Even small companies today have an average of 47.81 terabytes of data to manage. Whether you’re a small company or a trillion-dollar giant, data makes the decision. But as data ecosystems become more complex, it’s important to have the right tools for the job.
One modern data management tool that can help manage data of really any size from a wide variety of data sources is Presto. Presto has been the backbone of many big tech companies like Netflix and Lyft. But Presto isn’t limited to helping big tech companies or even big companies in general. New technologies have made Presto popular with companies of all sizes recently.
In this article, we’ll discuss what Presto is, why companies use it, and how your company can implement it with companies like Starburst Data so you can take full advantage of it for your business.
What is Presto?
Presto is an open-source distributed SQL query engine running interactive analytical queries on data sources of all sizes, from gigabytes to petabytes. Unlike many other SQL engines, which were often written for particular databases, Presto can sit on top of many databases. Specifically, Presto allows you to query data where it lives, including Hive, Cassandra, relational databases, or even proprietary data stores. Presto helps us to join data in data stores and databases. This means you don’t need to centralize all your data to perform ad hoc queries. This begins to bring us to the next point.
Why use Presto?
Presto is very platform agnostic. Whether in the cloud or on-premise, this technology is versatile and used by big companies like Netflix and Lyft, but it’s also popular with small companies and startups. These companies chose to use Presto because it can easily create a self-service BI layer accessible to more than just data engineers. The fact that data can be queried across data sources allows Presto to provide more agile access to data by data consumers. But this is only one of the many advantages Presto offers users. Managing and querying terabytes of data in Big Data is normal. This leads to slower queries. That’s a problem. Analysts want their queries to be faster.
Presto provides fast queries by leveraging known and new techniques for distributed query processing. These techniques include in-memory parallel processing, chained execution across nodes in a cluster, a multi-threaded execution model that keeps all CPU cores busy, and efficient flat memory data structures.
Presto Limitations
For everything great on Presto. Presto was developed as a simple SQL Engine. This means you will have to manage scaling, security, monitoring, and also create new connections yourself. This means that your teams won’t be able to easily lock down data if only he is the one to manage it.
This can often make Presto unaffordable because, with all its benefits, it is difficult to manage. This is where companies like Starburst Data stepped in and built the infrastructure around Presto.
What are Starburst dates, and how can they help?
Starburst Data makes Presto implementation easy. Starburst Data provides all the benefits of Presto, such as reducing the time it takes for analysts to access data in almost any data source. In addition, Starburst Data helped develop several security features, such as Global Security for fine-grained access control, data encryption, data masking, and query auditing.
Plus, Starburst Data makes it easy to manage your Presto scaling calculation. If you tried to manage Presto yourself, you would have to develop your software to manage how Presto scaled with increasing queries. Developing any of these features in-house is expensive and requires an entire extra team. So now we know what, why, and how. But let’s answer a few places.
Source: ahana.io
Use Cases
Below we’ll discuss a few use cases where companies are already using Presto and some general examples of how your team can use Presto.
Netflix’s Big Data Platform
Netflix is one of the many companies that have leveraged Presto as their big data platform. Netflix has use cases ranging from analyzing A/B test results to analyzing user streaming to training data models for their recommendation algorithms. Using standard data warehouse tools wasn’t enough for Netflix.
That’s why Netflix turned to Presto. Presto has helped solve ad hoc interactive use cases for companies. They needed a data storage system that could manage all their different use cases at scale. Presto was a storage system for Netflix. But Netflix isn’t the only company using Presto for the computing part of its data warehouse.
Data Lake and SQL Engine in Lyft
In early 2017, Lyft started exploring Presto use cases for OLAP, and we realized the potential of this amazing query tool. Now thousands of dashboards inside Lyft use Presto and its ability to manage large users. Before Presto, Lyft relied on AWS-Redshift and had interconnected data storage and computing.
Multiple compute and storage caused many performance issues. For example, polling would be very slow if the system required maintenance, upgrades, downtime, or node scaling. They needed a system where data and computation were separated, so Presto fit our use case beautifully.
Thanks to these improvements, Lyft’s backend now manages approximately 1.5 thousand weekly active users who run several million daily queries on its platform.
