#frencheducation

[tefcanada]

📚 Preparing for the TEF exams? Here’s a quick breakdown of the sections and their durations for both TEF IRN and TEF Modulaire!

✨ TEF IRN:

Compréhension écrite: 13 questions, 30 mins

Compréhension orale: 17 questions, 10 mins

Expression écrite: 2 subjects, 30 mins

Expression orale: 2 subjects, 10 mins

✨ TEF Modulaire:

Compréhension écrite: 40 questions, 60 mins

Compréhension orale: 40 questions, 60 mins

Expression écrite: 2 subjects, 60 mins

Expression orale: 2 subjects, 15 mins

Get ready to ace your French language goals! 🇫🇷

[saimanoj]

Become a beacon of hope in the field of mental health with our MS in Mental Health Psychology. 🕊️ Explore the causes behind rising mental disturbances and equip yourself to make a difference. At European International University (EIU) – Paris, our program is not just accredited but endorsed by the French Ministry of Education, ensuring excellence in education. Join us in shaping a healthier tomorrow! 💪

MentalWellness #Counseling #EIUParis #FrenchEducation #ASICAccredited #FutureofPsychology

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[anigdhaoverseas]

"Embark on a journey of business excellence with an MBA in France, where tradition meets innovation. 🇫🇷 Elevate your career with an MBA in the heart of Europe. France beckons with world-class education and a rich cultural experience! 🚀📚 #MBAJourney #FrenchEducation"

[adoseofwhitneyy]

first day of class: ✔️ * * * who’s ready to BLOW this semester away? i know for some, this is the LAST semester; how are y’all doing?! 🏫📚 #frenchmajor #frencheducation https://www.instagram.com/p/B7mNAJQDnBnjPBKo5kmRMakO3uj2VT2pDtzUJY0/?igshid=1p8zhimeu056

[mybulle]

DJECO’s take on classic push and pull toys, solid wood vehicles with a storybook look, watch the bird on Otto’s head bounce as it rolls along Sturdy enough to withstand even the most enthusiastic toddler, this friendly leopard has free spinning wheels with a rubber tread that grip, but won’t mar floors Pull along with the 27-inch cord or push around on the floor, the wheels are quiet, resilient and “high performance” All DJECO toys are made from the highest quality materials, are completely non-toxic and meet and exceed all US and European safety standards An excellent gift for toddlers 18 months + . . . . . #djecousa #pulltoy #koala #kidslovekoalas #montessoribaby #babytoys #toysforkids #woodentoyshop #onlinetoystore #shopeonline #miami #florida #palmbeachcounty #mybulle #frencheducation (at Miami, Florida) https://www.instagram.com/p/COeOnoVlfxm/?igshid=1ohrvbw17yio7

[northernstories]

Erin Mcnabb graduated from NMU in December of 2017. She graduated with a bachelors of secondary education, social studies, and french education. Erin is from Cascade, Michigan and says that she loved Northern because of the faculty. “I love how dedicated our faculty are to student success. They will go above and beyond to make sure that their students receive the best instruction and feedback. The history, education, and modern language departments have been like a second home to me the past 4 years and I have received nothing but support from the faculty and staff. I love how many opportunities I was given outside of the classroom to strive in my major.”

[mondlylanguages]

What's your favorite French expression? ⚡️ 💏 

. . . 

#mondly #languages #languagelearning #didyouknow #language #france #french #learnfrench #speakfrench #frenchlesson #frenchonline #frenchforbeginners #frenchstudy #studyfrench #frencheducation #studyinfrance #visitfrance #frenchapp

[primetimestudy]

@ #primetimestudy #studyineurope #studyinfrance #studyoverseas #studyabroad #studygram #study #studytips #studymode #larochellebusinessschool #frencheducation #studyplanner #studyabroad #abroadlife #internationalstudies # https://www.instagram.com/p/Bo3vQ8sH7_m/?utm_source=ig_tumblr_share&igshid=lvmlqjdupqbu

[nitorinfotech-blog]

Data Processing with Apache Spark

Spark has emerged as a favorite for analytics, especially those that can handle massive volumes of data as well as provide high performance compared to any other conventional database engines. Spark SQL allows users to formulate their complex business requirements to Spark by using the familiar language of SQL.

So, in this blog, we will see how you can process data with Apache Spark and what better way to establish the capabilities of Spark than to put it through its paces and use the Hadoop-DS benchmark to compare performance, throughput, and SQL compatibility against SQL Server.

