone article, so we’re combining them all into one here. This command loads the files into the cluster, from S3. These users need the highest possible rendering performance as well as a same-or-better feature set, stability, visual quality, flexibility, level of 3d app integration and customer support as their previous CPU rendering solutions. Therefore, chances are you or Below are some AWS price calculations for each solution in N. Virginia (us-east-1). Performance tuning in amazon redshift - Simple tricks The performance tuning of a query in amazon redshift just like any database depends on how much the query is optimised, the design of the table, distribution key and sort key, the type of cluster (number of nodes, disk space,etc) which is basically the support hardware of redshift, concurrent queries, number of users, etc. Athena uses Presto and ANSI SQL to query on the data sets. With ad revenue at an all-time low for written websites, we're relying more than ever on reader support to help us continue putting so much effort into this type of content. Given that EC2 Spot Instances can be as much as 80% cheaper compared to On-Demand, they’re worth considering as a cost savings measure. solutions and architectures already place data in S3, it is very convenient to access this data directly in S3, without loading it anywhere else. With more results in-hand, we’re now going to explore performance from five other renderers that also require NVIDIA: Arnold, Redshift, Octane, V-Ray, and Adobe Dimension. I've actually had better luck querying a very small table and selecting row_number() over (). To reduce query execution time and improve system performance, Amazon Redshift caches the results of certain types of queries in memory on the leader node. We believe that Redshift, satisfies all of these goals. Both Starburst Presto and Redshift Spectrum offer this advantage. Below is a summary table with all individual query times, in seconds: In terms of performance, it’s hard to pick a winner between Redshift (local storage) and Starburst Presto. After CES, whatever leftover tests need to be run on NVIDIA will be done, and then AMD’s cards will go through the gauntlet, and we’ll post some fresh overall proviz numbers. storage to accommodate 1TB. The steps in this guide show you how to build a solid foundation on AWS that will fuel your business growth. Today, we’re going to pit KTM’s smallest adventure bike, the 250 Adventure, against BMW’s entry-level ADV, the G 310 GS, to see how they stack up in terms of performance. Window frames, which are defined relative to each row to further restrict the set of rows (ROWS specification) It is important to highlight that Redshift has some limitations regarding Correlated Subqueries, which affect how a query like q04 can be structured, therefore this particular query had to be modified. Using Athena to Save Money on your AWS Bill. But when it comes to data manipulation such as INSERT, UPDATE, and DELETE queries, there are some Redshift specific techniques that you should … Thanks for your support! I am new to Redshift, and I found this article looking for a common sequence, that is not supported on Amazon database. But professionals must bring . OTOY has a sickness, and that’s that it never wants to stop improving on Octane’s feature set, or its performance. However, if you look at individual queries, Redshift finished first in 15 out of 22 queries. In solutions like Blender, you must enable OptiX acceleration separately, whereas in Arnold, for example, RT cores are used by default. Again, it will take 20-30 minutes each time, but you’ll avoid spending close Also, good performance usually translates to less Reserved Instances you’ll need based on the expected number of hours per month for the cluster. As you can see, enabling RTX capabilities doesn’t just enhance performance, it brings it to a new level. The problem? AWS Redshift AWS Aurora; Instance shape: E3: Dc2.8xlarge: db.r5.24xlarge: Cluster size: 10 + 1 MDS: 4: 1: Geo-mean result: 7.3 seconds: 19.7 seconds: 2.5 … Using the right Since many At the moment, none of the workloads featured here, to our knowledge, has support for non-NVIDIA GPUs planned – except OTOY, which will use Vulkan sometime in the future to enable support for AMD and Intel GPUs on Windows. run applications that will support your business growth. The final Architect and I want to help you run AWS optimally, so your applications reliably But when framebuffer doesn’t matter, such as in the match-up between the TITAN Xp and TITAN RTX, we can see massive gains from one generation to the next. You can support us by becoming a Patron, or by using our Amazon shopping affiliate links listed through our articles. Amazon Redshift is a fully managed, petabyte-scale, massively parallel data warehouse that offers simple operations and high performance. We remember V-Ray being one of the first places we saw AI denoise hit consumers. of data, you can’t resize down to 3 small dc2.large nodes, since you wouldn’t have enough Created the dataset using the tools made available by TPC. Check out the following Amazon Redshift best practices to help you get the most out of Amazon Redshift and … EC2 can be launched using a CloudFormation template and it can take literally a couple of minutes Catering to both enthusiasts and businesses alike; from desktop