Data Engineer Training Online 2017-11-21T14:55:56+00:00

Data Engineer

Data Engineer Training

Data Engineers, Workday, Artificial Intelligence, Oracle Fusion, Hyperion Courses, Data Engineer

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Data engineer

Data engineering

Objective of this Data Engineer Certification on Google Cloud Platform

The objective of this Data Engineer Certification on Google Cloud Platform is to provide an introduction to Google cloud platform solutions Fundamentals.  This course focuses on fundamental theory. You will receive a full training and Google cloud platform solutions 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 cloud platform solutions in practical situations.

This Data Engineer Certification on Google Cloud Platform is highly demanded by many enterprises.

TRAINING METHODOLOGY

In Class: $4,999
Locations: NEW YORK CITY, D.C, BAY AREA.
Next Session: 25th Nov 2017

Online: $2,499
Next Session: On Demand

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DATA ENGINEER TRAINING COURSE  

Instructor: John Doe, Lamar George

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data engineer

data engineer

Google Cloud Platform

Google Cloud Platform (used by data engineers) is a suite of cloud computing services that are used to run on the same infrastructure that Google uses internally (for its end-user products). Data engineer use a series of modular Google Cloud services including computing, data storage, data analytics and machine learning.

The popular products of Google Cloud Platform (used for Google cloud platform solutions):

  1. Google Compute Engine – IaaS providing virtual machines (which are used by data engineer).
  2. Google App Engine – PaaS for application hosting (which are used by data engineer).
  3. Bigtable – IaaS massively scalable NoSQL database (which are used by data engineer).
  4. BigQuery – SaaS large-scale database analytics (which are used by data engineer).
  5. Google Cloud Functions –FaaS providing serverless functions to be triggered by cloud events (which are used by data engineer).
  6. Google Cloud Datastore – DBaaS providing a document-oriented database.
  7. Cloud Pub/Sub – a service for publishing and subscribing to data streams and messages (which are used by data engineer).
  8. Google Storage – IaaS providing RESTful online file and object storage (which are used by data engineer).

Google decided to join AWS in the per-second billing market. Indeed, Google Cloud started charging by the second for all of its cloudy Virtual Machines (VMs). The company’s Compute Engine, Container Engine, Cloud Dataproc and App Engine all now offer a good payment plan.

Google Cloud has billed persistent disk by the second since the year 2013. Additionally, Google Cloud offered per-second billing for its committed use discounts and GPUs for quite some time now.

Indeed, AWS it is also per-second billing. Furthermore, per-second billing is more than a pissing match between cloud titans. Indeed, clouds don’t always cost less than on-prem data center.

Indeed, serverless computing has also popularized the idea that there are applications for which code may not run for more than a few seconds. Indeed, many data engineer want to do solutions using cloud-native tools, and many others have a reason to still using servers.

Google Cloud launched Google Cloud Dataprep. Indeed, the private beta has received massive adoption and rave reviews from Google Cloud customers and Google Cloud data engineer. Google Cloud data engineer are excited to bring the product into public beta.

Indeed, enterprises want to use cloud solutions (such as Google Cloud Platform, to increase flexibility and lower data center costs). Furthermore, Google is expanding their cloud offering to serve customer needs or data engineer needs. Indeed, Google Cloud observed a bottleneck amongst customers (and data engineer) in attempting to analyze diverse datasets in the cloud. Google Cloud customers (and google cloud engineers) validated the well-known statistic that over 80% of data analytics time is spent in data preparation. Google Cloud knows that by adding a self-service data service to the Google Cloud platform is critical for enterprises’ performing analytics in the cloud. Indeed, Google is collaborating with Trifacta to create Google Cloud Dataprep.

Trifacta is the leader in data preparation for the cloud. Indeed, Google Cloud Dataprep selected and integrated Trifacta’s interface and Photon Compute Framework directly into the Google Cloud Platform. Furthermore, Trifacta is using Google Cloud Dataprep, which means that data engineer automatically experience the same great functionality found in Trifacta.

