Gayane Saroyan is seeking a challenging career opportunity utilizing her people skills, persuasiveness, creativity, and self-motivation. She has over 10 years of experience in banking and customer service roles, most recently as Assistant Branch Manager at Citibank where she helped achieve sales and service goals, oversaw daily operations, and ensured excellent customer service. Prior to that, she was a Lead Teller and Sales Associate, receiving recognition for her superior customer service skills. She has strong management, communication, and leadership abilities along with administrative and computer skills.
Building Reactive Fast Data & the Data Lake with Akka, Kafka, SparkTodd Fritz
In this session, we will discuss:
* reactive architecture tenets
* distributed “fast data” streams
* application and analytics focused Data Lake
Enterprise level concerns and the importance of holistic governance, operational management, and a Metadata Lake will be conceptually investigated. The next level of detail will be to explore what a prospective architecture looks like at scale with Terabytes of ingestion per day, how scale puts pressure on an architecture, and how to be successful without losing data in a mission critical system via resilient, self-healing, scalable technologies. DevOps and application architecture concerns will be first-class themes throughout.
Reactive principles and technology will be the second act of this talk. Kafka. Akka. Spark. Various streaming technologies (Kafka Streams, Akka Streams, Spark Streaming) will be reviewed to identify what they are best suited for. The fast data pipeline discussion will center around Kafka, Akka, and Apache Flink (Lightbend Fast Data platform). We’ll also walk through an exciting addition to the Akka family, Alpakka, which is a Camel equivalent for Enterprise Integration Patterns.
The final act will be to dive into the Data Lake, from both an analytics and application development perspective. Technologies used to explain concepts will include Amazon and Hadoop. A Data Lake may service multiple analytics consumers with various “views” (and access levels) of data. It may also be a participant of various applications, perhaps by acting as a centralized source for reference data or common middleware (in turn feeding the analytics aspect). The concept of the Metadata Lake to apply structure, meaning and purpose will be an over-arching success factor for a Data Lake. The difference between the Data Lake and Metadata Lake is conceptually similar to a Halocline… Various technologies (Iglu/Snowplow and more) will be discussed from a feature standpoint to flesh out the technology capabilities needed for Data Lake governance.
Building Reactive Fast Data & the Data Lake with Akka, Kafka, SparkTodd Fritz
In this session, we will discuss:
* reactive architecture tenets
* distributed “fast data” streams
* application and analytics focused Data Lake
Enterprise level concerns and the importance of holistic governance, operational management, and a Metadata Lake will be conceptually investigated. The next level of detail will be to explore what a prospective architecture looks like at scale with Terabytes of ingestion per day, how scale puts pressure on an architecture, and how to be successful without losing data in a mission critical system via resilient, self-healing, scalable technologies. DevOps and application architecture concerns will be first-class themes throughout.
Reactive principles and technology will be the second act of this talk. Kafka. Akka. Spark. Various streaming technologies (Kafka Streams, Akka Streams, Spark Streaming) will be reviewed to identify what they are best suited for. The fast data pipeline discussion will center around Kafka, Akka, and Apache Flink (Lightbend Fast Data platform). We’ll also walk through an exciting addition to the Akka family, Alpakka, which is a Camel equivalent for Enterprise Integration Patterns.
The final act will be to dive into the Data Lake, from both an analytics and application development perspective. Technologies used to explain concepts will include Amazon and Hadoop. A Data Lake may service multiple analytics consumers with various “views” (and access levels) of data. It may also be a participant of various applications, perhaps by acting as a centralized source for reference data or common middleware (in turn feeding the analytics aspect). The concept of the Metadata Lake to apply structure, meaning and purpose will be an over-arching success factor for a Data Lake. The difference between the Data Lake and Metadata Lake is conceptually similar to a Halocline… Various technologies (Iglu/Snowplow and more) will be discussed from a feature standpoint to flesh out the technology capabilities needed for Data Lake governance.
Quite a bit has changed since our March sessions - not just the Prime Minister and the country’s approach to Europe, but also a fair amount that affects day to day legal practice.
We’ll be cutting through the noise on Brexit and looking at what companies and other bodies are doing now, and what you can and should be doing, for what may be a few uncertain years.
You’ll have greater clarity as to how Brexit affects the different areas of law that are governed by European regulation, including technology law, commercial law, data protection, intellectual property rights and employment law.