Self-service BI
At the end of the day, getting access to the right data remains the bottleneck for data analytics, machine learning, and data science. Data engineers and data architects must constantly work to integrate, migrate, and design new data warehouse tables. With more data coming in from all directions and increased demand for data-driven decisions, Presto can help increase the pressure on data engineers and data storage systems. Presto enables analysts to combine data from different data sources.
This includes systems like Hadoop, S3, and Cassandra with other resources like a traditional relational database.With Presto, you can finally stop moving data just to query it! Starburst allows you to enjoy the performance benefits of Presto and the benefits of using a Presto instance built for Enterprise use.
Data Lakes
We discussed data lakes in the Lyft example. But we wanted to discuss it separately. The great thing about Presto is that it’s not limited to querying structured data. Presto offers a decent amount of field and map functionality.
This means teams can work with less structured data and still use SQL to analyze the information. In addition, Presto, especially in partnership with Starburst Data, can access data from almost any data storage system, whether it’s Hadoop or S3. The ability to query data where it is makes Presto a good computing layer for data lakes.
Conclusion
Presto provides many benefits for companies of all sizes. In particular, the ability to query data where it is reduces the amount of time data engineers need to spend developing complex ETLs. This means your teams can answer questions their business owners have more quickly. This is a huge advantage and coupled with Starburst Data; it is easy to use Presto relativity.
Instead of needing a large team to manage your Presto clusters, you can easily have 1-2 engineers manage and grow your data infrastructure. And if your team needs help implementing Presto and Starburst Data, contact us today.
Our data science and engineering consulting team can help you build everything from big data platforms to machine learning models.
Presto is very platform agnostic. Whether in the cloud or on-premise, this technology is versatile and used by big companies like Netflix and Lyft, but it’s also popular with small companies and startups.
Starburst Data makes Presto implementation easy. Starburst Data provides all the benefits of Presto, such as reducing the time it takes for analysts to access data in almost any data source. In addition, Starburst Data helped develop several security features, such as Global Security.
The media shown in this article is not owned by Analytics Vidhya and is used at the Author’s discretion.
We use cookies essential for this site to function well. Please click to help us improve its usefulness with additional cookies. Learn about our use of cookies in our Privacy Policy & Cookies Policy.
Show details
Powered By
Cookies
This site uses cookies to ensure that you get the best experience possible. To learn more about how we use cookies, please refer to our Privacy Policy & Cookies Policy.
brahmaid
It is needed for personalizing the website.
csrftoken
This cookie is used to prevent Cross-site request forgery (often abbreviated as CSRF) attacks of the website
Identityid
Preserves the login/logout state of users across the whole site.
sessionid
Preserves users' states across page requests.
g_state
Google One-Tap login adds this g_state cookie to set the user status on how they interact with the One-Tap modal.
MUID
Used by Microsoft Clarity, to store and track visits across websites.
_clck
Used by Microsoft Clarity, Persists the Clarity User ID and preferences, unique to that site, on the browser. This ensures that behavior in subsequent visits to the same site will be attributed to the same user ID.
_clsk
Used by Microsoft Clarity, Connects multiple page views by a user into a single Clarity session recording.
SRM_I
Collects user data is specifically adapted to the user or device. The user can also be followed outside of the loaded website, creating a picture of the visitor's behavior.
SM
Use to measure the use of the website for internal analytics
CLID
The cookie is set by embedded Microsoft Clarity scripts. The purpose of this cookie is for heatmap and session recording.
SRM_B
Collected user data is specifically adapted to the user or device. The user can also be followed outside of the loaded website, creating a picture of the visitor's behavior.
_gid
This cookie is installed by Google Analytics. The cookie is used to store information of how visitors use a website and helps in creating an analytics report of how the website is doing. The data collected includes the number of visitors, the source where they have come from, and the pages visited in an anonymous form.
_ga_#
Used by Google Analytics, to store and count pageviews.
_gat_#
Used by Google Analytics to collect data on the number of times a user has visited the website as well as dates for the first and most recent visit.
collect
Used to send data to Google Analytics about the visitor's device and behavior. Tracks the visitor across devices and marketing channels.
AEC
cookies ensure that requests within a browsing session are made by the user, and not by other sites.