Before we begin, ensure that the following test environment is available:

SQL Server: 32 GB RAM with Windows server 2012 R2

Hadoop Cluster: 2 machines with 8GB RAM Ubuntu flavor

Sample Data:

For the purpose of this demo, we will use AdventureWorks2016DW data.

Following table is used in query with no of records:

We will compare performance of three data processing engines, which are SQL Server, Spark with CSV files as datafiles and Spark with Parquet files as datafiles.

Query:

We will use the following query to process data:

select pc.EnglishProductCategoryName, ps.EnglishProductSubcategoryName, sum(SalesAmount)

from FactInternetSales f

inner join dimProduct p on f.productkey = p.productkey

inner join DimProductSubcategory ps on p.ProductSubcategoryKey = ps.ProductSubcategoryKey

inner join DimProductCategory pc on pc.ProductCategoryKey = ps.ProductCategoryKey

inner join dimcustomer c on c.customerkey = f.customerkey

group by pc.EnglishProductCategoryName, ps.EnglishProductSubcategoryName  

 Let’s measure the performance of each processing engine:

1) SQL Server:

While running query in SQL Server with the 32GB RAM Microsoft 2012 Server, it takes around 2.33 mins to execute and return the data.

Following is the screenshot for the same:

2) Spark with CSV data files:

Now let’s export the same dataset to CSV and move it to HDFS.

Following is the screenshot of HDFS with the CSV file as an input source.

Now that we have the files for the specific input tables moved to HDFS as CSV files, we can start with Spark Shell and create DataFrames for each source file.

Run Following commands for creating SQL Context:

import org.apache.spark.sql.types._

import org.apache.spark.sql.{Row, SQLContext}

val sqlContext = new SQLContext(sc)

Run following command to create Fact Schema :

val factSchema = StructType(Array(

StructField("ProductKey", IntegerType, true),

StructField("OrderDateKey", IntegerType, true),

StructField("DueDateKey", IntegerType, true),

StructField("ShipDateKey", IntegerType, true),

StructField("CustomerKey", IntegerType, true),

StructField("PromotionKey", IntegerType, true),

StructField("CurrencyKey", IntegerType, true),

StructField("SalesTerritoryKey", IntegerType, true),

StructField("SalesOrderNumber", StringType, true),

StructField("SalesOrderLineNumber", IntegerType, true),

StructField("RevisionNumber", IntegerType, true),

StructField("OrderQuantity", IntegerType, true),

StructField("UnitPrice", DoubleType, true),

StructField("ExtendedAmount", DoubleType, true),

StructField("UnitPriceDiscountPct", DoubleType, true),

StructField("DiscountAmount", DoubleType, true),

StructField("ProductStandardCost", DoubleType, true),

StructField("TotalProductCost", DoubleType, true),

StructField("SalesAmount", DoubleType, true),

StructField("TaxAmt", DoubleType, true),

StructField("Freight", DoubleType, true),

StructField("CarrierTrackingNumber", StringType, true),

StructField("CustomerPONumber", StringType, true),

StructField("OrderDate", TimestampType, true),

StructField("DueDate", TimestampType, true),

StructField("ShipDate", TimestampType, true)

));

Run following command to create a DataFrame for Sales with Fact Schema:

val salesCSV = sqlContext.read.format("csv")

.option("header", "false")

.schema(factSchema)

.load("/data/FactSalesNew/part-m-00000")

Run following command to create Customer schema:

val customerSchema = StructType(Array(

StructField("CustomerKey", IntegerType, true),

StructField("GeographyKey", IntegerType, true),

StructField("CustomerAlternateKey", StringType, true),

StructField("Title", StringType, true),

StructField("FirstName", StringType, true),

StructField("MiddleName", StringType, true),

StructField("LastName", StringType, true),

StructField("NameStyle", BooleanType, true),

StructField("BirthDate", TimestampType, true),

StructField("MaritalStatus", StringType, true),

StructField("Suffix", StringType, true),

StructField("Gender", StringType, true),

StructField("EmailAddress", StringType, true),

StructField("YearlyIncome", DoubleType, true),

StructField("TotalChildren", IntegerType, true),

StructField("NumberChildrenAtHome", IntegerType, true),

StructField("EnglishEducation", StringType, true),

StructField("SpanishEducation", StringType, true),

StructField("FrenchEducation", StringType, true),

StructField("EnglishOccupation", StringType, true),

StructField("SpanishOccupation", StringType, true),

StructField("FrenchOccupation", StringType, true),

StructField("HouseOwnerFlag", StringType, true),

StructField("NumberCarsOwned", IntegerType, true),

StructField("AddressLine1", StringType, true),

StructField("AddressLine2", StringType, true),

StructField("Phone", StringType, true),

StructField("DateFirstPurchase", TimestampType, true),

StructField("CommuteDistance", StringType, true)