In particular you’ll better understand what to do when advising your clients, dealing with contracts and new projects, and know where there are areas of emerging certainty. We’ll also be sharing drafting.
Not all that’s new is Brexit-related - so we’ll also be covering new regulations and new case law over the last 6-12 months, including how recent decisions affect offers to settle disputes.
https://www.brownejacobson.com/sectors-and-services/sectors/in-house-legal
L'Insee a publié le 17 janvier 2017, le bilan démographique 2016.
Selon l'Insee, au 1er janvier 2017, la France compte 66 991 000 habitants, soit une hausse de 0,4% par rapport à 2015.
L'Insee révèle également une baisse des naissances pour la deuxième année consécutive. En 2016, 785 000 bébés sont nés en France soit 14 000 de moins qu'en 2015. L'indicateur conjoncturel de fécondité s'établit à 1,93 enfant par femme, il était de 1,96 en 2015.
Les espérances de vie, par contre, sont en hausse: dans les conditions de vie en 2016, une femme vivrait en moyenne 85,4 ans et un homme 79,3 ans.
Deep Dive : Spark Data Frames, SQL and Catalyst OptimizerSachin Aggarwal
RDD recap
Spark SQL library
Architecture of Spark SQL
Comparison with Pig and Hive Pipeline
DataFrames
Definition of a DataFrames API
DataFrames Operations
DataFrames features
Data cleansing
Diagram for logical plan container
Plan Optimization & Execution
Catalyst Analyzer
Catalyst Optimizer
Generating Physical Plan
Code Generation
Extensions
1. Gayane Saroyan
13167 Constable Ave
Granada Hills ,CA 91344
Gayanekamalyan@yahoo.com
Phone: 818 530-3323
Alt: 818 530-8883
Objective:
To obtain a challenging career with a progressive company that affords me the
opportunity to utilize my people skills; persuasiveness; creativity; and self-motivational
skills to further aid in achieving long-term career commitment.
Work Experience:
April 2006 to Present
Citibank
Assistant Branch Manager
As Assistant Branch Manager I work closely with the Branch Manager to achieve
overall sales and service goals by maximizing sales referrals, overseeing daily operations
and ensuring the delivery of excellent client service . Developing and executing action
plans to prove operational controls.
Assist in creating a strong sales, service and operation culture, fostering an environment
in which all branch employees and segment partners excel and achieve scorecard goals.
Support portfolio growth by coaching Tellers to effectively identify referral
opportunities and perform sales/referral activities. Develop high performing tellers in
order to build bench strength.
.
Leverage workforce planning tool to optimize staffing in branches to ensure effective
lobby and line management to provide exceptional client service and comply with all
legal and regulatory requirements.
Partnered closely with Branch Manager to manage hiring, performance management and
compensation of Tellers. Adhere to staffing guidelines and recommended mix of fulltime
and part-time employees.
Conduct discussions with prospects to understand background and identify needs; clearly
communicate potential solutions.
Conduct outbound sales activities (e.g., phone out-reach, community events, meetings
with local businesses) to establish visibility in the community and drive business to the
branch.
.
2. April 2006 to August 2013
Citibank
Lead teller
• Assisted Service Manager in supervising, training, and coaching successful tellers.
• Exceeded daily and quarterly sales and referral goals for the branch.
• Assisted the Service Manager on daily tasks and the Control Binder.
• Maintained and ordered branch supplies and brochures.
• Maintained accountability of transactions including the count and supervision of
large amounts of cash flow.
• Balanced and processed transactions made through the automated
teller machine
Identified and resolved customer's issue and problems offering solutions that enhanced
the customer's banking relationship.
• Recognized by customers and peers for superior quality customer service.
May 2005 to June 2006
PayLess Shoes Glendale, CA
Sales Associate
• Received payment by cash, check, and credit cards.
• Issued receipts, refunds, credits, or change due to customers.
• Responsible of all aspects of the store (orders, sales, employees, etc.).
• Assisted clients in the sale.
• In charge of changing the stores decoration every 2 weeks (promotional posters,
display products, price changes).
• In charge of preparing orders and receiving shipments once a week.
Skills Summary
• Reliable and dependable.
• Proven management, communication and leadership skills.
• Excellent administrative and customer service capabilities.
• Computer knowledge: Microsoft Word, Excel, Power Point, Access and Internet.
• Culturally responsive, fluent in English, Russian, Bulgarian, and Armenian.
• Self starter and quick learner.
• Excellent inter personal skills with customers and fellow employees.