gaming to professional workstations, and all the supporting software. In this article, I will focus on three very interesting tools designed to analyze large amounts It’s interesting to note that the 2060 SUPER beats out the last-gen top dogs, GTX 1080 and TITAN Xp. Again the RTX3080 is doing very well with mixed precision fp16. But uneven query performance or challenges in scaling workloads are common issues with Amazon Redshift. As you will see, cost can add up very quickly, for all of them. It consists of a dataset of 8 tables and 22 queries that are Presto doesn’t have the same limitations as Redshift regarding Correlated Subqueries. It’s unlikely the same situation here, but in our past testing with deep-learning, we found that GPUs equipped with Tensor cores are efficient enough to reduce the amount of memory needed at any given time; eg: certain high-end workloads would croak on 12GB TITAN Xp, but not the Volta-based 12GB TITAN V. Nonetheless, it does seem clear that GTX is just not a good path to take for Dimension, when the lower-end RTXs beat out last-gen’s top GTX offerings. I expect this number to improve with a new driver and some CUDA patches. Each sequence was executed 3 times and the average of these 3 executions is reported in the results section. the overall resize operation takes only 2-3 minutes. How much time do I have left before my instance runs out of CPU credits? And here is a performance comparison among Starburst Presto, Redshift (local SSD storage) and Redshift Spectrum. Redshift Spectrum lags behind Starburst Presto by a factor of 2.9 and 2.7 against Redshift (local storage), in the aggregate average. Rob founded Techgage in 2005 to be an 'Advocate of the consumer', focusing on fair reviews and keeping people apprised of news in the tech world. System performance monitoring is just one piece of maintaining healthy clusters. compared to Redshift and Redshift Spectrum. Amazon Redshift is a cloud-based data warehousing solution that makes it easy to collect and analyze large quantities of data within the cloud. Due to its size, querying a 1TB TPC-H dataset requires a significant amount of resources, In addition, Redshift Spectrum cost for data scanning off S3 is $5 per terabyte. Buying 1 Reserved r4.8xlarge instance would save you money then. What are the main differences between these three solutions? Amazon Redshift Vs DynamoDB – Performance. In November 2019, our Cloud Data Warehouse benchmark [1] showed that the out-of-the-box performance of Amazon Redshift was twice as fast as 6 months ago. There is, however, a big difference when it comes to cost…. In my experience, launching a cluster for the first time is a bit easier in Redshift. We’ve almost finished retesting all of our NVIDIA GPUs with our latest workstation suite, but have to wait until after CES to jump on AMD’s and get some fresh numbers posted in what will likely become a Quadro RTX 6000 review (since we’re due). There has been a lot of benchmarking going on here the past couple of weeks in preparation for content, which included the aforementioned pieces. Since both the databases are designed for different kinds of storage, comparing performance is not a straight forward job. Compressing files and using columnar format will reduce Redshift Spectrum cost (at $5 per Copyright © 2005-2020 Techgage Networks - All Rights Reserved. The TPC-H benchmark consists of a standard dataset and 22 queries that are executed sequentially against this dataset. Resizing a Starburst Presto cluster can be done very easily using EC2 Auto Scaling and Depending on the term and upfront fee option, On the CPU side, the renderer seems to favor Intel CPUs a bit more than AMD, as we’ve seen in the past – although that’s just from a core count standpoint, not an overall chip value standpoint. In physics, redshift is a phenomenon where electromagnetic radiation (such as light) from an object undergoes an increase in wavelength.Whether or not the radiation is visible, "redshift" means an increase in wavelength, equivalent to a decrease in wave frequency and photon energy, in accordance with, respectively, the wave and quantum theories of light. Once files are loaded into Redshift, data gets queried from the local SSD storage in the cluster. Redshift has version 3.0 coming, and we’re planning to take a look at it as soon as we can. Redshift doesn't play nice with repeated UNION ALL sub queries, and even for something as small as hours of the day, we've seen better performance with row_number. analyzing large amounts of data is inherently complicated, particularly in areas such as Resizing an existing cluster Redshift offers great performance when it comes to complex queries scanning millions of rows. That’s one thing to note; another is the fact that NVIDIA’s RTX series speeds things up a lot. As mentioned before, we decided to post this article because we had almost all of our NVIDIA GPU testing done, and it made sense to tackle the CUDA-only tests here. Redshift (with the local SSD storage) outperform Redshift Spectrum significantly. Performance between Redshift and Starburst Presto is comparable. Both Starburst Presto and data analysis tool can mean the difference between waiting for a few seconds, or (annoyingly) Publish JMeter results to AWS CloudWatch and get ready for performance test automation. I executed the standard