));

Run following command to create Customer a dataframe with Customer Schema.

 val customer = sqlContext.read.format("csv")

.option("header", "false")

.schema(customerSchema)

.load("/data/dimCustomer/part-m-00000")

 Now create product schema with the following command:

 val productSchema = StructType(Array(

StructField("ProductKey", IntegerType, true),

StructField("ProductAlternateKey", StringType, true),

StructField("ProductSubcategoryKey", IntegerType, true),

StructField("WeightUnitMeasureCode", StringType, true),

StructField("SizeUnitMeasureCode", StringType, true),

StructField("EnglishProductName", StringType, true),

StructField("SpanishProductName", StringType, true),

StructField("FrenchProductName", StringType, true),

StructField("StandardCost", DoubleType, true),

StructField("FinishedGoodsFlag", BooleanType, true),

StructField("Color", StringType, true),

StructField("SafetyStockLevel", IntegerType, true),

StructField("ReorderPoint", IntegerType, true),

StructField("ListPrice", DoubleType, true),

StructField("Size", StringType, true),

StructField("SizeRange", StringType, true),

StructField("Weight", DoubleType, true),

StructField("DaysToManufacture", IntegerType, true),

StructField("ProductLine", StringType, true),

StructField("DealerPrice", DoubleType, true),

StructField("Class", StringType, true),

StructField("Style", StringType, true),

StructField("ModelName", StringType, true),

StructField("LargePhoto", StringType, true),

StructField("EnglishDescription", StringType, true),

StructField("FrenchDescription", StringType, true),

StructField("ChineseDescription", StringType, true),

StructField("ArabicDescription", StringType, true),

StructField("HebrewDescription", StringType, true),

StructField("ThaiDescription", StringType, true),

StructField("GermanDescription", StringType, true),

StructField("JapaneseDescription", StringType, true),

StructField("TurkishDescription", StringType, true),

StructField("StartDate", TimestampType, true),

StructField("EndDate", TimestampType, true),

StructField("Status", StringType, true)

))

 Create product data frame with Product schema.

val product = sqlContext.read.format("csv")

.option("header", "false")

.schema(productSchema)

.load("/data/dimProduct/part-m-00000")

 Now create Product Category schema using following command:

val productCategotySchema = StructType(Array(

StructField("ProductCategoryKey", IntegerType, true),

StructField("ProductCategoryAlternateKey", IntegerType, true),

StructField("EnglishProductCategoryName", StringType, true),

StructField("SpanishProductCategoryName", StringType, true),

StructField("FrenchProductCategoryName", StringType, true)

))

Now create Product Category Data frame with ProductCategory Schema:

val productCategory = sqlContext.read.format("csv")

.option("header", "false")

.schema(productCategotySchema)

.load("/data/dimProductCategory/part-m-00000")

 Now create Product Sub Category schema using following command:

val productSubCategotySchema = StructType(Array(

StructField("ProductSubcategoryKey", IntegerType, true),

StructField("ProductSubcategoryAlternateKey", IntegerType, true),

StructField("EnglishProductSubcategoryName", StringType, true),

StructField("SpanishProductSubcategoryName", StringType, true),

StructField("FrenchProductSubcategoryName", StringType, true),

StructField("ProductCategoryKey", IntegerType, true)

))

And create productsubcategory data frame using below command:

val productSubCategory = sqlContext.read.format("csv")

.option("header", "false")

.schema(productSubCategotySchema)

.load("/data/dimProductSubCategory/part-m-00000")

 Now create temporary views of each data frame that we have created so far:

sales.createOrReplaceTempView("salesV")

customer.createOrReplaceTempView("customerV")

product.createOrReplaceTempView("productV")

productCategory.createOrReplaceTempView("productCategoryV")

productSubCategory.createOrReplaceTempView("productSubCategoryV")

And Run the same query which we ran in SQL Server:

Val df_1=spark.sql("""select pc.EnglishProductCategoryName, ps.EnglishProductSubcategoryName, sum(SalesAmount)

from salesV f

inner join productV p on f.productkey = p.productkey

inner join productSubCategoryV ps on p.ProductSubcategoryKey = ps.ProductSubcategoryKey

inner join productCategoryV pc on pc.ProductCategoryKey = ps.ProductCategoryKey

inner join customerV c on c.customerkey = f.customerkey

group by pc.EnglishProductCategoryName, ps.EnglishProductSubcategoryName """)

df_1.show()

It took around 3 mins to execute the result set.

