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Essential Functionalities to Guide you While using AWS Glue and PySpark!

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Introduction

In this post, I have penned down AWS Glue and PySpark functionalities which can be helpful when thinking of creating AWS pipeline and writing AWS Glue PySpark scripts.

AWS Glue PySpark

AWS Glue is a fully managed extract, transform, and load (ETL) service to process large amounts of datasets from various sources for analytics and data processing.

While creating the AWS Glue job, you can select between Spark, Spark Streaming, and Python shell. These jobs can run a proposed script generated by AWS Glue, or an existing script that you provide or a new script authored by you. Also, you can select different monitoring options, job execution capacity, timeouts, delayed notification threshold, and non-overridable and overridable parameters.

AWS Glue PySpark

AWS Glue PySpark

Recently AWS recently launched Glue version 2.0 which features 10x faster Spark ETL job start times and reducing the billing duration from a 10-minute minimum to 1-minute minimum.

With AWS Glue you can create development endpoint and configure SageMaker or Zeppelin notebooks to develop and test your Glue ETL scripts.

AWS Glue PySpark - AWS Glue

I create a SageMaker notebook connected to the Dev endpoint to the author and test the ETL scripts. Depending on the language you are comfortable with, you can spin up the notebook.

AWS Glue PySpark -Jupyter Notebook

Now, let’s talk about some specific features and functionalities in AWS Glue and PySpark which can be helpful.

1. Spark DataFrames

Spark DataFrame is a distributed collection of data organized into named columns. It is conceptually equivalent to a table in a relational database. You can create DataFrame from RDD, from file formats like csv, json, parquet.

With SageMaker Sparkmagic(PySpark) Kernel notebook, the Spark session is automatically created.

AWS Glue PySpark -Spark Dataframe

To create DataFrame –

# from CSV files S3_IN = "s3://mybucket/train/training.csv"csv_df = ( spark.read.format("org.apache.spark.csv") .option("header", True) .option("quote", '"') .option("escape", '"') .option("inferSchema", True) .option("ignoreLeadingWhiteSpace", True) .option("ignoreTrailingWhiteSpace", True) .csv(S3_IN, multiLine=False)
)# from PARQUET files S3_PARQUET="s3://mybucket/folder1/dt=2020-08-24-19-28/"df = spark.read.parquet(S3_PARQUET)# from JSON files
df = spark.read.json(S3_JSON)# from multiline JSON file df = spark.read.json(S3_JSON, multiLine=True)

2. GlueContext

GlueContext is the entry point for reading and writing DynamicFrames in AWS Glue. It wraps the Apache SparkSQL SQLContext object providing mechanisms for interacting with the Apache Spark platform.

from awsglue.job import Job
from awsglue.transforms import *
from awsglue.context import GlueContext
from pyspark.context import SparkContext
from awsglue.utils import getResolvedOptions
from awsglue.dynamicframe import DynamicFrameglueContext = GlueContext(SparkContext.getOrCreate())

Glue Context

3. DynamicFrame

AWS Glue DynamicFrames are similar to SparkSQL DataFrames. It represents a distributed collection of data without requiring you to specify a schema. Also, it can be used to read and transform data that contains inconsistent values and types.

DynamicFrame can be created using the following options –

  • create_dynamic_frame_from_rdd — created from an Apache Spark Resilient Distributed Dataset (RDD)
  • create_dynamic_frame_from_catalog — created using a Glue catalog database and table name
  • create_dynamic_frame_from_options — created with the specified connection and format. Example — The connection type, such as Amazon S3, Amazon Redshift, and JDBC

DynamicFrames can be converted to and from DataFrames using .toDF() and fromDF(). Use the following syntax-

#create DynamicFame from S3 parquet files
datasource0 = glueContext.create_dynamic_frame_from_options( connection_type="s3", connection_options = { "paths": [S3_location] }, format="parquet", transformation_ctx="datasource0")#create DynamicFame from glue catalog datasource0 = glueContext.create_dynamic_frame.from_catalog( database = "demo", table_name = "testtable", transformation_ctx = "datasource0")#convert to spark DataFrame df1 = datasource0.toDF()#convert to Glue DynamicFrame
df2 = DynamicFrame.fromDF(df1, glueContext , "df2")

You can read more about this here.

