Etleap customers will benefit from new technology in Etleap for faster query performance
SAN FRANCISCO, Calif. – December 2, 2019 — Today, Etleap, an Advanced Technology Partner in the Amazon Web Services (AWS) Partner Network (APN) and provider of fully-managed Extract, Load, Transform (ETL)-as-a-service, announced support for Amazon Redshift Materialized Views. The new feature is designed to help customers achieve up to 100x faster query performance on analytical workloads such as dashboarding queries from Business Intelligence (BI) tools and ELT data processing. Because Etleap was built from the ground up to handle data integration for Amazon Redshift users, including orchestration of transformations within Amazon Redshift, the company is uniquely positioned to test this new capability and provide support for it in their product.
“We are delighted to have Etleap help launch the Materialized Views feature in Amazon Redshift,” said Andi Gutmans, Vice President, Analytics, Amazon Web Services, Inc. “Amazon Redshift Materialized Views allow customers to realize a significant boost in query performance in ETL pipelines and BI dashboards. By integrating Etleap with this new functionality, customers can seamlessly get the benefits of Amazon Redshift Materialized Views without needing to make any application changes.”
Learn how Etleap and Looker helped AXS reduce manual ETL work and reporting, allowing them to focus on growth for themselves and their clients.
AXS is a ticketing company for live entertainment
AXS is a leading ticketing, data, and marketing solutions provider in the US, UK and Europe. The company and its solutions empower more than 200 clients— teams, arenas, theaters, clubs and colleges— to turn data into action, maximize the value of all their events and create joy for fans. It is an enterprise event technology platform that services venues, promoters and sports teams; providing fans the opportunity to purchase tickets directly from their favorite venues via a user-friendly ticketing interface. While customers know them as a destination for tickets, clients recognize AXS for their data services, including transforming, reporting, analyzing, and more.
The data services offered by AXS have always been incredibly helpful for clients. But to make them so valuable, a significant amount of ETL work and reporting was required, which resulted in some challenges for the team. To learn more about those challenges, and how they eventually found a solution in both Etleap and Looker, we spoke with Ben Fischer, the Sr. Director of Business Intelligence and Strategy.
Ben oversees the Business Intelligence and Strategy team, which manages everything from integrations and building data models to powering the data warehouse and products across AXS. The team’s main objectives are to power AXS’s internal data services, while also delivering data services for clients.
Before Etleap and Looker, the Data Engineering team was spending more and more of their time working on internal and external requests for one-off ingestions and custom data sources. Each data source would take anywhere from half a day to weeks (or even months) to implement, which meant the team was spending most of their time on ETL work, and not enough time on making the data useful.
“With Etleap, we’re able to do the ETL end-to-end and get it directly into the hands of whoever’s trying to use it right away.”
– Ben Fischer, Sr. Director of Business Intelligence
Ben told us, “The whole team was just getting sucked into ETL work constantly, which was not the best use of their time. We wanted to be working on modeling and on the products.”
Not so fast. The “length” of a string may not be exactly what you expect. It turns out that the string length property is the number of code units in the string, and not the number of characters (or more specificallygraphemes) as we might expect. For example; “😃” has a length of 2, and “👱♂️” has a length of 5!
In our application we have a data wrangler that lets you view a sample of your data in a tabular format. Since this table supports infinite scrolling, both rows and columns are rendered on demand as you scroll vertically or horizontally. We can’t render all the rows and columns at once since a table could easily include more than a hundred thousand cells, which would bring the browser to its knees.
“The ‘length’ of a string may not be exactly what you expect.”
Imagine if most rows of a column contains a small amount of data, such as a single word, but a single row contains more data, such as a sentence. If this row is outside of the currently viewed area we don’t want the column to expand as you scroll down, and we definitely don’t want to cram the sentence into the same small space that’s required by the word. This means that we need to find the widest cell in the column before rendering all the cells. It’s fast and straightforward to find the length of the content in each cell, however what if the cell contains emojis or other content where we can’t rely on the length property to give us an accurate value?
Code units vs. code points
UTF-16 is a variable length encoding, which means that it uses either 2 or 4 bytes for each code point depending on what is required. To differentiate, we say that UTF-16 uses one or two code units to represent one Unicodecode point. The most used characters all fit into one code unit, however some of the more exotic characters, such as emojis, require two code units.
This is where a problem arises. Since the .length property returns the number of code units, and not the number of code points, it does not directly map to what you may expect. As an example, the emoji “☺️” has a length of 2, even though it looks like only one character.
How can we work around this? ES2015 introduced ways of splitting a string into its respective code points by providing astring iterator. Both Array.from and the spread operator […string] uses this internally so both can be used to get the length of a string in code points.