3)Spark with Parquet file for Fact Table:

Now, let’s convert FactInternetSaleNew file to parquet file and save to hdfs using the following command:

salesCSV.write.format("parquet").save("sales_parquet")

Create dataframe on top of Parquet file using below command:

val sales = sqlContext.read.parquet("/user/nituser/sales.parquet")

And create temp view using sales data frame:

sales.createOrReplaceTempView("salesV")

Now, we will run the same query which we used in step 2:

val df_1=spark.sql("""select pc.EnglishProductCategoryName, ps.EnglishProductSubcategoryName, sum(SalesAmount)

from salesV f

inner join productV p on f.productkey = p.productkey

inner join productSubCategoryV ps on p.ProductSubcategoryKey = ps.ProductSubcategoryKey

inner join productCategoryV pc on pc.ProductCategoryKey = ps.ProductCategoryKey

inner join customerV c on c.customerkey = f.customerkey

group by pc.EnglishProductCategoryName, ps.EnglishProductSubcategoryName """)

 It will return the same result set in less than 20 secs.

We can conclude by stating that Spark with commodity hardware performs very similar to the high-end server of SQL Server. However, Spark outshines other engines when it deals with column-oriented efficient and compressed storage format.

So, we need to decide the specifications for the processing engine and storage based on business requirements, while also understanding how we can boost the power of such a highly efficient processing engine and get the required performance.

Reach out to us at Nitor Infotech know more about Apache Spark and how you can utilize it to accelerate your business and make advanced analytics more innovative.

[polyglotstudyabroad]

French Teacher Training Course.

Become A Qualified French Teacher.

Start From Scratch Or Improve Your Existing Qualifications.

Call POLYGLOT French Classes.

9511532956.

#french #frenchteachertrainingcourse #qualifiedfrenchteachers #certifiedfrenchteachers #qualifiedfrenchteachers #frencheducation #frenchteaching #teachfrench #frenchfaculty #frenchteachingjobs #teachfrenchinschools #frenchschoolteacher #schoolfrench #frenchinschool #learnfrenchonline #frenchcourse #assam #guwahati #jorhat #dibrugarh #tinsukia #tezpur #shillong #sibsagar

#dimapur #jaipur #jodhpur #alwar #udaipur #delf #dalf

[jculture-en]

French gastronomes acquire taste for Japan's sake

#Wine #JapaneseWine [The Straits Times]… both in wine-growing regions. Mr Takuma Inagawa, a Frencheducated Japanese brewer, has a different idea. He wants to start production in an old warehouse in Fresnes, a town just south of Paris.

[gistwrld]

List of Courses offered by Usman Danfodio University(UDUSOK) AgricultureArabic StudiesBiology EducationCommon and Islamic LawEconomicsEconomics EducationEducation and ArabicEducation and ChemistryEducation and English LanguageEducation and FrenchEducation and... [[ click on the topic to continue reading and see more related news]]

[newsnaijaschool]

via Newsnaijaschool -Nigerian Schoolnews And Scholarships AgricultureArabic StudiesBiology EducationCommon and Islamic LawEconomicsEconomics EducationEducation and ArabicEducation and ChemistryEducation and English LanguageEducation and FrenchEducation and... [[ click on the topic to continue reading and see more related news]]

[amakvitaa]

FCE Abeokuta Degree Post-UTME 2017: Cut-off mark, Screening And Registration Details

This is to invite candidates who sat for 2017 UTME Examination and scored 180 and above, to apply for full-time Degree Programmes at the Federal College of Education, Abeokuta, in affiliation with the university of Ibadan, in the under listed programmes: EDUCATION/CHRISTIAN RELIGIOUS STUDIESEDUCATION/ENGLISHEDUCATION/FRENCHEDUCATION/BIOLOGYEDUCATION/CHEMISTRYEDUCATION/ISLAMIC STUDIESHUMAN…

[punkecodavid]

Could the French hold the key of being able to produce a society full of leaders?... #leadership #frencheducation #education #oureducationsystemfailsus #freethinker #freethyneighbor #organizing #activism #sundaymorningthoughts #revelation #revolution #freedomfromfear #compassion #philosophy #psychology #modelcitizens #changeyourmindset #organizinggoals #activistgoals #votingforthelesserevil #firstpastthepostsucks https://www.instagram.com/p/Boo-5GmHyz9/?utm_source=ig_tumblr_share&igshid=1or6y3saz2jjc

[segoevia]

Parcours L’Italie à Paris; deux pays un même héritage. Parcours du 12/11/2015

Collège de France