At the end, the cumulative data flow correctly! We modified our ETL -framework to record the sequence of SQL queries in every ETL and submit them to Queryparser, at which point Queryparser was programmatically generating graphs of data-flow for all the modeled tables in our warehouse. See Figure 5, below, for an example:. Table lineage data has been useful in responding to data quality incidents, decreasing the mitigation time by offering tactical visibility into incident impact.
For example, given the table dependencies in Figure 5, if there was an issue in raw table A, we would know that the scope of impact included the modeled tables E and G.
We would also know that once the issue was resolved, E and G would need to be backfilled. To address this, we could combine the lineage data with table access data to send targeted communications to all users of E and G. Table lineage data is also useful for identifying the root cause of an incident.
For instance, if there was an issue with modeled table E in Figure 5, it could only be due to the raw tables A or B. Finally, the ability to analyze queries at runtime unlocked defensive operations tactics that enabled our data warehouse to run more smoothly. With Queryparser, queries can be intercepted en route to the data warehouse and submitted for analysis. If Queryparser detects parse errors or certain query anti-patterns, then the query can be rejected, reducing the overall load on the data warehouse.
Fred Brooks famously argued that there is no silver bullet in software engineering. While beneficial for our storage needs, Queryparser was no exception. As the project unfolded, it revealed some interesting essential complexities.
This was immediately apparent during the prototype phase, when Queryparser exclusively handled Vertica , and was further confirmed when support for Hive and Presto was added.
Second, tracking catalog state was hard. Recall that catalog information is needed for resolving column names and table names. We experimented briefly with using Queryparser to track catalog state; if Queryparser was already analyzing every query, we wondered if we could simply add an analysis that reported the schema changes and produce the new catalog state by applying them to the previous catalog state.
Ultimately, that approach was unsuccessful due to the difficulty of ordering the entire stream of queries. Instead, our alternative and more effective approach was to treat the catalog state as more-or-less static, tracking the schema membership and column-lists of tables through configuration files.
Third, sessionizing queries with Queryparser was difficult. In a perfect world, Queryparser would be able to track table lineage across an entire database session, accounting for transactions and rollbacks and various levels of transaction isolation. In practice, however, reconstructing database sessions from the query logs was difficult, so we decided not to add table lineage support for those features. Finally, Hive is a leaky abstraction over the underlying filesystem.
This particular issue was temporarily mitigated with a regular expression to infer the table name from the HDFS path. However, in general, if you choose to bypass the SQL abstractions of Hive in favor of filesystem-layer operations, then you opt out of Queryparser analysis. Installing Haskell itself was straightforward. You can integrate Parseur with Zapier , allowing you to send scraped data from your email to thousands of apps.
You could, for example, create Google Calendar events or Mailchimp subscribers, automatically, when new emails come in. The downside: Parseur is more expensive than the alternatives.
That might be worthwhile, depending on your needs, so try Parseur out before you decide on a service. Don't let the pseudo-French name turn you off entirely. SigParser Web. SigParser is the most specialized of all the tools here: it focuses exclusively on the contact information in email signatures. But think about the value in that—most emails have signatures, meaning there's all sorts of contact information in your inbox that you never even think about.
You could copy and paste that contact information into your address book or CRM of choice, but with SigParser, you don't have to. The free version of SigParser reviews 90 days of your emails—you can pay more to go back further. The app can also scan new emails as they come in, meaning all of the contact information in your inbox is automatically grabbed.
You can then send this info to your CRM, address book, or anywhere else it might come in handy. You could, in theory, use any of the tools here to scrape contact information, but it would take some work. Everyone's email signature is a little bit different, and simple rules aren't enough to consistently parse it. This app is made for one job, and in my tests, it did an admirable job on a variety of different signatures.
It may seem like a simple thing, but it's potentially game-changing if your business depends on following up with potential customers. You can also integrate SigParser with Zapier , allowing you to send scraped contact information to thousands of apps, including Mailchimp and Constant Contact.
Justin Pot is a writer and journalist based in Hillsboro, Oregon. He loves technology, people, and nature, not necessarily in that order. Learn more: justinpot. Why Zapier? How Zapier works. Product tour. Customer stories. Popular ways to use Zapier. Apps that work with Zapier. Explore Zapier by job role. Blog Read the Zapier blog for tips on productivity, automation, and growing your business.
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