From January 2015, she started to practice leetcode questions; she trains herself to stay focus, develops "muscle" memory when she practices those questions one by one.
2015年初, Julia开始参与做Leetcode, 开通自己第一个博客. 刷Leet code的题目, 她看了很多的代码, 每个人那学一点, 也开通Github, 发表自己的代码, 尝试写自己的一些体会.
She learns from her favorite sports – tennis, 10,000 serves practice builds up good memory for a great serve. Just keep going.
Hard work beats talent when talent fails to work hard.
Architect in the Database Server Manageability group focusing on SQL optimization and execution. The server manageability group was created in 2002 to lower the TCO of Oracle databases by greatly simplifying their management. Led the design and implementation of the following features:
In addition to focusing on core database manageability features, lead architect for the redesign of Oracle Enterprise Manager Cloud Control (EM CC) 12c. EM CC is the product developed by Oracle which manages all software and hardware assets of an IT organization: storage, operating systems, database, cloud, virtualization, middleware, application, etc. EM CC is a J2EE application with multiple server instances and many deployed agent programs responsible to directly interact with all managed assets. Main accomplishments are:
• Lead architect for the re-design of Enterprise Manager • Consulting role for our virtualization and cloud management solutionArchitect in the Database Server Manageability group focusing on SQL optimization and execution. The server manageability group was created in 2002 to lower the TCO of Oracle databases by greatly simplifying their management. Led the design and implementation of the following features: • Automatic SQL Memory management • Automatic SQL Tuning advisor • SQL Performance Analyzer (SPA) • Real-time SQL monitoring • Database Active Report Framework • Fault Diagnosability Infrastructure In addition to focusing on core database manageability features, lead architect for the redesign of Oracle Enterprise Manager Cloud Control (EM CC) 12c. EM CC is the product developed by Oracle which manages all software and hardware assets of an IT organization: storage, operating systems, database, cloud, virtualization, middleware, application, etc. EM CC is a J2EE application with multiple server instances and many deployed agent programs responsible to directly interact with all managed assets. Main accomplishments are: • Lead architect for the re-design of Enterprise Manager • Consulting role for our virtualization and cloud management solution
Lead architect and developer in the Oracle database parallel execution group, which is responsible for parallelizing SQL statements (queries, DML and DDL) and executes them over a RAC cluster. Major accomplishments in this area include:
• Redesign and implement new parallel granule management • Introduce partition-based parallelism: partition-wise joins and aggregations, intra-partition parallelism, advanced pruning techniques, etc. • Self-tuning the degree of parallelism in multi-user environments • Define new Intra SQL parallelization model for parallel query • Database as an ETL engine: lead architect for defining many features aimed at transforming the database into a scalable ETL engine • Large scale data warehouse benchmarks: involved in many high-scale data warehouse benchmarks like TPC-H or POC benchmarks
The firm offers a cloud-based data storage and analytics service, generally termed "data-as-a-service".[4][5] It allows corporate users to store and analyze data using cloud-based hardware and software. Snowflake services main features are separation of storage and compute, on-the-fly scalable compute, data sharing, data cloning, and third-party tools support in order to scale with its enterprise customers.[6] It has run on Amazon S3 since 2014,[2] on Microsoft Azure since 2018[7] and on the Google Cloud Platform since 2019.[8][9] The company was ranked first on the Forbes Cloud 100 in 2019.[10] The company's initial public offering raised $3.4 billion in September 2020, one of the largest software IPOs in history.[11]
Snowflake Inc. was founded in July 2012 in San Mateo, California by three data warehousing experts: Benoît Dageville, Thierry Cruanes and Marcin Żukowski. Dageville and Cruanes previously worked as data architects at Oracle Corporation; Żukowski was a co-founder of the Dutch start-up Vectorwise. The company's first CEO was Mike Speiser, a venture capitalist at Sutter Hill Ventures.[12]
