How women networking should NOT be organized

There was one small episode during ICDE 2017, and although it has been a month already, I still feel like I want to write about. Here is want happened

Among other booths of different vendors there was (as usual) the Amazon AWS. And one of their reps told me,that on Thursday they are going to have a “women event”, and whether I want to sign up, and if I just could leave my email with them. I told her: well, there is a conference banquet on Thursday, at what time precisely your event is going to be? And she said reassuringly: after the banquet!

Now, the banquet would start at 6PM, and on Wednesday evening I receive the following email:

Hi Hettie,
I wanted to reach out on behalf of AWS and invite you to attend the AWS Women in Engineering Networking Event tomorrow on Thursday, April 20. Our recruitment and engineering teams are coming down from Seattle for the ICDE Conference and we’d love to meet you in-person at our happy hour at Blue Door Winery in San Diego (around 3 miles from the conference venue).
There will be wine tasting, artisanal bites, and a raffle on-site. Please feel free to bring guests, the more the merrier!

I am clicking on the invite, and guess what start time it shows? Yes, you are right – at 6PM.

Let me tell you that. The banquet is the most important social event at any conference, and I would always make a point for the younger generation about the importance of attending a conference banquet. There you can be introduced or just introduce yourself to anybody, you can talk at length with the authors of the papers which were most interesting for you. People just are more relaxed and do not run to attend the next session. And if somebody organized a “women networking event” at the same time – how this should be perceived? Like “kid’s table”?! How much this kind of networking would worth? And if the event organizers didn’t bother to look at the conference program when scheduling this event, it’s even worse…

Fortunately, at least at the first glance, there was not that many women who would trade the banquet for this networking event 🙂

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Dos and Don’ts of the Data Warehouse

In the past couple of months the number of employees in our company have grown significantly. And guess what: almost all of the new employees need access to the Data warehouse!

While we were very small, I used to be able (to have time) to explain each new person, how our Data warehouse is organized, how it is being populated, how data is refreshed, and what you should and should not do. But recently I barely could memorize the names of new employees! And when I overheard one of myexperienced co-workers asking one of the new co-workers: do you know how to join tables?… I’ve realized I owe them some education.

So, last Thursday I gave a presentation about our data warehouse, and it was a big success – for many folks it was the first time realizing “how this thing works”. But un-doubtfully the most popular one was the last slide: what not to do with your database.

Since I think those statements are largely universial, I am going to paste here the contents of the last slide.

  • Although you can’t write anything to the Data Warehouse there are plenty of ways to crush the system,  so use caution.
  • Please use the copies of the core tables for exploration purposes only, do not run big queries on them
  • Please kill any query which runs over 1 min and ask somebody from the IT database group for assistance
  • Do not use temporal tables.
  • Do not create objects in the public schema.
  • Before creating a new report or requesting one, please check what’s already available. The view and mat. views in our Data Warehouse are well- documented

Couple of comments
1. “Over 1 minute” is a surprisingly good estimate. Granted, out Data Warehouse is relatively small now, but most of the time when something is running over 1 min, it indicates that either the join criteria  are not specified correctly, or one or more conditions have very low selectivity, or there is an index missing. In all of those cases an IT person should take a closer look

2. Why avoid using temporal tables? Because they occupy the same space on disk which is used to allocate the intermediate result sets, and at the end of the day slow the things down due to extra IO

3. Why not to create objects in the public schema? Well, because it’s public! Because anybody can create tables in the public schema! And everybody create tables owned by them, which other people can’t access. The public schema should only hold the publicly used functions and such.

I think, the rest is self-explanatory!

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Chicago PUG meetup with Joe Conway

Yesterday was a Day – a day when Joe Conway presented at Chicago PUG. He was talking about the PL/R extension of Postgres, which is really important for out data analysts.

We had a full house:

And everybody were listening to the great presentation:

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May PUG with Joe Conway!

I neglected to advertise our May event, and this is going to be indeed the most interesting meetup of 2017! Because just in two days, on May 19 Joe Conway will be speaking at Chicago PUG.

I definitely do not need to advertise him, but I am advertising the fact of his appearance in Chicago, and encourage everybody to attend.

Please RSVP at our Meetup page, and hope to see there.

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The Data science education panel on ICDE 2017

In order to keep up with my own promises to tell more about what was happening on ICDE 2017 I am going to write about the panel on data science education. The panel was called “Data Science Education: We’re Missing the Boat, Again”, and I’d say it was probably the most interesting panel I’ve ever attended! By the time the panel was about to start, there was a huge crowd, and people were encouraged to take a dozen of remaining seats in the first and second rows (do I need to mention that I was at the front five minutes before the panel started?)

