Data Analyst Training of Google BigQuery 2017-11-09T13:05:49+00:00

Data Analyst Training of Google BigQuery

Cloud Technologies 

(0 votes)

Data Analyst Training of Google BigQuery

Data Analyst Training of Google BigQuery

Objective of this Google BigQuery Training

 The objective of this course is to provide an introduction to Google BigQuery Fundamentals.  This course focuses on fundamental theory. You will receive a full training and Google BigQuery Fundamentals. This course guarantees you that you will receive all tools end theory needed from experts in the field.

This course takes students through the fundamentals, giving them a solid foundation that they can build upon, then moves on to more advanced knowledge, teaching them how they can apply Google BigQuery Fundamentals in practical situations.

TRAINING METHODOLOGY

In Class: $9,999
Locations: NEW YORK CITY, D.C, BAY AREA.
Next Session: Nov 25th 2017

Online: $4,999
Next Session: Nov 25th 2017

GET 1 WEEK FREE TRIAL

Home / All courses /  Cloud Technologies /Data Analyst Training of Google BigQuery

Data Analyst Training of Google BigQuery

Instructor: John Doe, Lamar George

(0 votes)

DESCRIPTION

Data Analyst Training of Google BigQuery

Data Analyst Training of Google BigQuery

Understanding Google BigQuery:-

Google BigQuery(used by Google BigQuery Training)  is a cloud-based big data analytics web service for processing very large read-only data sets.

BigQuery (used by Google BigQuery Training) was designed for analyzing data on the order of billions of rows, using a SQL-like syntax. It runs on the Google Cloud Storage infrastructure and can be accessed with a REST-oriented application program interface (API), which is very important in BigQuery.

BigQuery is what Google calls an “externalized version” of its home-brewed Dremel query service software (which is very useful in Google BigQuery). Dremel and BigQuery (Google BigQuery) employ columnar storage for fast data scanning and a tree architecture for dispatching queries and aggregating results across huge computer clusters.

BigQuery (Dremel) has been used inside Google to track device installation data, create crash reports and analyze spam, which is very useful in Google BigQuery. Since its inception, BigQuery (Google BigQuery) features have continually been improved.

Google BigQuery

Google BigQuery has five advantages:

  1. Scale: Google BigQuery allows you queries of virtually unlimited size/number.
  2. Cost: Google BigQuerycharges by query size.
  3. Managed: servers or hardware are handled by Google’s cloud platform.
  4. Integration of data: Google BigQuery can ingest data from Google Analytics, Android apps, CRM and any other sources, which can then be joined up in your queries.
  5. Ease: Google BigQuery uses a variation of SQL to run queries, making it very accessible to anyone who can use a traditional database.
  6. With in-house Google BigQuery expertise, Data Runs Deep can help your business take advantage of this extremely powerful and versatile data platform.

A quantitative analyst use a variety of tools and techniques to mine big data for information that can provide insight into market trends (and Google BigQuery can resolve this issue). Quotes, trades, and other events that happen at predictable intervals (like financial time series) can be analyzed by using established techniques, including frequency analysis and moving averages (and Google BigQuery).

Dealing with massive datasets can be challenging and that’s why Google BigQuery was created. Traditional tools might not scale as the dataset continually grows and Google BigQuery resolve this issue. Storage requirements can grow as fast as the dataset, so downloading data is no longer a workable approach (and using Google BigQuery can resolve this issue). And it can take a long time to retrieve the right subsets of data from a traditional database query (using Google BigQuery).

Google BigQuery enables you to run SQL-like queries against append-only tables and then returning results very quickly by using the processing power of Google’s infrastructure (which solve all the issues). Google BigQuery can be used on the web, on the command line, and through APIs, including REST APIs. Google BigQuery and Google Cloud Platform enables you to build the data-analysis solutions you need now.

Indeed, security is always important when working with financial data and Google Cloud Platform (Google BigQuery) to keep your data safe, secure, and private in several ways. For example, encrypting all data during transmission and when at rest, and the Cloud.

Data warehousing (and Google BigQuery) is like simultaneous investments in integrating enterprise data to create a single source of truth, and in making that single source for decision-making for the right people (which could be anyone such as a developer) at the right time. Actually, it is economically as possible.

