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Showing posts from December, 2017

Spot your churning users sooner with statistics

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The girl (or guy) you're dating has called you on average five times a week lately. As a neurotic analytics enthusiast/stalker you happen to know this. Now it's been three days without a call. Should you be worried?
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Becoming a data-driven company

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How does a company become data-driven? Here's what I've learned at Zervant. Radiate information If you want to turn the culture more data-driven, radiate information. You'll never know what will stick. Radiating information also has the side-effect of evangelizing the work of analytics: people will remember you exist and they will take analytics into consideration more easily. Oh right, when we're building this we need to talk to analytics about what to measure!  Write an internal blog, give short presentations about an information nugget, etc. Frequency is more important than the perfect quality of content. Teach People love to learn new things. They will first tell you they're in no way analytical-minded and far from technical, but they will eagerly learn. De-mystifying analytics will help everyone in your organization to speak the same language and be on the same page about what analytics can do for them. As I've held workshops on analyt...
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Dramatically improve your conversions with session recording

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Are you satisfied with your sign-up rate? Or activation rate? Do you think it could be higher? What do you think you can do to improve them? How do you know that? One of the most powerful things that you can do to improve your activation rate is to watch what your users are doing: what confuses them, what path is logical to them and where they fail. When you do that, you will find out that your call-to-action is too mild, your layout is confusing, your responsive design isn't adapting well in all cases or that the navigation path you thought everyone  takes isn't common at all. You cannot solve what you do not know. Luckily most of your issues are likely low-hanging fruits you can find easy remedy to. There are a gazillion services you can use, HotJar  being perhaps the biggest name. But it wasn't the right choice, at least for me. HotJar lets you record towards a quota and you can define pretty flexibly who you want to start recording and when. But HotJar doesn...
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How to access AWS S3 with pyspark locally using AWS profiles tutorial

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At Zervant , we currently use databricks for our ETL processes, and it's quite great. However, there's been some difficulty in setting up scripts that work both locally and on the databricks cloud. Specifically, databricks uses their own prorpietary libraries to connect to AWS S3 based on AWS hadoop 2.7. That version does not support accessing using AWS profiles. Internally, we use SSO to create temporary credentials for an AWS profile that then assumes a role. Therefore, reading the ACCESS_ID and ACCESS_SECRET from the .credentials file is something we don't want to do. In order to accomplish this, we need to set two hadoop configurations to the Spark Context fs.s3a.aws.credentials.provider com.amazonaws.auth.profile.ProfileCredentialsProvider This is done by running this line of code: sc._jsc.hadoopConfiguration().set("fs.s3a.aws.credentials.provider", "com.amazonaws.auth.profile.ProfileCredentialsProvider") Note! You need to set your environment var...
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Snowflake UPSERT operation (aka MERGE)

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You want to insert data to a table, but if a corresponding row already exists (by some rule, e.g. unique key) you want to update that instead of adding a new row, keeping the dataset's unique requirements intact. That's an "UPDATE AND INSERT" operation, or UPSERT. Some SQL languages have native support for it.  PostgreSQL has UPSERT as native. Also MySQL supports the operation with INSERT and ON DUPLICATE KEY UPDATE. How do you do UPSERT on Snowflake? Here's how: Snowflake UPSERT i.e. MERGE operation Snowflake's UPSERT is called MERGE and it works just as conveniently. It just has a different name. Here's the simple usage: MERGE INTO workspace.destination_table d USING workspace.source_table s ON d.id = s.id AND d.val1 = s.val1 WHEN MATCHED THEN update SET d.val2 = s.val2, d.val3 = s.val3 WHEN NOT MATCHED THEN INSERT (id, val1, val2, val3) VALUES (s.id, s.val1, s.val2, s.val3); Here the destination_table and source_table are of similar form,...
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