![]() ![]() ![]() Mai, Juliane Stisen, Simonĭistributed hydrologic models are typically calibrated against streamflow observations at the outlet of the basin. Preliminary modeling results suggest that the spatial and velocity distributions in the sodium exotail are sensitive to the near surface lunar sodium velocity distribution and that observations of this sort along with detailed modeling offer new opportunities to describe the time history of lunar surface sputtering over several days.Ĭalibration of a distributed hydrologic model using observed spatial patterns from MODIS dataĭemirel, Mehmet C. The spatial distribution of the sodium atoms is elongated along the ecliptic with the location of the peak intensity drifting 3 degrees east along the ecliptic per night. We present both spatially and spectrally resolved observations obtained over four nights around new moon in October 2007. Here we use the Wisconsin H-alpha Mapper to obtain the first kinematically resolved maps of the intensity and velocity distribution of this emission over a 15 x times 15 deg region on the sky near the anti-lunar point. Our earlier observations determined the average radial velocity of sodium atoms moving down the lunar tail beyond Earth along the Sun-Moon-Earth line (i.e., the anti-lunar point) to be 12.4 km/s. The lunar sodium tail extends long distances due to radiation pressure on sodium atoms in the lunar exosphere. He said that they are excited with what they are anticipating and that they can measure them on the basis of Airflow.Sodium Atoms in the Lunar Exotail: Observed Velocity and Spatial Distributions Otto believes that Airflow is the defacto tool for data engineers and that it is going to be the core of all distributed data services. Otto said that Airflow's large footprint makes it easy for Astronomer to focus on picking it up and taking it to the next level, which is a natural extension of what the company has already been doing. George Mathew, managing director at Insight Partners, said in a written statement that Apache Airflow has become the generation platform for modern orchestration. The company intends to use the new capital to grow its engineering and customer success teams, technology development and scale its go-to-market operations.Īs the modern data stack has arrived at scale, we need an experience to support it. About $300 million has been given to Astronomer to date. The latest round of funding was led by Insight Ventures. The next development for the modern data platform would be the combination of us two. He pointed out that if you don't have access to the data, the lineage doesn't understand the data end-to-end. ![]() Otto said Datakin was building a data lineage product and was in the open source community. The Datakin data operations tool was acquired from the founders of the Open Lineage and Marquez open source projects. Otto said that the closing of $213 million in Series C funding gave Astronomer enough of a cash cushion to advance some of its strategic plans. We are getting ready to launch a product and start scaling field teams, so there is a big opportunity out there. Otto didn't go into specifics about other growth metrics, but he did say that he expects Astronomer to grow its base considerably in the year 2022, and that the company was just getting started.įor the last couple of years, we focused on Airflow and working with the people who created it. In the past two years, Astronomer has grown its employee count by 10 times and now has hubs in Cincinnati, New York, San Francisco and San Jose. Astronomer is one of the top contributors to Airflow.Īstronomer raises $3.5M to make data analytics more accessible Joe Otto, CEO of Astronomer, explained that data orchestration is like a muscle that has to be connected as more and more data services are being launched.Īirflow is used by hundreds of thousands of data teams and 8 million monthly downloads, up from 180,000 in the previous year. The company began developing its modern data orchestration tools, powered by Apache Airflow, an open source platform for data engineering, that enables users to build, run and observe data engineering pipelines, and started driving that project in the year 2018. Since we profiled the company, it has grown a lot.Īt that time, the data analytics company got $3.5 million in funding to develop its tool for what happens after you collect a bunch of data, namely assembling and organizing it so the data can be analyzed. ![]()
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