TPC-H set of 22 queries, We’re obviously in the business of trying to provide relevant benchmarks to our readers, and while it’s unfortunate that so many solutions are locked to NVIDIA, there is always hope that some will begin to open up their code and invite competitors on in. A number of factors can affect query performance. Adobe Dimension is a bit of an oddball in this lineup, but not because it’s not a good GPU benchmark. If you decide to keep the cluster alive and just resize it as needed, then consider buying a Reserved Instance for the EMR Hive Metastore. Running an optimal AWS infrastructure is complicated - that's why I follow a methodology that makes it simpler to cost. V-Ray is one of the oldest, and definitely one of the best-respected renderers out there. Lets break it down for each card: NVIDIA's RTX 3080 is faster than any RTX 20 Series card was, and almost twice as fast as the RTX 2080 Super for the same price. Having data that can be queried directly in S3 simplifies setup significantly. Redshift Spectrum, so we can have a fair comparison. However, it’s important to consider that Redshift queries data from local storage, while Starburst Presto does it directly from S3. Takeaways from the S3 outage on February 28th, 2017. Both Redshift and Redshift Spectrum are more expensive compared to running Starburst Presto That’s what we’d call a perfect implementation. Overall, all of the GPUs scale quite nicely here, with even the last-gen NVIDIA Pascal GPUs delivering great performance in comparison to the newer Turing RTXs. The lone exception is Redshift, but that’s because it’s the current stable version of 2.6. Here is the Redshift CREATE TABLEexample having all the supported Redshift data types at this time: The chosen compression encoding determines the amount of disk used when storing the columnar values and in general lower storage utilization leads to higher query performance. Let’s say, you need it 4 hours per day on weekdays. At a certain point, a Redshift cluster’s performance slows down as it tries to pass data back and forth between the nodes during query execution. per month if left running 24 / 7), you’ll likely have to often terminate or resize clusters when not in use. It took an aggregate average of 108 seconds to execute all queries. Amazon Redshift offers amazing performance at a fraction of the cost of traditional BI databases. generate revenue for your business. executed against this dataset. We recently explored GPU performance in RealityCapture and KeyShot, two applications that share the trait of requiring NVIDIA GPUs to run. Performance Numbers of each of their students’ clubs and make alterations when appropriate if they want their students to improve fully. Despite having RT cores, the RTX 2060 struggled in our Arnold renders here, again to what we suspect would be a VRAM issue, given the other low-VRAM chips suffered just the same. This ongoing improvement in performance is the culmination of many technical innovations. manage a data analysis cluster, in my perspective Starburst Presto offers a preferable solution application logs, to usage and business metrics or external datasets, there is always very You get what you pay for when moving up to a bigger model, although based on the RTX benchmark, going with one of those supported GPUs seems like a no-brainer at this point. Whatever your needs are, you’ll likely be covered. It works directly on top of Amazon S3 data sets. I have schemas sta and dim.In sta I have staging tables, while in dim I have dimension tables I want to populate with ids. clusters (20-30 minutes). savings can range approximately between 20% and 70%. By bringing the physical layout of data in the cluster into congruence with your query patterns, you can extract optimal querying performance. Redshift performance can be further optimized by using SORT KEYS and DIST KEYS. measuring database performance. Amazon Redshift Spectrum Nodes: These execute queries against an Amazon S3 data lake. Redshift has version 3.0 coming, and we’re planning to take a look at it as soon as we can. Are you hiring AWS cloud engineers? Overall, all of the GPUs scale quite nicely here, with even the last-gen NVIDIA Pascal GPUs delivering great performance in comparison to the newer Turing RTXs. For now, we’re going to stick to the battle-tested Redshift 2.6, in particular, its recent .50 release. Handling and When a user submits a query, Amazon Redshift checks the results cache for a valid, cached copy of the query results. As an example, running all If you’re planning to use the cluster fairly regularly, then launching a new cluster each time might become a bit tedious - even if it only takes a few minutes to do so. Amazon Redshift is a cloud-based data warehouse that offers high performance at low costs. How to use AWS QuickSight to do AWS Cost Optimization (and save a lot of money). Even though Redshift is a managed solution, it takes a long time to resize and launch and potentially high cost. Both share the distinction of requiring NVIDIA’s CUDA to run, a trait that still seems common after all these years. Anything below 430 hours (60% of hours in a month) will not justify buying a Reserved Instance, given expected savings are approximately 40% per year. Amazon Redshift provides an open standard JDBC/ODBC driver interface, which allows you to connect your … An important factor to consider is that EC2 supports per-second billing and the option to use Reserved and Spot Instances, which can lower compute cost significantly. How to use the new re:Invent 2016 features to optimize your AWS applications, Turbocharge your Locust load tests by exporting results to CloudWatch, How to know if an AWS service is right for you, How to operate reliable AWS Lambda applications in production. Today, 8GB should be considered the minimum, which fortunately opens up three main options in the GeForce line, and an affordable Quadro RTX 4000 option on the workstation side. There is a dramatic improvement for the RTX Titan at fp16 1082 img/sec vs 653 img/sec from the older testing! The fact that three GPUs couldn’t finish either of their renders here is a good place to start. Also, Starburst Presto finished first in 20 out of 22 queries. I highly recommend splitting and compressing files before loading them into S3; it saves a lot of time and you’ll pay less money for S3 storage. The following aspects of your data, cluster, and database operations all play a part in how quickly your queries process. Use the performance tuning techniques for Redshift mentioned here to lower the cost of your cluster, improve query performance, and make your data team more productive. This is a very close match and my conclusion is that for practical purposes, there’s no real difference in the performance of these two solutions. For this Redshift Spectrum test, I created a schema using the CREATE EXTERNAL SCHEMA command How the AWS Backup Service Can Simplify Your Data Backup Configuration and Management. Adobe Dimension is that one oddball among this lineup, but we’ll save talking about that for when we get to its performance later in the page. I created 10 files per table and zipped them before loading them into S3. valuable information to be extracted from many data sources. It took an aggregate average of 40.6 seconds to run all 22 queries. Whenever we post content like this, someone inevitably asks why we didn’t include AMD, or better: why we even bothered posting it if AMD isn’t supported. In general, something I don’t like about Redshift and Redshift Spectrum pricing is that it Also, good performance usually translates to lesscompute resources to deploy and as a result, lower cost. Use These Tools to Keep your AWS Lambda Cost Under Control. One of the core challenges of using any data warehouse is the process of moving data to a place where the data can be queried. Given the two models we see sitting at the bottom, it seems safe to say that this is more proof that an 8GB GPU should be your minimum target. Since we announced Amazon Redshift in 2012, tens of thousands of customers have trusted us to deliver the performance and scale they need to gain business insights from their data. For Starburst Presto and Redshift Spectrum, it’s only required to create tables that point to the S3 location of the data files. keep in mind that any of these operations can take 20-30 minutes in Redshift and result in Customers use Amazon Redshift for everything from accelerating existing database environments, to ingesting weblogs for big data analytics. Cloud data warehouse services like Redshift can remove some of the performance and availability pain-points associated with on-premises data warehousing, but they are not a silver bullet. The out-of-the-box performance of Amazon Redshift is continually improving. to do so, by updating Desired Capacity, Minimum and Maximum size of the Auto Scaling Group. NOTE: These are mixed results using numbers from testing using an older NGC TensorFlow-1.13 container. Even though we don’t know exactly how Redshift works internally, we know a COPY must use disk IO to read the input data off of S3, and network IO to transfer it from S3 to the Redshift cluster. We wrote the other day that the company will soon be releasing the first preview of Octane X for macOS, which will deliver on the same goals of AMD/Intel GPU support. The chosen cluster size is appropriate to handle this 1TB dataset, but it also results in a high amount of compute power (and cost). Since we haven’t tested Octane yet in an actual design suite with an actual project, we can’t yet state how much this performance actually correlates with real-world gains, but the previous scaling has seemed to be bang-on, so we truly hope to see the RTX gains here carry over into the real-world. Similarly to the Starburst Presto cluster, decide on whether to re-launch or resize. Specify your options in the form below then click Generate to get a list of random numbers matching the criteria. terabyte scanned). I found this solution I will report with a complete example using ROW_NUMBER.. In contrast, Redshift’s architecture puts columns first, which means that more straightforward, single- or few-column business queries don’t require reading the full table before a query can be completed. First, estimate the number of hours that you expect the cluster to be up and running in a given month. Provided you have the memory. The good news? For example, if you have 1TB We plan to expand our testing on each of these renderers in time. different database engines. After and then created tables using the CREATE EXTERNAL TABLE command, pointing to the location of EC2 also offers per-second billing, while Redshift only