Starting in the past year or two, it was seen an increasing number of users express interest in a fully managed, cloud-based approach to data analytics. In this sense, the serverless cloud (Google Cloud Platform) is on its way to becoming a new, and popular, modern architecture for data warehousing that helps do away with the “complexity tax” that can bedevil customers.

Hadoop (BigData) is the framework and the technology behind Big Data. Querying and analyzing real time data with Hadoop (Big Data) is hard and expensive. At the heart of Hadoop (BigData) is the MapReduce framework, which is not suitable for interactive queries.

Though it was Google that heavily contributed to the MapReduce paradigm, which is also one of the first to identify the drawbacks of MapReduce. Dremel was created because Google’s engineers realized that MapReduce is not ideal to query large, distributed data sets in real time. Dremel enables the engineers to run SQL queries on large data sets in real time. Actually, Dremel was designed to deliver blazing fast query performance on distributed data sets that are stored across thousand of servers.

Google crearted BigQuery, which have a high performance and scalable query engine on the cloud.

BigQuery have the ability to handle large data sets (for example: querying tens of thousands of records might take only a few seconds). Big query is based on standard SQL query language. Indeed, BigQuery is a structured data store on the cloud. Actually, it follows the paradigm of tables, fields and records. Actually, Google replicates BigQuery data across multiple data centers to make it highly available and durable.

BigQuery (Google BigQuery) comes at the end of the BigData pipeline and it is not a replacement to existing technologies.Indeed, Data can be ingested from the data sets stored in Google Cloud Storage (which is so useful).

With the recent announcement of Google Cloud Pub/Sub and Google Cloud Dataflow, BigQuery will play an important role in Google’s cloud strategy (which is very important).

Data Analyst with Google BigQuery Training is working with multiple business intelligence tools deployed across different departments. This means Data Analyst with (Google BigQuery Training)  can build data pipelines dedicated to each tool at the cost of agility and resources.

Data Analyst with Google BigQuery Training AtScale is launching the latest platform, AtScale 6.0, which helps users deploy analytical workloads on Google BigQuery, and cut costs and speed up delivery of results.

Data Analyst with Google BigQuery Training has tested Google BigQuery against some of the most demanding queries.

This Google BigQuery technology has shown to be easy to use. Data Analyst with Google BigQuery Training are working with AtScale’s Adaptive Cache.

Data Analyst with Google BigQuery Training (in AtScale) continually analyzes query patterns and automatically creates and manages aggregates, this means users get faster query results and put significantly less load on BigQuery, resulting in big infrastructure savings.

Data Analyst with Google BigQuery Training uses Adaptive Cache graph engine to optimize the processing of aggregates, and early deployments have shown processing time to improve by up to 10X.

Data Analyst with Google BigQuery Training can re-route big data queries to the Adaptive Cache. Indeed, Data Analysts with Google BigQuery Training use AtScale 6.0 to reduce the number of unique requests reaching the underlying infrastructure.

Data Analysts with Google BigQuery Training use it to reduce infrastructure stress and it also lowers the cost incurred for each query. Data Analyst with Google BigQuery Training tested Google BigQuery and the query costs have been reduced.

In addition there’s a patented Hybrid Query Service technology (used by Data Analyst with Google BigQuery Training). This means that any BI user (and Data Analyst with Google BigQuery Training) can create reports and dashboards to run live on the data and this is now also possible on Google BigQuery.

One benefit of cloud-native data analytics technologies (used by Data Analyst with Google BigQuery Training) is the separation of storage and compute. Data Analyst (with Google BigQuery Training) uses cloud-native storage services to manage and scale, and potentially more affordable as well. Furthermore, Data Analyst with Google BigQuery Training can rapidly scale your processing power and leverage cheap ephemeral compute capacity.

Two Google Cloud Platform (GCP) services are used by data Analyst with Google BigQuery Training. Google BigQuery is a fully managed, petabyte-scaleand low-cost enterprise data warehouse used by Data Analyst with Google BigQuery Training. Google Cloud Dataflow (used by Data Analyst with Google BigQuery Training) is used for running batch and streaming data processing pipelines with equal expressiveness and reliability.