  • Predictive transformation: Google Cloud Dataprep detects schema, type, distributions, and missing or mismatched values. Indeed, Google Cloud Dataprep uses machine learning to recommend corrective data transformations.
  • Interactive exploration: it is an intuitive user experience.
  • Out-of-box integration with Google Cloud Platform: Users and data engineer can securely access raw data from Google Cloud Storage or BigQuery. Data engineer: upload data into Google Cloud Dataprep, cleaned, prepped, and returned or inserted into BigQuery for further analysis.
  • Structuring of unstructured data: Handles JSON, AVRO, Excel, compressed files, nested arrays, and so on.
  • Fully-managed infrastructure: Google Cloud Dataprep handles IT resource provisioning and management including usage-based billing and quota restrictions

Indeed, Google Cloud Dataprep has native integration with Cloud Dataflow, which is a massively parallel processing engine that Google hosts to ensure efficient processing.

Google Cloud is collaborating with Trifacta. Indeed, Trifacta is the leader in the data preparation market.

Actually, Google Cloud Dataprep and Trifacta use the same interface and functionality, which is easy for data engineer to use both solutions.

Indeed, Google Cloud Dataprep generates consistent transformation logic, metadata, and data lineage, and it still leverages the best of breed engine per environment (Spark or Google Cloud Dataflow).

Indeed, Trifacta ensures that any future data wrangling needs can be met. Furthermore, it doesn’t matter the type of cloud, the combination of clouds, on-prem, or hybrid strategies (an organization chooses to use in the future), Trifacta will interoperate with those computing environments.

Indeed, Trifacta is the best choice for a seamless hybrid data preparation environment.

Google did his entry into India’s fast-growing cloud services market. Additionally, Google is using machine learning and artificial intelligence (AI) to win customers and compete with rivals Amazon Web Services (AWS) and Microsoft Azure in the Cloud market.

Google is trying to sell its service by using tools that are developed specifically for Indian users.

Google is the innovative provider of machine learning and artificial intelligence (AI) solutions. Additionally, Google was really bringing cutting-edge technology. Indeed, Google has already started getting machine learning benefits such as text to speech.

Actually, Google has offerings (that are effective and that are not overrated) in machine learning-based services such as sticker recommendations for messaging.

Hike, which is a messaging start-up, said that they are really satisfied with the incredibly fast performance and with lower cost. Indeed, the BigQuery platform is a cost-effective solution that is going to migrate to Google Cloud.

Indeed, Google is looking to capitalize on cloud-based value-added services for traditional business organizations such as Ashok Leyland, which is a Chennai-based truck and commercial vehicle maker. Actually, Ashok Leyland uses Google Cloud to link its customers with certified mechanics across different road networks through Service Mandi.

Indeed, Google Cloud has invested $30 billion. Indeed, Google Cloud invested too much in infrastructure. In India, Google Cloud has invested heavily in areas such as eco-system building for small, medium and large businesses.

Indeed, Google Cloud is also investing in its G-suite offerings such as Gmail, Docs, Drive, Calendar, and Hangouts for Indian enterprises.

Indeed, Google Cloud will have its India Cloud Region in Mumbai by the end of this year, its fifth such center in the Asia-Pacific region. Indeed, the other Google Cloud centers are in Tokyo, Taiwan, Singapore, and Sydney. The Mumbai Google Cloud unit will also have its first storage facility in the country. Indeed, the Indian government is working on a data protection law, which could force global and local internet firms to store user data within the country’s geographic boundaries.

Google cloud said its pay per minute model for customers would also help it get an edge over other cloud providers. Indeed, the opportunity for cloud services in India is huge.

Google and their Data Engineer launched the beta of Cloud IoT Core, which is the enterprise IoT platform offering.

Google and their Data Engineer had all the essential building blocks for developing and deploying scalable Internet of Things (IoT) solutions in its cloud platform. It is used Cloud IoT to connect the dots among existing services to deliver an end-to-end device management and data processing pipeline.

Indeed, an enterprise IoT PaaS must have a scalable device management layer complemented by a robust data processing pipeline.

Furthermore, the device registry acts as a central repository of all the devices connected to the platform. The device registry contains device metadata (such as serial number, make, model, location, asset id and more). Indeed, the credentials associated with each device are saved in the registry. Furthermore, the registry also stores the digital twin of the device, which contains the last state. Indeed, applications can query the device’s digital twin to get the metadata and latest data.