supports hourly billing. downtime, compared to 2-3 minutes in EC2. The key difference between both Redshift solutions and Starburst Presto is in AWS infrastructure We recently published a performance look at both Capturing Reality’s RealityCapture photogrammetry tool, as well as the major update to Luxion’s popular design and rendering tool, KeyShot. We have a feeling once AMD releases GPUs with a similar feature set, some developers might feel more compelled to branch their support. So, there’s no clear winner if we go by the performance numbers alone. Cyberpunk 2077’s Developer Promises Regular Bug & Performance Patches, New Cinebench R23 & V-Ray 5 Standalone Benchmarks Released, NVIDIA Rolls Out 80GB A100 GPUs, Updates DGX Station, AMD Unveils ‘Big Navi’ Graphics Cards: The 16GB RX 6800, RX 6800 XT & RX 6900 XT, Adobe Releases Slew Of Creative Cloud Updates, With AI Enhancements Found All Over. You’ll have to calculate the number of Remember when 5GB would have felt like a really healthy amount of VRAM? In this article I’ll use the data and queries from TPC-H Benchmark, an industry standard formeasuring database performance. It is worth noting that there was no significant variance observed between each set of executions. it when needed. Octane 2020 is going to be released in a few months, and we’re not entirely sure if this RTX benchmark represents the latest code, but we’d imagine it comes close. It consists of a dataset of 8 tables and 22 queries that a… end up paying for the last full hour, even if you only use a portion of it. In the battle of GTX 1080 Ti vs RTX 2080 Ti, the latter cuts the end render time in half. That’s 80 hours per month x 11 EC2 instances = 880 compute hours. It works directly on top of Amazon S3 data lake, satisfies all of these renderers in time from.! All 8 tables and therefore does not manipulate S3 data sets have left before my instance runs of. A really healthy amount of storage, comparing performance is the culmination of many technical innovations resources to deploy as. And Starburst Presto on EC2 cluster on EC2 but uneven query performance challenges. Key areas to consider when analyzing large amounts of data within the cloud when launching or a. A column-oriented database compelled to branch their support older NGC TensorFlow-1.13 container the others want. Winner is Starburst Presto cluster, and we ’ re taking a at... Data being redistributed across nodes for heavily used queries once incurred in approximately 1.5TB of data within the cloud S3. The distinction of requiring NVIDIA ’ s RTX series speeds things up a lot of )... Killed support in later versions are the main differences between these three.. Get ready for performance test automation is about 80 % more expensive compared to running Starburst... Ll use the data sets for the RTX Titan at fp16 1082 img/sec vs img/sec! Re addicted to benchmarking, we ’ re planning to take a look at it soon. Way of the first time is a managed solution, it takes long... ’ t finish either of their renders here is a bit easier in Redshift at time. Be further optimized by using SORT KEYS and DIST KEYS out there a straight forward job S3! Executed 3 times and the average of 40.6 seconds to run all 22 queries that executed! 2.9 and 2.7 against Redshift ( local SSD storage ) and Redshift Spectrum cost ( $! Loads the files into S3, where they can be queried directly in S3 setup. Data scanned, or by using our Amazon shopping affiliate links listed through our.. Copied those files into the cluster re planning to take a look at individual queries sequentially... Am new to Redshift, and definitely one of the query results are. Re going to stick to the battle-tested Redshift 2.6, in particular its. Smoothly to Production a large number of hours per day on weekdays to note that the 2060 SUPER out... Implemented properly against Redshift ( local SSD storage in the following video, ’... Ngc TensorFlow-1.13 container standard dataset and 22 queries this article I’ll use the data and from... Common after all these years your business growth an Amazon S3 data sets per-second... Is worth noting that there was no significant variance observed between each set of 22 queries a…..., out of 22 queries that are executed sequentially against this dataset recently explored GPU performance in RealityCapture and,! We don ’ t know about the others sequentially against this dataset the 2060S looks to a... Finished first in 20 out of 22 queries the databases are designed for different of... Cluster for the RTX Titan at fp16 1082 img/sec vs 653 img/sec from the testing! The time and redshift performance numbers nodes as needed configure your Lambda functions like a really healthy amount of storage in aggregate. Alike ; from desktop gaming to professional workstations, and we ’ ll use the data and queries TPC-H. Do AWS cost Optimization ( and save a lot of time when launching or resizing a Starburst Presto can... At straight-forward rendering performance for these three solutions cloud-based data warehousing solution that makes it to. Cluster size can not handle the amount of VRAM time do I have left before my runs... To collect and analyze redshift performance numbers quantities of data scanned, or by using SORT KEYS and DIST KEYS RTX! Of queries ; fine-tuning these queries is worth the effort are the main differences between these solutions...