Kiva Lending History

Over the past seven years I’ve participated in 46 loans through Kiva.org, a microcredit lending facilitator. I try to target my loans at female entrepreneurs in Sub-Saharan Africa whenever possible, but I never had a feel for how consistent I was being in my lending. Luckily, Kiva saved me the trouble of having to process my portfolio data.

Gender

I’ve been successful at targeting loans towards female entrepreneurs. Over 46 loans, 86.96% have gone to females or groups comprised of females.

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Countries

This chart contains the percentages for the 10 countries receiving most of the loans. I’ve lent to a total of 23 different countries. The largest percentage of my loans go to Sub-Saharan Africa.

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Sectors

I didn’t expect this, but I suppose it makes sense. Agriculture is a primary industry in lesser-developed countries and helps to employ a number of people.

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Most impressively, of the 46 loans I’ve made, only one has defaulted. Most pay back on time and I reuse those funds for additional loans.

If you haven’t made a loan through Kiva.org, I highly recommend it.

Which Database are You Taking to Prom?

Project downloads, job postings, Github commits and mailing list activity are common methods to gauge traction for commercial and open source software projects. I frequently use those same metrics as input when looking at emerging projects, and for good reason.

Increasing traffic on a user mailing list indicates growing use and adoption. However, if that traffic continues growing, especially after the milestone release, it can indicate poor documentation or quality control. Do the same questions keep coming up on user forums? Your documentation is poor or nonexistent. Did the bug count go up after the 1.0 release? Looks like you’re focusing on features over quality.

Metrics like these are useful but they don’t exist in a vacuum. They need context, the type provided by the overall market, vendor strategies and customer feedback. A simple measure of popularity lacks this context, and therefore lacks insight. Additionally, popularity doesn’t measure sentiment. You don’t know if someone on Stackoverflow is saying “Database X is awesome,” or “Database Y is awful.”

Also concerning is the self-fulfilling notion of popularity. Once a database is measured as relatively more popular than another, it will experience a further uptick in popularity metrics and the cycle continues. Ultimately this results in more marketplace hype and confusion.

Which Database are You Taking to Prom?

Project downloads, job postings, Github commits and mailing list activity are common methods to gauge traction for commercial and open source software projects. I frequently use those same metrics as input when looking at emerging projects, and for good reason.

Read more…

BI Hadoop Specialists Trail, Broader Tools Lead

There is a romantic notion of leaving the past behind and embracing the future unencumbered. Previous mistakes forgotten, we can venture forward to accomplish great things to the amazement of friends, colleagues and casual onlookers.

This is the promise made by BI and analytics vendors in the Hadoop-only ecosystem. After all, if your data moves to Hadoop, why concern yourself with data stored in legacy data warehouses? Based on the audience response from a polling question conducted during a webinar on Hadoop 2.0, you can’t escape your past. You can only embrace it.

On January 16th, Merv Adrian and I presented webinars discussing the impact of Hadoop 2.0. As part of the webinar, we asked our audience three polling questions. One of the questions asked participants which SQL-on-Hadoop methods they were most likely to use to access data stored in HDFS and HBase. We decided to ask this question based on the arms race occurring in the Hadoop ecosystem based on SQL. Here are the responses: hadoop_webinar_q2

These results indicate that analytics tools lead, but Hadoop-specific tools trail significantly. Most responded they were most likely to use interfaces provided by existing analytics tool providers. Hive came in second, indicating increasing familiarity and comfort with the most established SQL-on-Hadoop interface. However, the Hadoop-specific BI specialists were tied for last. There are a few possible reasons for this apparent reluctance, like maturity, availability of skills and existing investment in analytics tools. However, the understanding that data exists in more places than just Hadoop may be a core factor in attendee responses. If your data lives in warehouses, marts, as well as Hadoop, your analytics tools must cope with that reality.

BI Hadoop Specialists Trail, Broader Tools Lead

There is a romantic notion of leaving the past behind and embracing the future unencumbered. Previous mistakes forgotten, we can venture forward to accomplish great things to the amazement of friends, colleagues and casual onlookers.

This is the promise made by BI and analytics vendors in the Hadoop-only ecosystem. After all, if your data moves to Hadoop, why concern yourself with data stored in legacy data warehouses? Based on the audience response from a polling question conducted during a webinar on Hadoop 2.0, you can’t escape your past. You can only embrace it.

Read more…

Monarchs at Natural Bridges State Beach

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