Using Google Cloud IoT Core, the device registry is provisioned and exposed as an endpoint in a particular region, which can be used to connect the devices for the first time and for sending machine-to-machine (M2M) commands. Indeed, the registry also stores per-device security credentials, which are used for whitelisting and blacklisting devices. Indeed, customers and data engineer can associate a pair of certificates with each device. Data engineer can connect devices to the service, and then the JWT-formatted signature is used for authentication.

Indeed, data engineer use devices to send messages to each other via secure MQTT or REST endpoints. Furthermore, data engineer can send these messages, which are delivered to other GCP services through the Pub/Sub topics.

Indeed, Cloud IoT Core supports industry standard MQTT broker that needs no changes to existing code. Data engineer are familiar with Paho or any other MQTT client library can target Cloud IoT Core without modifying the code.

Data engineer use the MQTT endpoint of the device registry, which acts as the gateway for sending commands and messages among connected devices. After data engineer authenticated it, certain devices ingest high-velocity data for processing. Some data points of the stream are analyzed in real-time and other data points are routed to a data store for analyzing historical trends.

Data engineer use Google Cloud Pub/Sub, which acts as the endpoint for ingesting high-velocity data. Data engineer create a pipeline in Cloud Dataflow for processing the inbound data. Data engineer use Cloud Dataflow to give service support in real-time. Indeed, data engineer use Cloud Dataflow to give service support for batch processing of data. In the case of a Hadoop cluster, data engineer can quickly route data to Cloud Dataproc, GCP’s own Big Data platform based on managed Hadoop and Spark stack. Implementing a data lake is also possible by routing the raw data to Google Cloud Storage. Indeed, Data engineer use BigQuery, which is the most popular data warehouse in the cloud, to aggregate and query the data.

Data engineer use Google Cloud Pub/Sub for data ingestion service in the cloud. Indeed, every inbound message into the Cloud IoT Core enters the Pub/Sub platform through the designated topic. Indeed, the device registry becomes the publisher with multiple services acting as subscribes to the same topic.

Cloud Functions (such as Google’s Functions as a Service (FaaS) offering) can be one of the subscribers to the Pub/Sub topic. Data engineer can write a simple code snippet in Cloud Functions, which can evaluate the data points and invoke another contextual service. Furthermore, this may include republishing the message to another topic of MQTT or invoking a 3rd party web service such as Twilio or SendGrid to push a notification. Indeed, data engineer can also take advantage of Firebase, the backend data store for integrating IoT data with mobile applications.

Data engineer would be able to integrate predictive analytics with the Internet of Things (IoT) through the TensorFlow-based Cloud ML Engine. Data engineer use cloud functions to invoke a web service exposing a Machine Learning model for performing predictive analytics on the sensor data. Furthermore, data engineer and this integration open up interesting scenarios (such as predictive maintenance (PdM), remaining useful life (RUL), and anomaly detection).

Indeed, almost every service in the GCP portfolio can become a subscriber to Pub/Sub to deal with sensor and device data ingested into the platform.

Data engineer use Google’s Cloud IoT Core, which reflects the principles of GCP (lightweight, simple, secure, robust and scalable). Indeed, the key differentiator of the service is the integration of serverless components of the platform (such as Cloud Functions, Dataflow, Dataproc, and BigQuery). Furthermore, the industry standard MQTT broker would help Google in on-boarding existing devices into the platform.

Data engineer and Google Cloud IoT Core will become a viable alternative to existing cloud-based IoT platforms. Furthermore, GCP customers would benefit from the seamless integration with existing services.

Google Cloud announce the acquisition of Bitium, which provides enterprise customers with identity and access management solutions, including single sign-on and provisioning for cloud applications.

Indeed, using the cloud has unlocked new levels of productivity and collaboration for businesses and their partners, employees, customers and data engineer. Using the cloud, there are many things to take into account and that is needed to ensure that the right levels of security and user data access policies are in place.

Google Cloud will gain capabilities to help data engineer deliver on the Cloud Identity vision. Data engineer want a comprehensive solution for identity and access management and SSO that works across their modern cloud and mobile environments. Bitium and data engineer help enterprises to deliver a broad portfolio of app integrations for provisioning and SSO that complements our best in class device management capabilities in the enterprise.

Google Cloud Dataprep is an intelligent and fully-managed cloud service (built in collaboration with Trifacta), which visually explores, cleans and prepares structured and unstructured data for analysis or training machine-learning models.