4. AWS Glue Job Bookmark

AWS Glue Job bookmark helps process incremental data when rerunning the job on a scheduled interval, preventing reprocessing of old data.

You can read more about this here. Also, you can read this.

5. Write out data

The DynamicFrame of the transformed dataset can be written out to S3 as non-partitioned (default) or partitioned. “partitionKeys” parameter can be specified in connection_option to write out the data to S3 as partitioned. AWS Glue organizes these datasets in Hive-style partition.

In the following code example, AWS Glue DynamicFrame is partitioned by year, month, day, hour, and written in parquet format in Hive-style partition on to S3.

s3://bucket_name/table_name/year=2020/month=7/day=13/hour=14/part-000–671c.c000.snappy.parquet

S3_location = "s3://bucket_name/table_name"datasink = glueContext.write_dynamic_frame_from_options(
frame= data,
connection_type="s3",
connection_options={ "path": S3_location, "partitionKeys": ["year", "month", "day", "hour"]
},
format="parquet",
transformation_ctx ="datasink")

You can read more about this here.

6. “glueparquet” format option

glueparquet is a performance-optimized Apache parquet writer type for writing DynamicFrames. It computes and modifies the schema dynamically.

datasink = glueContext.write_dynamic_frame_from_options( frame=dynamicframe, connection_type="s3", connection_options={ "path": S3_location, "partitionKeys": ["year", "month", "day", "hour"] }, format="glueparquet", format_options = {"compression": "snappy"}, transformation_ctx ="datasink")

You can read more about this here.

7. S3 Lister and other options for optimizing memory management

AWS Glue provides an optimized mechanism to list files on S3 while reading data into DynamicFrame which can be enabled using additional_options parameter “useS3ListImplementation” to true.

You can read more about this here.

8. Purge S3 path

purge_s3_path is a nice option available to delete files from a specified S3 path recursively based on retention period or other available filters. As an example, suppose you are running AWS Glue job to fully refresh the table per day writing the data to S3 with the naming convention of s3://bucket-name/table-name/dt=<data-time>. Based on the defined retention period using the Glue job itself you can delete the dt=<date-time> s3 folders. Another option is to set the S3 bucket lifecycle policy with the prefix.

#purge locations older than 3 days
print("Attempting to purge S3 path with retention set to 3 days.")
glueContext.purge_s3_path( s3_path=output_loc, options={"retentionPeriod": 72})

You have other options like purge_table, transition_table, and transition_s3_path also available. The transition_table option transitions the storage class of the files stored on Amazon S3 for the specified catalog’s database and table.

You can read more about this here.

9. Relationalize Class

Relationalize class can help flatten nested json outermost level.

You can read more about this here.

10. Unbox Class

The Unbox class helps the unbox string field in DynamicFrame to specified format type(optional).

You can read more about this here.

11. Unnest Class

The Unnest class flattens nested objects to top-level elements in a DynamicFrame.

root
|-- id: string
|-- type: string
|-- content: map
| |-- keyType: string
| |-- valueType: string

With content attribute/column being map Type, we can use the unnest class to unnest each key element.

unnested = UnnestFrame.apply(frame=data_dynamic_dframe)
unnested.printSchema()root
|-- id: string
|-- type: string
|-- content.dateLastUpdated: string
|-- content.creator: string
|-- content.dateCreated: string
|-- content.title: string

12. printSchema()

To print the Spark or Glue DynamicFrame schema in tree format use printSchema().

datasource0.printSchema()root
|-- ID: int
|-- Name: string
|-- Identity: string
|-- Alignment: string
|-- EyeColor: string
|-- HairColor: string
|-- Gender: string
|-- Status: string
|-- Appearances: int
|-- FirstAppearance: choice
| |-- int
| |-- long
| |-- string
|-- Year: int
|-- Universe: string

13. Fields Selection

select_fields can be used to select fields from Glue DynamicFrame.