The topic of the panel described in my own words was the following. The Data science is a buzz word, students want to be taught “data science”, and there is a common believe that data science is about machine learning and statistical modeling while in reality 80% of time of the data scientists is spent on data pre-processing, cleansing, etc.

The panelists were given the questions which I am copying below.

If data scientists are spending 80% of their time grappling with data, what are they doing wrong? What are we doing wrong? What can we teach them to reduce this cost?
• What should a practicing data scientist learn about sys- tems engineering? What’s the difference between a data engineer and a data scientist?
• Scale is at the heart of what we do, and it’s a daily source of friction for data scientists. How can we teach funda- mental principles of scalability (randomized algorithms, for example) in the context of data systems?
• Perhaps data scientists are just consumers of our technol- ogy — how much do they really need to know about how things work? Empirically, it appears to be more than we think. There is a black art to making our systems sing and dance at scale, even though we like to pretend everything happens automatically. How can we stop pretending and start teaching the black art in a principled way?
• How can we address emerging issues in reproducibility, provenance, curation in a principled yet practical way as a core part of data engineering and data systems? Consider that the ML community has a vibrant workshop on fairness, accountability, and transparency. These topics are at least as relevant from a database perspective as they are from an ML perspective, maybe more so. Can we incorporate these issues into what we teach?
• How much math do we need to teach in our database- oriented data science courses? How can we expose the underlying rigor while remaining practical for people seeking professional degrees?

Bill Howe from UW was a moderator and the first panelist to give his talk.

The second one was Jeff Ullman, and thereby I have nothing more to say:)

Actually, i really liked the fact that he mentioned, that the math courses, linear algebra and calculus should be included into the Database curriculum.  I was always saying that nobody without Calc  BC should be allowed anywhere near any database.

The next panelist was Laura Haas, and again – what else I need to say, except of I’ve enjoyed each and every moment of her presentation?

One thing from her presentation which I find really important is that the Data science is not a part of the Computer Science, and not a part of Database management.  As Laura put it, “we provide the tools”, but not like “we” should teach the DS as a part of CS.

Next panelist was Mike Franklin from UC, and I hope this picture is clear enough for you to see a funny example of DS he is showing.

And the last one was very controversial Tim Kraska from Brown, who started with “he is going to disagree with all the rest of panelists” – and he did.

To be honest, it’s very difficult to write about this panel, because each of you can google all these great people, but you would need to see a video recording of this panel to really fell how interesting, and how much fun it was.

After the panel I talked to several conference participants, who like me are from industry and asked them what are they looking for when hiring recent grads. And literally everybody said the same thing that I was thinking about: they said they hire smart people with solid basic education, people who can solve problems, “and we will teach them all the rest”. Which I couldn’t agree more!

Paradoxically, the students think it’s cool to have something about “Data science” in their curriculum, they often think it will make them more marketable, but real future employers do not care that much!

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ICDE 2017 – Laura Haas’ keynote talk

I’ve missed the first keynote of the conference, but there was no way I could miss the second one – Laura Haas’ “Leveraging data and people to accelerate data science”.

Here is a reference for the Accelerated Discovery Lab in IBM, where you can find lots of information about different projects. The keynote talk highlighted the project related to food contamination.

Below are several pictures from the presentation.

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ICDE 2017 – Day 3

This will be again more a note to myself to write in more details about what I’ve learned at ICDE 2017.

I didn’t stay the whole Day 3, but I made sure to pay for the TSA pre-check and use the fact that the conference venue was so close to the airport.  The main events of Day 3 were:

  • The keynote by Pavel Pevzner about the “New revolution” in online education.  I can’t say I liked it, because I disagree with a lot of what was said, but I it was something which would make you think
  • The Industry 2 session, which was to be honest less interesting than Industry 1, although quite educational.  The last presentation made me think again that the way we use the FDW for populating our Data Mart is something not convetional, and probably should   be publicized more.

During the conference people were asking me what y company is doing, and I’ve realized that our data modeling and predictive analytics (which I do not know much about) were of the most interest. Also, I am always saying the “we do not have any big data”, but now, seen what other people consider being “big data” I am starting to think that may be we have :).

Overall I am very excited about what I’ve learned, about the people I’ve met, adn I want to reinvent my life again, and to do all those great things…. and to submit a paper to ICDE 2018, of cause :).

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