Indeed, the main Cloud Dataprep features include a visual experience that makes data preparation intuitive and approachable for data engineer that want to modify or enrich their datasets directly. Data engineer use Cloud Dataprep, which runs on serverless infrastructure that handles scalability, performance, availability, and security.

Data engineer use Cloud Dataprep, which has intelligence built-in for understanding and automatically operationalizing your particular usage patterns, making data preparation even faster and less prone to user error.

Merkle Inc. is a performance marketing agency specializing in data-based marketing solutions that help its clients maximize their most profitable customer relationships through a framework it calls Connected CRM. Merkle uses Google Cloud Datastore and Google BigQuery for bringing new data into BigQuery for analysis.

Merkle uses Cloud Dataprep to offer a better solution for rapid data ingestion than other tools and techniques. Indeed, Cloud Dataprep is used to view and understand new datasets.

Venture Development Center (VDC) LLC is an advisory services company that helps its clients define, identify and implement big data use cases that can lead to business transformation and data monetization. Indeed, Cloud Dataprep and BigQuery are key ingredients in its platform for delivering those services.

Actually, the enterprises need Google Cloud Platform and BigQuery. Google Cloud Platform is a platform that was versatile, easy to utilize and provided a migration path as our needs for data review, evaluation, hygiene, interlinking, and analysis advanced.

Cloud Dataprep integrates with other GCP services (e.g., Cloud Storage, Google BigQuery, Cloud Dataflow, Cloud Machine Learning Engine) for easy adoption within your current workflow.

Google Cloud Platform and SAP developed SAP Cloud Platform on GCP (beta), which is an open platform-as-a-service providing unique in-memory database and business application services. Using Google Cloud Platform (GCP), data engineer get global coverage, the benefits of the GCP network backbone for global availability of applications developed on SAP Cloud Platform, the ability to leverage Google Cloud services like BigQuery adjacent to SAP Cloud Platform.

Data engineer can run SAP’s real-time ERP (enterprise resource planning) for digital business.

Google Cloud Platform was the first cloud to offer Skylake processor, which enables data engineer to have early access to Intel’s latest technology.

Google and Puppet are working together to provide enterprises with a better way to build and deploy applications in the cloud (including those that began their lives in private data centers).

Indeed, Google and Puppet approved modules for the infrastructure-as-code system, so data engineer can use the same tools in the cloud that they’re familiar with from deploying apps on-premises. Furthermore, Google has built tools internally to ensure that as users’ cloud services change.

Indeed, Google has provided those modules and a way to automatically generate the modules from their own platform.

Google wants to attract more enterprise customers, and Puppet could Google to bring Amazon Web Services or Microsoft Azure. Furthermore, Puppet has an opportunity to stand out from other developer tool vendors and gain a powerful ally in Google. Furthermore, Puppet is supporting other cloud platforms.

Google-Cloud data engineer have access to many powerful compute instances. Furthermore, it’s possible for users to rent virtual machines with up to 96 virtual CPUs and 624GB of RAM.

These virtual machines are based on Intel’s Skylake Xeon Scalable processors. Indeed, data engineer can purchase three prebuilt virtual machine shapes with 96 processors and varying amounts of memory. Furthermore, users can adjust the amount of memory available using Google’s custom machine types feature.

Furthermore, Microsoft, Amazon, Google, Oracle, IBM, and others are all competing to provide customers with beefy cloud instances for mission-critical and performance-intensive workloads like databases.

Google wants to attract enterprise users. Additionally, Google wants to support SAP HANA, which is a key workload for many enterprises contemplating a cloud migration.

Google Cloud Search is a new tool, which uses machine learning to help organizations find and access information quickly.

Data engineer use Google Cloud Search to search queries in a natural. Additionally, Google wants to make it easy for data engineer to find information in the workplace (using their language and everyday expressions).

Data engineer are using natural language processing (NLP) technology in Cloud Search. Indeed, data engineer can track down information (such as like documents, presentations or meeting details in less time).

For example, if a user is looking for a Google Doc, then he is more likely to remember who shared it with you than the file name. Now, data engineer can use NLP technology, which is an intuitive way to search using the natural language, to find information quickly in Cloud Search.