# From DynamicFramedatasource0.select_fields(["Status","HairColor"]).toDF().distinct().show()

AWS Glue PySpark -Fields Selection

To select fields from Spark Dataframe to use “select” –

# From Dataframedatasource0_df.select(["Status","HairColor"]).distinct().show()

Image for post

14. Timestamp

For instance, the application writes data into DynamoDB and has a last_updated attribute/column. But, DynamoDB does not natively support date/timestamp data type. So, you could either store it as String or Number. In case stored as a number, it’s usually done as epoch time — the number of seconds since 00:00:00 UTC on 1 January 1970. You could see something like “1598331963” which is 2020–08–25T05:06:03+00:00 in ISO 8601.

You can read more about Timestamp here.

How can you convert it to a timestamp?

When you read the data using AWS Glue DynamicFrame and view the schema, it will show it as “long” data type.

root
|-- version: string
|-- item_id: string
|-- status: string
|-- event_type: string
|-- last_updated: long

To convert the last_updated long data type into timestamp data type, you can use the following code-

import pyspark.sql.functions as f
import pyspark.sql.types as tnew_df = ( df .withColumn("last_updated", f.from_unixtime(f.col("last_updated")/1000).cast(t.TimestampType()))
)

15. Temporary View from Spark DataFrame

In case you want to store the Spark DataFrame as a table and query it using spark SQL, you can convert the DataFrame into a temporary view that is available for only that spark session using createOrReplaceTempView.

df = spark.createDataFrame( [ (1, ['a', 'b', 'c'], 90.00), (2, ['x', 'y'], 99.99), ], ['id', 'event', 'score'] )df.printSchema()
root |-- id: long (nullable = true) |-- event: array (nullable = true) | |-- element: string (containsNull = true) |-- score: double (nullable = true)df.createOrReplaceTempView("example")spark.sql("select * from example").show()+---+---------+-----+
| id| event|score|
+---+---------+-----+
| 1|[a, b, c]| 90.0|
| 2| [x, y]|99.99|
+---+---------+-----+

16. Extract element from ArrayType

Suppose from the above example, you want to create a new attribute/column to store only the last event. How would you do it?

You use the element_at function. It returns an element of the array at the given index in extraction if col is an array. Also, it can be used to extract the given key in extraction if col is a map.

import pyspark.sql.functions as element_atnewdf = df.withColumn("last_event", element_at("event", -1))newdf.printSchema()
root |-- id: long (nullable = true) |-- event: array (nullable = true) | |-- element: string (containsNull = true) |-- score: double (nullable = true) |-- last_event: string (nullable = true)newdf.show()
+---+---------+-----+----------+
| id| event|score|last_event|
+---+---------+-----+----------+
| 1|[a, b, c]| 90.0| c|
| 2| [x, y]|99.99| y|
+---+---------+-----+----------+

17. explode

The explode function in PySpark is used to explode array or map columns in rows. For example, let’s try to explode “event” column from the above example-

from pyspark.sql.functions import explodedf1 = df.select(df.id,explode(df.event))df1.printSchema()
root |-- id: long (nullable = true) |-- col: string (nullable = true)df1.show()
+---+---+
| id|col|
+---+---+
| 1| a|
| 1| b|
| 1| c|
| 2| x|
| 2| y|
+---+---+

18. getField

In a Struct type, if you want to get a field by name, you can use “getField”. The following is its syntax-

import pyspark.sql.functions as f
from pyspark.sql import Rowfrom pyspark.sql import Row
df = spark.createDataFrame([Row(attributes=Row(Name='scott', Height=6.0, Hair='black')), Row(attributes=Row(Name='kevin', Height=6.1, Hair='brown'))]
)df.printSchema()
root |-- attributes: struct (nullable = true) | |-- Hair: string (nullable = true) | |-- Height: double (nullable = true) | |-- Name: string (nullable = true)df.show()
+-------------------+
| attributes|
+-------------------+
|[black, 6.0, scott]|
|[brown, 6.1, kevin]|
+-------------------+df1 = (df .withColumn("name", f.col("attributes").getField("Name")) .withColumn("height", f.col("attributes").getField("Height")) .drop("attributes") )df1.show()
+-----+------+
| name|height|
+-----+------+
|scott| 6.0|
|kevin| 5.1|
+-----+------+

19. startswith

In case, you want to find records based on a string match you can use “startswith”.