Data Analyst with Google BigQuery Training uses this approach, which can improve performance and efficiency, especially for workloads with high contention and concurrency (which are typical of real-world analytics use cases).

Separation of compute and state is  what makes BigQuery so good at concurrency and Data Analyst with Google BigQuery Training uses it because of that.

Separation of compute and state (which is a term used by Data Analyst with Google BigQuery Training) have the ability to maintain intermediate state between processing stages in a high-performance component separate from either the compute cluster or storage. Data Analyst with Google BigQuery Training may recognize its utility in the shuffle step. For a Data Analyst with Google BigQuery Training, the key difference is that the shuffle state is facilitated by a separate sub-service.

There are several benefits (of Google BigQuery) to separating compute from state (watch this video for more details):

  • Less state in compute, which means that compute becomes more ephemeral and scalable. For a Data Analyst with Google BigQuery Training, it’s easier to re-parallelize processing intra-stage and interstage, and easier to recover from a lost node.
  • Processing is more streamlined, which means that processing stages don’t conflict within the same compute nodes, resulting in resource contention and bottlenecks.
  • For a Data Analyst with Google BigQuery Training, it’s easier for the processing engine to re-partition workloads between stages.
  • The processing engine can take advantage of pipelined execution, which is very important for a Data Analyst with Google BigQuery Training.
  • The processing engine can implement dynamic work repartitioning (the ability to re-parallelize work due to slow workers or data skew), which is very important for a Data Analyst with Google BigQuery Training.
  • Keeping less state in processing nodes.
  • The service can use available resources much more efficiently across compute as well as shuffle, which is very important for a Data Analyst with Google BigQuery Training.

BigQuery and Cloud Dataflow both take full advantage of these benefits:

  • BigQuery’s current implementation of the Dremel distributed query engine deviates from the architecture, which is very important for a Data Analyst with Google BigQuery Training. Subsequent versions of Dremel and BigQuery leverage dynamic processing trees and a separate in-memory shuffle component, which is very important for a Data Analyst with Google BigQuery Training.
  • While historically Cloud Dataflow has implemented shuffle within the processing nodes, which is very important for a Data Analyst with Google BigQuery Training.

The result is faster performance for jobs in high-concurrency environments, which is very important for a Data Analyst with Google BigQuery Training. For this reason, complex workloads (AKA large, multi-stage queries with numerous JOINs executed at high concurrency) perform exceptionally well on services that separate compute and state

Motorola is an avid user of BigQuery and have many Data Analysts with Google BigQuery Training. Motorola Mobility has experienced the differences in performance before and after BigQuery implemented separation, which is very important for this industry. Actually, BigQuery’s Flat Rate pricing model maintains its cluster at a consistent size.

Since BigQuery rolled out its most recent iteration of separation of compute and state in 2015, BigQuery usage at Motorola Mobility has nearly tripled, which is very important for their Data Analysts with Google BigQuery Training. At the same time, both performance variability and query execution speeds improved, which is very important for a Data Analyst with Google BigQuery Training.

Specifically, Motorola Mobility (and their Data Analysts with Google BigQuery Training) observed significant decrease in performance variability of jobs with average runtime around 45 seconds. Light queries complete in three seconds on average, which is very important for a Data Analyst (with Google BigQuery Training). Meanwhile, Motorola Mobility and their Data Analysts (with Google BigQuery Training) see averaging around 100 seconds (down from almost 200 seconds a year and a half ago).

AtScale is a big data analytics software vendor whose founders experienced the power of first-generation big data technologies at Yahoo! (and this company have Data Analysts with Google BigQuery Training). AtScale architects (Data Analysts with Google BigQuery Training) built what they could not buy when AtScale tired of moving data out of Apache Hadoop into small relational engines.

AtScale (Data Analysts with Google BigQuery Training) added support for BigQuery and Cloud Dataflow.