For example, users or data engineer can ask questions such as “Docs shared by Dan,” “Who’s Blair’s manager?” or “What docs are relevant?”, then the Cloud Search will show answer cards with relevant information.

Indeed, Cloud Search users will now see content from the new Google Sites in their Cloud Search results.

The advantage of Cloud Search is that users and data engineer have access to information quicker, and it can help them make better and faster decisions in the workplace.

Data engineer developed a way to find a GIF that has a caption such as a movie line or song (that don’t have a tag with this caption). Data engineer used optical character recognition (OCR) and Google Cloud Vision to help users find the perfect GIF.

GIPHY engineers had already generated metadata about the collection of GIFs (using Google Cloud Vision). Furthermore, an image recognition tool that is powered by machine learning was used to find the caption in the GIF. Indeed, data engineer use Cloud Vision to perform an optical character recognition (OCR) on the entire GIF library in order to detect text or captions within the image. Furthermore, the OCR results of Google Cloud Vision were so good that data engineer were ready to incorporate the data directly into the search engine. Then data engineer parsed the data. Additionally, data engineer indexed each GIF, and then data engineer updated the search query to leverage the new metadata.

Data engineer used Luigi package to write a batch job that processed the JSON data generated from Google Cloud Vision. Indeed, data engineer used Amazon Web Services (AWS Simple Queue Service) to coordinate data transfer from Google Cloud Vision to documents in the search index. Additionally, GIPHY search is built on top of Elasticsearch. Indeed, Elasticsearch stores GIF documents. Furthermore, the search query returns results based on the data in the Elasticsearch index.

The main problem of this system is that it has to process millions of GIFs quickly. Data engineer had to learn how to optimize the runtime of the code that prepares GIF updates for Elasticsearch. The first iteration of data engineer took more than 80 hours, but then data engineer run it in just eight hours.

Actually, all the data was indexed, and then it was incorporated the text/caption metadata into the query. Actually, this is like a match phrase query, because it looks for words in the caption that appeared in the same order as the words in the search input (which guarantees that a substring of my movie quote is intact in the results). Data engineer also had to decide how to assign weights to the data in order to determine the most relevant results.

Furthermore, data engineer used an internal GIPHY tool called Search UX for searching “where are the turtles,” a quote from “The Office.” data engineer found that the difference between the old query and the new one was dramatic.

Data engineer used a tool that examines the change on a larger scale (by running the old and new queries against a random set of search terms). Indeed, it is useful for ensuring that this change doesn’t make noise to popular searches that already deliver high-quality results.

Indeed, these GIPHY tools indicated a positive change in the search. Furthermore, data engineer launched the updated query as an experiment. Data engineer found good results in the experiments, with an overall increase in click-through rate of 0.5 percent. In the last experiment, data engineer found that this last change affects a very specific type of search, especially longer phrases, and the impact of the change is even more noticeable for queries in this category. For example, data engineer click-through rate (of searching) for the phrase “never give up never surrender” (which is a quote from “Galaxy Quest”) increased 32%, and click-through rate for the phrase “gotta be quicker than that” increased 31%. Furthermore, the quotes from movies and TV shows, we saw improvements for general phrases like “everything will be ok” and “there you go.”  Indeed, data engineer found that the final click-through rate for these queries is almost 100 percent!

Bigdataguys has organized courses to help data engineer (or any person that wants to know more about Google cloud platform solutions) gain a greater understanding of Google cloud platform solutions. This course gives you excellent opportunities in the job market as data engineer. These classes aim to bring data engineer up to speed on Google cloud platform solutions.

This Google cloud platform solutions course of Bigdataguys offers you to know more about Google cloud platform solutions. The best way to learn about Google cloud platform solutions is to take a course with us. Being a data engineer is highly demanded by companies. This course covers the basic theory and practical examples

The average salary for google cloud platform data engineer ranges from approximately $107,214 per year for Solutions Engineer to $133,761 per year for Platform Engineer.