In the following example I am searching for all records where value for description column starts with “[{“.

import pyspark.sql.functions as fdf.filter(f.col("description").startswith("[{")).show()

20. Extract year, month, day, hour

One of the common use cases is to write the AWS Glue DynamicFrame or Spark DataFrame to S3 in Hive-style partition. To do so you can extract the year, month, day, hour, and use it as partitionkeys to write the DynamicFrame/DataFrame to S3.

import pyspark.sql.functions as fdf2 = (raw_df .withColumn('year', f.year(f.col('last_updated'))) .withColumn('month', f.month(f.col('last_updated'))) .withColumn('day', f.dayofmonth(f.col('last_updated'))) .withColumn('hour', f.hour(f.col('last_updated'))) )

About the Author

Author

Anand Prakash – 5x AWS Certified | 5x Oracle Certified

Avid learner of technology solutions around databases, big-data, machine learning.
Connect on Twitter @anandp86

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Source: https://www.analyticsvidhya.com/blog/2020/08/essential-functionalities-to-guide-you-while-using-aws-glue-and-pyspark/

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Join Hands with Instagram’s New Algorithm to Boost Your Business’s User Engagement

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👉 Most people are not at all happy with the latest 📸 Instagram algorithm. However, if you want to make the most of your business, you should understand how it works. The trick here is you must work with its algorithm and not go against it here; it is easy for your know-how. 👇🔻

🚩This post will guide you on how the 🆕 new Instagram algorithm works and how you can use it for the advantage of your business-

How does the new Instagram algorithm work?

The new 📸 Instagram ♀️ algorithm is a mystery to many users on the platform. It no longer functions at a slow pace as it did in the past. To score high on Instagram today, you need to be extremely diligent as a business owner. The algorithm needs to be fully optimized so that you get success in the platform. So, read and remember the following factors to help you determine how your posts can perform well to boost user engagement and sales revenue for your business with success-

The volume of social engagement you receive -: Note, your posts with the maximum number of shares, views, comments, etc., will rank high in the Instagram feed over others. When your post receives a lot of comments and likes, the Instagram platform gets a signal that it is engaging to users and has high-quality content. It wants more users to see it, and the algorithm on the platform will display it to other users online.

However, here again, there is a catch. It is not only the engagement that Instagram considers now; it is how fast your Instagram post engages the readers in some cases. Trending hashtags on Instagram, for instance, is the best-known example of the above. The volume of engagement you get for your business posts is important; however, how quickly you get this engagement is even more important!

👉▶Tip- You should discover when the best time of the day to post is. In this way, you can schedule posts targeted at that time only. You should post when most users can see it. This increases your chances of boosting engagement faster, and the Instagram algorithm as it knows what somebody likes on Instagram and will take over from here to share it with those users who will ✌ ⚡ like, share and comment on your post.

How long are people looking at your post on Instagram -: If you see the algorithm that Facebook uses, it takes into account how long users look at your post and spend time interacting with its content. Instagram is no different! Its algorithm will take a look at the duration of time that users spend on your post too. This is again, another important factor that Instagram uses to boost posts.

👉▶Tip- Here, you should craft a great 📸 Instagram caption for your post to boost user engagement. If your Instagram caption is engaging, users will want to read it or click on the button that says “more” to spend more time on your post, and in this way, you can boost more engagement with success on  📸 Instagram.

This is why Boomerangs and videos are generally posted in the 📹 video format. This makes them perform well with the Instagram algorithm. Users take more time to watch them toll the end. Another great way for you to make users stay on your posts is to swipe up CTA action for them to view more. This is another great strategy you can use for your business to boost engagement.

When did you post your photo?-: This is another factor that influences how the 📸 Instagram algorithm works for your business is determining the time of the post. It depends on how often you use the 📲 app. In the past, the algorithm used to give you insights into the recent posts; however, if you tend to log on to Instagram just a few times in one week, it will show posts that have been posted recently. You might even get likes from a previous post that you published a few days ago. The target here is to keep users updated on the posts they might have missed due to the fact that they did not log in regularly.