Environment Specific Differences
Did you know there’s aninja cat emoji? Neither did we, because it’s a Windows-only emoji! It’s represented by a combination of “🐱” and “👤”. This means that Windows users will see this combination as one character, while other users will see it as two characters. Depending on the users choice of fonts, they could even see something completely different. You could try to prevent this issue by choosing a specific font for your web app, however that won’t be sufficient as the browser will still search through other fonts on your system if a character is not available in your chosen font.
“The various environment specific differences means that there’s generally no way of measuring the rendered width of a string mathematically. “
The various environment specific differences means that there’s generally no way of measuring the rendered width of a string mathematically. Therefore, the only way to determine the pixel length is to render it and measure. For our use case in the wrangler, this is exactly what we wanted to avoid in the first place. However there are some optimizations that we can make.
Instead of rendering all the strings in each column, we can split the strings into their corresponding graphemes and render them individually. This allows us to cache the pixel length of each grapheme we encounter. Since there are substantially fewer graphemes than unique strings in a table, this results in a significant reduction in total rendering. This way we can easily determine the correct width of a column, all while keeping the scrolling snappy and your browser happy.
Between 15:30 UTC on 8/27 and 14:00 UTC on 8/29 we experienced periods of higher-than-usual pipeline latencies. Between 04:00 and 10:00 UTC on 8/29 most pipelines were completely stopped. At Etleap we want to be transparent about system issues that affect customers, and this post summarizes the timeline of the incident and our team’s response, and what we are doing to prevent a similar incident from happening again.
Number of users with at least one pipeline with higher-than-normal latency.
What happened and what was the impact?
At around 11:30 UTC on 8/27 our ops team was alerted about spikes in two different metrics: CPU of a Zookeeper node and stop-the-world garbage collection (STW GC) time in a Java process responsible for orchestrating certain ETL activities. The two processes were running in different Docker containers on the same host. From this point onwards we saw intermittent spikes in both metrics and periods of downtime of the orchestration process, until the final fix was put in place at 14:00 UTC on 8/29. Additionally, at 15:30 UTC on 8/27 we received the first alert regarding high pipeline latencies. There were intermittent periods of high latency until 10:00 UTC on 8/29.
When our ops team received the first alert they followed our incident response playbook in order to diagnose the problem. It includes checking on potential causes such as spikes in usage, recently deployed changes, and infrastructure component health. The team determined that the issue had to do with the component that sets up source extraction activities, but found no other correlations. Suspecting an external change related to a pipeline source was leading to the increased garbage collection activity, they went on to attempt to narrow down the problem in terms of dimensions such as source, source type, and customer. Etleap uses a Zookeeper cluster for things like interprocess locking and rate limiting, and the theory was that a misbehaving pipeline source was causing the extraction logic to put a significant amount of additional load on the Zookeeper process, while at the same time causing memory pressure within the process itself. However, after an exhaustive search it was determined that the problem could not be attributed to a single source or customer. Also, memory analysis of the Java process with garbage collection issues showed nothing out of the ordinary.
I am pleased to announce our integration with Snowflake. This is the second data warehouse we support, augmenting our existing Amazon Redshift data warehouse and our S3/Glue data lake offering.
Etleap lets you integrate all your company’s data into Snowflake, and transform and model it as necessary. The result is clean and well-structured data in Snowflake that is ready for high-performance analytics. Unlike traditional ETL tools, Etleap does not require engineering effort to create, maintain, and scale. Etleap provides sophisticated data error handling and comprehensive monitoring capabilities. Because it is delivered as a service, there is no infrastructure to maintain.
Like any other pipeline set up in Etleap, pipelines to Snowflake can extract from any of Etleap’s supported sources, including databases, web services, file stores, and event streams. Using Etleap’s interactive data wrangler, users have full control over how data is cleaned, structured, and de-identified before it is loaded into Snowflake. From there, Etleap’s native integration with Snowflake is designed to maximize flexibility for users in specifying attributes such as Snowflake schemas, roles, and cluster keys. Once the data is loaded, Etleap’s SQL-based modeling features can be used to further improve the usability and performance of the data for analytics.
I’m excited to tell you about two new features we’re launching today: Models and History Tables.
Etleap has long supported single-source transformations through data wrangling. This is great for cleaning, structuring, and filtering data, and for removing unwanted data, such as PII, before it is loaded to the destination. Today, we’re announcing the general availability of models, which enable transformations expressed as SQL queries. Two primary use cases for models are combining data from different sources to build data views optimized for analytics, and aggregating data to speed up analytics queries.
Etleap models are Redshift tables backed by SQL SELECT queries that you define, running against data that has been loaded to Redshift. Etleap creates tables that are the result of these SELECT queries, and updates these tables incrementally or through full refreshes. Etleap triggers updates based on changes to dependent tables, or on a schedule.