In June 2014, the company appointed former Microsoft executive Bob Muglia as CEO. In October 2014, it raised $26 million and came out of stealth mode, being used by 80 organizations.[13] In June 2015, the company raised an additional $45 million and launched its first product, its cloud data warehouse, to the public.[14][15][16] It raised another $100 million in April 2017.[17][18] In January 2018, the company announced a $263 million financing round at a $1.5 billion valuation, making it a unicorn.[19] In October 2018, it raised another $450 million in a round led by Sequoia Capital, raising its valuation to $3.5 billion.[4][20]
In May 2019, Frank Slootman, the retired former CEO of ServiceNow, joined Snowflake as its CEO and Michael Scarpelli, the former CFO of ServiceNow joined the company as CFO.[8] In June 2019, the company launched Snowflake Data Exchange.[21] In September 2019, it was ranked first on LinkedIn's 2019 U.S. list of Top Startups.[22]
On February 7, 2020, the company raised another $479 million. At that time, it had 3,400 active customers.[23] On September 16, 2020, Snowflake became a public company via an initial public offering (IPO) raising $3.4 billion, one of the largest software IPOs and the largest to double on its first day of trading.[11][24][25][26][27]
On May 26, 2021, the company announced that it would become headquarterless,[28] with a principal executive office located in Bozeman, Montana.[29]
On March 2, 2022, the company acquired Streamlit for $800 million. [30] Then on October 17, 2022, the company announced an investment in advanced TV advertising firm OpenAP.[31]
Snowflake announced it would acquire privacy-focused search startup Neeva in May 2023.[32]
On October 23, 2023, Snowflake acquired a start-up named Ponder to expand its Python capabilities for enterprises.[33]
MicrosoftMar 2017 - Oct 2019 · 2 yrs 8 mosMar 2017 - Oct 2019 · 2 yrs 8 mosGreater Seattle AreaGreater Seattle Area
• Managed a team of 10 engineers/data scientists to build a marketing big data platform for campaign segmentation purpose. • Gathered requirements from business owners. Used Scope to perform data analysis in Cosmos (MS internal big data platform). Wrote functional specification based on the results. • Worked closely with data scientists, trained and deployed more than 10 machine learning models. • Coordinated internal and external teams. Implemented agile processes. Ran sprints for the team. • Represented the team to report program status to leadership and partner teams on a regular basis. Established communication with technical and non-technical clients.
Joe Rabil from Rabil Stock Research discusses how he would add and reduce to a long-term holding that you like and want to hold as long as the trend is positive. He shows how many of the institutional portfolio managers he has worked with over the past 30 years go about trading around a core position. He then analyzes the stock requests that came through this week.
One of the keys for me in trading is to trade around a core position. Trading around a core position means that I keep a small position and then I trade around that small position. If I am long, then I will buy dips and sell rallies all throughout that time I keep a small position with a wide stop. I trade small enough in my core position that I don't get shaken out easily. This has been the case for me with Gold for sometime now. I want to be long Gold for Macroeconomic reasons, but the charts have been too choppy for me to stay with a full position. In today's video I go over Gold on the charts and explain my process for trading around a core position.
What does it mean? How do you do it? Trading around a core position is about going, say long on stock XYZ at a certain price for 1000 shares and you're allowing yourself a maximum of say, 3000 shares (you have to work this beforehand and have it in your trading plan). So you could buy another tranch at a pullback. If the market recovers you could sell back the 1000 shares. You are buying in pullbacks and scalping in combination with your core position. The point of the whole strategy is that you almost have two trades running at the same time. One is the swing trade and you also have multiple trades going on at the same time.
Julia's note
I like the explanation of steps:
Choose a stock to invest in, long term the stock will go higher.
Setup core position, given an example, one thousand shares, target price, and stop loss price, max share, for example, three thousand shares.
First purchase core positions with stop loss, and then while waiting for the up process, swing trade when pullback with stop loss.
Incredibly good explanation, I learn the trading around a core position very well.