👉 This means that users can see your posts now for longer periods.

The sort of content you post also influences engagement – Think about if 📸 Instagram only focused on content with the optimal engagement; users would only see that content every time they logged into 📸 Instagram. However, this is not the case with the Instagram algorithm.

For instance, the content 👥 users’ genre searches for in the platform also influence the algorithm of how Instagram works. If a user is a fan of sports, says the NBA and sees that genre of content regularly, Instagram will immediately catch on to this and bring the user more similar content related to sports and the NBA. It knows that the user will be interested in such posts and perhaps bring news and updates of the LA Lakers to boost 👤 user engagement and satisfaction.

Accounts that users search for -: Like the above, the accounts that users search for also determines how the 📸  Instagram algorithm works. This is why when users search a specific 🧾 account many times; Instagram will bring more such content from other accounts to that user. This is why they see it often in their Instagram feeds.

From the above, it is evident that if you want to work with the new Instagram algorithm, you must understand how it works and optimize your posts to help it boost your business. In the past, the feed on Instagram was chronological; however, things have changed now.

🟥 So, ensure that your CTA is strong; you use the right hashtags, post at the best time, and make your feed on Instagram as attractive as possible. In this way, you can boost user engagement, lead conversions, sales, and of course, gain a strategic edge in the market as well.

↘ Source: Ron Johnson. Ron is a Marketer. He always shares his tips on trendy marketing tricks. He always implements new tricks in his field.

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Top 10 Big Data trends of 2020

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Top 10 Big Data trends of 2020

By Priya Dialani

During the last few decades, Big Data has become an insightful idea in all the significant technical terms. Additionally, the accessibility of wireless connections and different advances have facilitated the analysis of large data sets. Organizations and huge companies are picking up strength consistently by improving their data analytics and platforms.

2019 was a major year over the big data landscape. In the wake of beginning the year with the Cloudera and Hortonworks merger, we’ve seen huge upticks in Big Data use across the world, with organizations running to embrace the significance of data operations and orchestration to their business success. The big data industry is presently worth $189 Billion, an expansion of $20 Billion more than 2018, and is set to proceed with its rapid growth and reach $247 Billion by 2022.

It’s the ideal opportunity for us to look at Big Data trends for 2020.

Chief Data Officers (CDOs) will be the Center of Attraction

The positions of Data Scientists and Chief Data Officers (CDOs) are modestly new, anyway, the prerequisite for these experts on the work is currently high. As the volume of data continues developing, the requirement for data professionals additionally arrives at a specific limit of business requirements.

CDO is a C-level authority at risk for data availability, integrity, and security in a company. As more businessmen comprehend the noteworthiness of this job, enlisting a CDO is transforming into the norm. The prerequisite for these experts will stay to be in big data trends for quite a long time.

Investment in Big Data Analytics

Analytics gives an upper hand to organizations. Gartner is foreseeing that organizations that aren’t putting intensely in analytics by the end of 2020 may not be ready to go in 2021. (It is expected that private ventures, for example, self-employed handymen, gardeners, and many artists, are excluded from this forecast.)

The real-time speech analytics market has seen its previously sustained adoption cycle beginning in 2019. The idea of customer journey analytics is anticipated to grow consistently, with the objective of improving enterprise productivity and the client experience. Real-time speech analytics and customer journey analytics will increase its popularity in 2020.

Multi-cloud and Hybrid are Setting Deep Roots

As cloud-based advances keep on developing, organizations are progressively liable to want a spot in the cloud. Notwithstanding, the process of moving your data integration and preparation from an on-premises solution to the cloud is more confounded and tedious than most care to concede. Additionally, to relocate huge amounts of existing data, organizations should match up to their data sources and platforms for a little while to months before the shift is complete.

In 2020, we hope to see later adopters arrive at a conclusion of having multi-cloud deployment, bringing the hybrid and multi-cloud philosophy to the front line of data ecosystem strategies.

Actionable Data will Grow

Another development concerning big data trends 2020 recognized to be actionable data for faster processing. This data indicates the missing connection between business prepositions and big data. As it was referred before, big data in itself is futile without assessment since it is unreasonably stunning, multi-organized, and voluminous. As opposed to big data patterns, ordinarily relying upon Hadoop and NoSQL databases to look at data in the clump mode, speedy data mulls over planning continuous streams.