These are the advantages:

  • Data loading is the process to move data to the Google cloud and load it into BigQuery is simple and scalable, which is done by Data Analysts with Google BigQuery Training.
  • Out-of-the-box performance, which is the BigQuery engine, performs well out-of-the-box with minimal query tuning and no system configuration (which is very important for a Data Analyst with Google BigQuery Training).
  • Impressive concurrency, where the BigQuery’s serverless model on small data sets shows no query degradation (which is used by a Data Analyst with Google BigQuery Training), even at query volumes above 25 concurrent BI users (which is very important for a Data Analyst with Google BigQuery Training).

Separation of compute and state (which is very important for a Data Analyst with Google BigQuery Training) is a relatively new architectural consideration, but one that lets BigQuery and Cloud Dataflow push the boundaries of performance, efficiency, reliability, and scalability. Ultimately, customers benefit from more powerful services and greater competition, and it has a huge impact in the market.

Data Analyst with Google BigQuery Training can use BigQuery for many things, and businesses use faster access to their data to identify and engage with clients who show an intent to convert.

For example, it’s well known that a good time to offer a discount to consumers is just after they’ve shown intent (like adding a product to their cart) but then abandoned the conversion funnel, (and BigQuery can be used for it). Indeed, an offer at that moment can bring back large numbers of consumers who then convert. In a case like this, it’s critical (for a Data Analyst with Google BigQuery Training) to use the freshest data to identify those users in minutes and deploy the right campaign to bring them back.

More frequent updates also help clients recognize and fix issues more quickly, and react to cultural trends in time to join the conversation (which is very important task for a Data Analyst with Google BigQuery Training). BigQuery (used by a Data Analyst with Google BigQuery Training) is an important part of the process. It helps Data Analysts (with Google BigQuery Training) join other datasets from CRM systems, call centers, or offline sales that are not available in Google Analytics today to gain greater context into those clients, issues, or emerging trends.

When streaming data is combined with BigQuery’s robust programmatic and statistical tools, predictive user models can capture a greater understanding of your audience (which is very important for a Data Analyst with Google BigQuery Training). And help Data Analysts with Google BigQuery Training engage those users where and when they’re ready to convert. That means more sales opportunities and better returns on company’s investment and that increases the market.

Those who opt in to streaming Google Analytics data into BigQuery will see data delivered to their selected BigQuery project as fast as every 10 minutes (which is very important for a Data Analyst with Google BigQuery Training).

The new export uses Cloud Streaming Service, which costs a little extra: $0.05 per GB (that is, “a nickel a gig”), which is used by Data Analysts with Google BigQuery Training. If a Data Analyst with Google BigQuery Training doesn’t take any action, the account will continue to run as it does now, and there will be no added cost.

Most data sent directly to Google Analytics is included (which is very important). However, data pulled in from other sources like AdWords and DoubleClick, also referred to as “integration sources”, operate with additional requirements like fraud detection (which is using BigQuery). That means that this data is purposefully delayed for your benefit and therefore exempt from this new streaming functionality (which is very important for a Data Analyst with Google BigQuery Training).

Some companies say will allow users to browse, preview and import that data through a single interface, which means that they are using Google Cloud storage and Google BigQuery (which means that they have Data Analysts with Google BigQuery Training).

This has huge potential for teams that rely on Google-generated data (and for Data Analysts with Google BigQuery Training). For instance, marketing teams using DoubleClick ads data can now use the Cloud Dataprep platform to prepare the result back into BigQuery for analysis, which is used by Data Analysts with Google BigQuery Training.

Google Cloud Dataprep (used by Data Analysts with Google BigQuery Training) has been in private beta for about six months, and has now entered public beta.

Bigdataguys has organized courses to help developers gain a greater understanding of Google BigQuery. This course gives you excellent opportunities in the job market. These classes aim to bring students up to speed on Google BigQuery.

Google BigQuery Training of Bigdataguys offers qualify courses in Google BigQuery. The best way to learn about Google BigQuery is to take a course with us. Google BigQuery training covers the basic theory and practical examples.