Curriculum

Lecture1.1 Definitions
Lecture1.2 What is Blockchain
Lecture1.3 Private and Public Blockchain
Lecture1.4 How Transaction gets executed
Lecture1.5 Consensus How Conflicts are being Resolved
Lecture1.6 When to use Blockchain
Lecture1.7 Security Why Blockchain is More Secure
Lecture1.8 Attacks on Blockchain
Lecture1.9 Private Blockchain Can I Setup my Own
Lecture1.10 Section Summary

Lecture1.1 Introduction to Big Data
Lecture1.2 The Big Data Pipeline
Lecture1.3 Core Elements of Apache Hadoop
Lecture1.4 The Apache Hadoop Ecosystem
Lecture1.5 Solving Big Data Problems with Apache Hadoop
Lecture1.6 Use Cases

Lecture2.1 Introduction to Developing Hadoop Application
Lecture2.2 Job Execution Framework MapReduce v1 & v2
Lecture2.3 Write a MapReduce Program
Lecture2.4 Use the MapReduce API
Lecture2.5 Managing, monitoring, and testing MapReduce jobs
Lecture2.6 Characterizing and improving MapReduce job performance
Lecture2.7 Working with different data sources in MapReduce
Lecture2.8 Managing multiple MapReduce jobs
Lecture2.9 Using MapReduce streaming

Lecture3.1 Introduction to HBase
Lecture3.2 HBase Data Model
Lecture3.3 HBase Architecture
Lecture3.4 HBase Schema Design
Lecture3.5 Basic Schema Design
Lecture3.6 Design Schemas for Complex Data Structures
Lecture3.7 Use Hive to Query HBase

Lecture4.1 Hive in the Hadoop Ecosystem
Lecture4.2 Use cases of Hive
Lecture4.3 Steps in the data pipeline
Lecture4.4 Create and Load Data
Lecture4.5 Create databases, internal tables, external tables, and partitioned tables
Lecture4.6 Learn about data types and casting in Hive
Lecture4.7 Load data into tables and databases
Lecture4.8 Query and Manipulate Data
Lecture4.9 Query, sort, and filter data
Lecture4.10 Manipulate data with user-defined functions

Lecture5.1 Pig in the Hadoop Ecosystem
Lecture5.2 Use cases of Pig
Lecture5.3 Steps in the data pipeline
Lecture5.4 Extract, Transform, and Load Data
Lecture5.5 Load data into relations
Lecture5.6 Debug Pig scripts
Lecture5.7 Perform simple manipulations
Lecture5.8 Save relations as files
Lecture5.9 Manipulate Data
Lecture5.10 Subset relations
Lecture5.11 Combine relations
Lecture5.12 Use UDFs on relations

Lecture6.1 Introduction to Apache Spark
Lecture6.2 Load and Inspect Data in Apache Spark
Lecture6.3 Build a Simple Apache Spark Application
Lecture6.4 Work with PairRDD
Lecture6.5 Work with DataFrames
Lecture6.6 Monitor Apache Spark Applications
Lecture6.7 Apache Spark Data Pipelines
Lecture6.8 Create an Apache Spark Streaming Application
Lecture6.9 Use Apache Spark GraphX
Lecture6.10 Use Apache Spark MLlib

Data Engineer training
Data Engineer Training
Lecture7.1 Introducing NoSQL
Lecture7.2 Hadoop & NoSQL
Lecture7.3 MongoDB Introduction
Lecture7.4 Introduction to Cassandra
Lecture7.5 Cloud NoSQL Databases
Lecture7.6 Use Cases

Online: $2,499
Next Batch: starts from – On Demand

In Class: $4,999
Locations: New York City, D.C., Bay Area
Next Batch: starts from 25th Nov 2017

Course Duration: Fully Immersive 8 Weeks 8 AM to 4 PM EST

COURSE HIGHLIGHTS

Course Duration : 8 WEEKS
Location: NYC|D.C|Bay Area|Toronto|Online
Certificate: Yes
Assessments: Daily
Prerequisites: Basic Python programming

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FAQ'S

Advanced Course like Data Engineer online training 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 Engineer online training by BigdataGuys will not only increase your CV potential but will offer you a global exposure with enormous growth potential.

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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.

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Training | Workshops | Paid Consulting | Bootcamps
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Service Type
Training | Workshops | Paid Consulting | Bootcamps
Provider Name
BigDataGuys ,
1250 Connecticut Ave, NW,Washington,D.C-20036,
Telephone No.202-897-1944
Area
NYC | D.C | Toronto | Bay Area | Online
Description
The Data Engineering training program is designed to teach Data engineers how to build and operate frameworks to handle the exploding amount of data being collected in today’s top firms.