Today we’re excited to share that we’ve raised $1.5M from First Round Capital, SV Angel, Liquid2, BoxGroup, and others to continue to scale our enterprise-grade ETL solution for building and managing cloud data warehouses.
ETL has traditionally been associated with expensive projects that take months of custom development by specialized engineers. We started Etleap because we believe in a world where analytics teams manage their own data pipelines, and IT teams aren’t burdened with complex ETL infrastructure and tedious operations.
Etleap runs in the cloud and requires no engineering work to set up, maintain, and scale. It helps companies drastically lower the cost and complexity of their ETL solution and improve the usefulness of their data.
Over the past few years we’ve spent a lot of time with analytics teams in order to understand their challenges and have built features for integration, wrangling, and modeling. It’s a thrill to see data-driven customers, including Airtable, Okta, and AXS, use them. Their analytics teams are pushing the boundaries of what’s possible today, and we’re hard at work building features to help bring their productivity to new levels.
Curious how Etleap can solve your analytics infrastructure challenges? Click here to get a demo of Etleap!
This recorded session is from DataEngConf NYC 17. Slides are available on the event page.
There’s an often-quoted statistic that says that data analysts spend 80% of their time preparing data and only 20% actually analyzing it. There’s a lot that we as data engineers can do to help our analytics teams be more productive and spend less time worrying about data preparation. This session discusses common problems in data warehousing infrastructure from the point of view of analytics teams, and suggests practical solutions.
React.js is a great library for creating user interfaces consisting of components. In the browser React is used to output DOM elements like divs, sections and.. SVG! The DOM supports SVG elements, so there is nothing stopping us from outputting it inline directly with React. This allows for easy creation of SVG components that are updated with props and state just like any other component.
Even though a lot is possible with plain CSS, creating complex shapes like hearts or elephants is very difficult and requires a lot of code. This is because you are restricted to a limited set of primitive shapes that you have to combine to create more complex ones. SVG on the other hand is an image format and allows you a lot more flexibility in creating custom paths. This makes it much easier to create complex shapes as you are free to create any shape you want. If you need convincing, checkout these slides from Sara Soueidan’s great talk about SVG UI components.
At Etleap we have used React with SVG output in some of our graphical components. A great example of this is our circular progress bar.
Circular progress bar used on our dashboard.
This component uses SVG to display the circular progress bar and works just like any other React component. It accepts a few props, including the percentage value to display, and updates whenever new props are received. The reason we opted for SVG in this case was that creating a circular progress bar in CSS is tricky. Using SVG for this was much more appropriate and was straight forward using React to output the SVG markup directly to the DOM, let’s compare the two approaches.
SVG Progress Bar
The essential SVG markup required to render the progress bar is very simple:
We need two circles, one for the dark background, and one for the lighter progress display. The circles are transparent, and the stroke of the circles show the progress and background. To show the amount of progress we use a dashed outline for the circle. If the space between the first and second dash is at least the length of the circumference of the circle only one dash will be shown and we can manipulate the length of that dash to show the current progress. We use stroke-dasharray to specify the length and distance between each dash and stroke-linecap: round to get rounded ends.
CSS Progress Bar
Let’s have a look at how we can create a similar progress bar in CSS:
Since CSS does not support stroke-dasharray, nor stroke-linecap, we are immediately at a disadvantage, therefore lets simplify the problem and start by creating a pie-chart. We create two circles here as well, one for the background and one for the progress bar. To display progress we need to be able to cut away part of the circle, so that we are left with a pie slice. To make this happen we can use the CSS clip property (unfortunately it has been deprecated, and the replacement clip-path has very poor browser support). This enables us to define a rectangle mask for the circle so that we can hide parts of it. The problem is that this only works for a maximum of 50% at a time, so we actually need two of these, one for the right and one for the left… As you can see; this is already getting pretty complicated, and we have not even looked at how to handle the rounded edges. So to prevent doubling the length of this post we’ll stop here. If you are interested in the full solution (without rounded edges) checkout this post by Anders Ingemann.
When to use SVG
SVG should not be a replacement for all graphical user elements, but can be used to more easily achieve tricky UI effects where CSS falls short. The most important difference is that SVG supports custom paths. This means you can create any complex shape you want and easily display it, or use it as a mask. This is especially relevant in scenarios involving charts or line drawings. Other interesting features that CSS is lacking includes drawing text along a path, animating paths, and support for a bunch of filters. That being said, CSS is catching up with SVG and has seen support for several filters, masks, and even custom clip paths. For now though, if your designer has created some truly fancy UI effect that you instinctually disregard as impossible, perhaps it is a good time to look into SVG and make it a reality after all.