CURRICULUM

● Understand the purpose of and use cases for Google BigQuery

● Describe ways in which customers have used Google BigQuery to improve their businesses

Lab: Sign Up for the Free Trial and Create a Project

● Register for the GCP free trial

● Create a project using the Cloud Platform Console

● Describe the components of a BigQuery project

● Identify how BigQuery stores data and list the advantages of the storage model

● Understand the architecture of BigQuery and how queries are processed

● Describe the methods of interacting with BigQuery

Lab: Explore BigQuery Interfaces

● Explore features of the BigQuery web UI

● Learn how to use the BQ shell

● Execute queries using the BigQuery CLI in Cloud Shell

● Describe the purpose of denormalizing data

● Identify the purpose and structure of BigQuery schemas and data types

● Explain the types of actions available in BigQuery jobs

● Understand the purpose of and advantages of BigQuery destinations tables and caching

Lab: BigQuery Components and Jobs

● Explore how data is organized in BigQuery

● Learn about the two types of table schemas

● Learn about jobs, and how to cancel them

● Investigate caching and destination tables

● Describe the methods for ingesting data, transforming data, and storing data using BigQuery

● Explain the function of BigQuery federated queries

Lab 4, Part I: Loading Data into BigQuery and Using Federated Queries

● Load a CSV file into a BigQuery table using the web UI

● Load a JSON file into a BigQuery table using the CLI

● Transform data and join tables using the web UI

● Store query results in a destination table

● Query a destination table using the web UI to confirm your data was transformed and loaded correctly

● Export query results from a destination table to Google Cloud Storage

● Create a federated query that queries data in Cloud Storage

Lab 4, Part II: Exporting App Engine Logs to BigQuery

● Set up Google Cloud Logging to export App Engine log data from the Guestbook application

● Use the BigQuery web UI to query the log data

● Explain the advantages of the BigQuery pricing model

● Use the pricing calculator to calculate storage and query costs

● Identify the quotas that apply to BigQuery projects

Lab: BigQuery Pricing

● Evaluate the size of a query within BigQuery using the BigQuery web UI

● Use the Pricing Calculator and the total size of the query to estimate the query cost

● Examine how changing a query affects query cost

● Explain the differences between BigQuery SQL and ANSI SQL

● Identify the purpose of and use cases for user­defined functions

● Explain the purpose of various BigQuery functions

Lab: BigQuery Clauses and Functions

● Create and run a query using a wildcard function

● Create and run a query using a window function

● Create and run a query using a user­defined function

● Identify the purpose and structure of BigQuery nested, repeated, and nested repeated fields

● Describe the use cases for nested, repeated, and nested repeated fields

Lab: Nested Fields

● Create a BigQuery table using nested data

● Run queries to explore the structure of the nested data

Lab: Repeated Fields

● Create a BigQuery table using repeated data

● Run queries to explore the structure of the repeated data

Lab: Nested Repeated Fields

● Create a BigQuery table using nested repeated data

● Run queries to explore the structure of the nested repeated data

● Explain the impact of the following in query performance: JOIN and GROUP BY, table wildcards, and table decorators

● Identify various best practices for optimizing query performance

Lab: BigQuery Best Practices and Optimization Techniques

● Use denormalization to improve query performance

● Use sub selects to improve the performance of queries with JOIN clauses

● Use destination tables to lower costs when running multiple, similar queries

● Use table decorators and table wild cards to improve query performance and to reduce costs

● Describe how to handle the most common BigQuery errors: request encoding errors, resource errors, and HTTP errors

Lab: Handling Errors

● Correct queries that produce syntax­related error messages

● Correct an error involving the order of a JOIN clause

● Correct an error involving an invalid table name

● Modify queries that exceed resource constraints

  • Describe the purpose of access control lists in BigQuery
  • List and explain the project and data set roles available in BigQuery
  • Apply views for row­level security

Lab: Access Control

  • Manage access to datasets using project­level ACLs

● Manage access to datasets using dataset­level ACLs

Online: $4999
Next Batch: starts from Nov 15th 2017

In Class: $9999
Locations: New York City, D.C., Bay Area
Next Batch: starts from Nov 15th 2017

COURSE HIGHLIGHTS

Skill level: Intermediate
Language: English
Certificate: No
Assessments: Self
Prerequisites: Basic Python programming

SCHEDULE YOUR FREE DEMO

TALK TO US

NEED CUSTOM TRAINING FOR YOUR CORPORATE TEAM?