G_ENABLED_IDPS
use the cookie when customers want to make a referral from their gmail contacts; it helps auth the gmail account.
test_cookie
This cookie is set by DoubleClick (which is owned by Google) to determine if the website visitor's browser supports cookies.
_we_us
this is used to send push notification using webengage.
WebKlipperAuth
used by webenage to track auth of webenagage.
ln_or
Linkedin sets this cookie to registers statistical data on users' behavior on the website for internal analytics.
JSESSIONID
Use to maintain an anonymous user session by the server.
li_rm
Used as part of the LinkedIn Remember Me feature and is set when a user clicks Remember Me on the device to make it easier for him or her to sign in to that device.
AnalyticsSyncHistory
Used to store information about the time a sync with the lms_analytics cookie took place for users in the Designated Countries.
lms_analytics
Used to store information about the time a sync with the AnalyticsSyncHistory cookie took place for users in the Designated Countries.
liap
Cookie used for Sign-in with Linkedin and/or to allow for the Linkedin follow feature.
visit
allow for the Linkedin follow feature.
li_at
often used to identify you, including your name, interests, and previous activity.
s_plt
Tracks the time that the previous page took to load
lang
Used to remember a user's language setting to ensure LinkedIn.com displays in the language selected by the user in their settings
s_tp
Tracks percent of page viewed
AMCV_14215E3D5995C57C0A495C55%40AdobeOrg
Indicates the start of a session for Adobe Experience Cloud
s_pltp
Provides page name value (URL) for use by Adobe Analytics
s_tslv
Used to retain and fetch time since last visit in Adobe Analytics
li_theme
Remembers a user's display preference/theme setting
li_theme_set
Remembers which users have updated their display / theme preferences
We do not use cookies of this type.
_gcl_au
Used by Google Adsense, to store and track conversions.
SID
Save certain preferences, for example the number of search results per page or activation of the SafeSearch Filter. Adjusts the ads that appear in Google Search.
SAPISID
Save certain preferences, for example the number of search results per page or activation of the SafeSearch Filter. Adjusts the ads that appear in Google Search.
__Secure-#
Save certain preferences, for example the number of search results per page or activation of the SafeSearch Filter. Adjusts the ads that appear in Google Search.
APISID
Save certain preferences, for example the number of search results per page or activation of the SafeSearch Filter. Adjusts the ads that appear in Google Search.
SSID
Save certain preferences, for example the number of search results per page or activation of the SafeSearch Filter. Adjusts the ads that appear in Google Search.
HSID
Save certain preferences, for example the number of search results per page or activation of the SafeSearch Filter. Adjusts the ads that appear in Google Search.
DV
These cookies are used for the purpose of targeted advertising.
NID
These cookies are used for the purpose of targeted advertising.
1P_JAR
These cookies are used to gather website statistics, and track conversion rates.
OTZ
Aggregate analysis of website visitors
_fbp
This cookie is set by Facebook to deliver advertisements when they are on Facebook or a digital platform powered by Facebook advertising after visiting this website.
fr
Contains a unique browser and user ID, used for targeted advertising.
bscookie
Used by LinkedIn to track the use of embedded services.
lidc
Used by LinkedIn for tracking the use of embedded services.
bcookie
Used by LinkedIn to track the use of embedded services.
aam_uuid
Use these cookies to assign a unique ID when users visit a website.
UserMatchHistory
These cookies are set by LinkedIn for advertising purposes, including: tracking visitors so that more relevant ads can be presented, allowing users to use the 'Apply with LinkedIn' or the 'Sign-in with LinkedIn' functions, collecting information about how visitors use the site, etc.
li_sugr
Used to make a probabilistic match of a user's identity outside the Designated Countries
MR
Used to collect information for analytics purposes.
ANONCHK
Used to store session ID for a users session to ensure that clicks from adverts on the Bing search engine are verified for reporting purposes and for personalisation
We do not use cookies of this type.
Cookie declaration last updated on 24/03/2023 by Analytics Vidhya.
Cookies are small text files that can be used by websites to make a user's experience more efficient. The law states that we can store cookies on your device if they are strictly necessary for the operation of this site. For all other types of cookies, we need your permission. This site uses different types of cookies. Some cookies are placed by third-party services that appear on our pages. Learn more about who we are, how you can contact us, and how we process personal data in our Privacy Policy.