NEED HELP? MESSAGE US

SOME COURSES YOU MAY LIKE

data science Bootcamp
Deep Learning with Tensor Flow In-Class or Online

Good grounding in basic machine learning. Programming skills in any language (ideally Python/R).

Instructors: John Doe, Lamar George
Duration:
 
50 hours
Lectures:  25

Neural Networks Fundamentals using Tensor Flow as Example Training (In-Class or Online) 

Good grounding in basic machine learning. Programming skills in any language (ideally Python/R).

Instructors: John Doe, Lamar George
Duration:
 
50 hours
Lectures:  25

Deep learning tutorial

Tensor Flow for Image Recognition Bootcamp (In-Class and Online)

Good grounding in basic machine learning. Programming skills in any language (ideally Python/R).

Instructors: John Doe, Lamar George
Duration:
 
50 hours
Lectures:  25

OUR PRODUCTS

SOME OTHER COURSES YOU MAY LIKE

FAQ'S

Advanced Course like Data Analyst Training of Google BigQuery Training Course duration largely depends on trainee requirements, it is always recommended to consult one of our advisors for specific course duration.

We record each LIVE class session you undergo through and we will share the recordings of each session/class.

If you have any queries you can contact our 24/7 dedicated support to raise a ticket. We provide you email support and solution to your queries. If the query is not resolved by email we can arrange for a one-on-one session with our trainers.

You will work on real world projects wherein you can apply your knowledge and skills that you acquired through our training. We have multiple projects that thoroughly test your skills and knowledge of various aspect and components making you perfectly industry-ready.

Our Trainers will provide the Environment/Server Access to the students and we ensure practical real-time experience and training by providing all the utilities required for the in-depth understanding of the course.

Yes. All the training sessions are LIVE Online Streaming using either through WebEx or GoToMeeting, thus promoting one-on-one trainer student Interaction.

The Data Analyst Training of Google BigQuery by BigdataGuys will not only increase your CV potential but will offer you a global exposure with enormous growth potential.

REVIEWS

Artificial Intelligence Bootcamp
90 / 100 Reviewer
{{ reviewsOverall }} / 100 (0 votes) Users
Pros
"AI Training and Placement in 12 weeks" I am C/C++ developer with 3 + years of programming experience. I completed deep learning tensorflow instructor lead online course. This is live instructor lead interactive training of 8 students. The instructor is PhD, interactive,very helpful and knowledgeable. His training approach is very detail oriented and focused on how to apply deep learning in real time. After training was completed, their consulting partners helped me get project placement within 4 weeks with healthcare startup in NYC. I would highly recommend this online boot camp if you are looking to jumpstart your career in AI and have programming background which is mandatory this course. I appreciate the instructors and Bigdataguys's team for helping me upgrade my career into AI and wish them success in their future initiatives.
Cons
I wish they had branches in Texas too and more frequent batches.
Lab Exercises94
Projects88.5
Trainer Quality98
Promptness88.5

COMMENTS

BLOG

INSTRUCTORS

John Doe
Learning Scientist & Master Trainer 
John Doe has been a professional educator
for the past 20 years. He’s taught, tutored,
and coached over 1000 students, and he
holds degrees in Physics and Literature
from Northwestern University. He has
spent the last 4 years studying how
people learn to code and develop applications.

Lamar George
Learning Scientist & Master Trainer 
He has been a professional educator for
the past 20 years. He’s taught, tutored,
and coached over 1000 students, and
he holds degrees in Physics and Literature
from Northwestern University. He has
spentthe last 4 years studying how
people learn to code and develop applications.

Summary
Training | Workshops | Paid Consulting | Bootcamps
Service Type
Training | Workshops | Paid Consulting | Bootcamps
Provider Name
BigDataGuys,
1250 Connecticut Ave, Suite 200,Washington, D.C,20036,
Telephone No.202-897-1944
Area
NYC | D.C | Toronto | Bay Area | Online
Description
This workshop offers Data Analyst for Google Big Query Training | PROGRAMS | TUTORIALS | COURSES | Instructor led boot camps | Email Training@bigdataguys.com