Because of this data stream handling, data can be separated immediately, within a brief period in only a single millisecond. This conveys more value to companies that can make business decisions and start processes all the more immediately when data is cleaned up.

Continuous Intelligence

Continuous Intelligence is a framework that has integrated real-time analytics with business operations. It measures recorded and current data to give decision-making automation or decision-making support. Continuous intelligence uses several technologies such as optimization, business rule management, event stream processing, augmented analytics, and machine learning. It suggests activities dependent on both historical and real-time data.

Gartner predicts more than 50% of new business systems will utilize continuous intelligence by 2022. This move has begun, and numerous companies will fuse continuous intelligence during 2020 to pick up or keep up a serious edge.

Machine Learning will Continue to be in Focus

Being a significant innovation in big data trends 2020, machine learning (ML) is another development expected to affect our future fundamentally. ML is a rapidly developing advancement that used to expand regular activities and business processes

ML projects have gotten the most investments in 2019, stood out from all other AI systems joined. Automated ML tools help in making pieces of knowledge that would be difficult to separate by various methods, even by expert analysts. This big data innovation stack gives faster results and lifts both general productivity and response times.

Abandon Hadoop for Spark and Databricks

Since showing up in the market, Hadoop has been criticized by numerous individuals in the network for its multifaceted nature. Spark and managed Spark solutions like Databricks are the “new and glossy” player and have accordingly been picking up a foothold as data science workers consider them to be as an answer to all that they disdain about Hadoop.

However, running a Spark or Databricks work in data science sandbox and then promoting it into full production will keep on facing challenges. Data engineers will keep on requiring more fit and finish for Spark with regards to enterprise-class data operations and orchestration. Most importantly there are a ton of options to consider between the two platforms, and companies will benefit themselves from that decision for favored abilities and economic worth.

In-Memory Computing

In-memory computing has the additional advantage of helping business clients (counting banks, retailers, and utilities) to identify patterns rapidly and break down huge amounts of data without any problem. The dropping of costs for memory is a major factor in the growing enthusiasm for in-memory computing innovation.

In-memory innovation is utilized to perform complex data analyses in real time. It permits its clients to work with huge data sets with a lot more prominent agility. In 2020, in-memory computing will pick up fame because of the decreases in expenses of memory.

IoT and Big Data

There are such enormous numbers of advancements that expect to change the current business situations in 2020. It is hard to be aware of all that, however, IoT and digital gadgets are required to get a balance in big data trends 2020.

The function of IoT in healthcare can be seen today, likewise, the innovation joining with gig data is pushing companies to get better outcomes. It is expected that 42% of companies that have IoT solutions in progress or IoT creation in progress are expecting to use digitized portables within the following three years.

Digital Transformation Will Be a Key Component

Digital transformation goes together with the Internet of Things (IoT), artificial intelligence (AI), machine learning and big data. With IoT connected devices expected to arrive at a stunning 75 billion devices in 2025 from 26.7 billion presently, it’s easy to see where that big data is originating from. Digital transformation as IoT, IaaS, AI and machine learning is taking care of big data and pushing it to regions inconceivable in mankind’s history.

Source: https://www.fintechnews.org/top-10-big-data-trends-of-2020/

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What are the differences between Data Lake and Data Warehouse?

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Overview

  • Understand the meaning of data lake and data warehouse
  • We will see what are the key differences between Data Warehouse and Data Lake
  • Understand which one is better for the organization

Introduction

From processing to storing, every aspect of data has become important for an organization just due to the sheer volume of data we produce in this era. When it comes to storing big data you might have come across the terms with Data Lake and Data Warehouse. These are the 2 most popular options for storing big data.

Having been in the data industry for a long time, I can vouch for the fact that a data warehouse and data lake are 2 different things. Yet I see many people using them interchangeably. As a data engineer understanding data lake and data warehouse along with its differences and usage are very crucial as then only will you understand if data lake fits your organization or data warehouse?

So in this article, let satiate your curiosity by explaining what data lake and warehousing are and highlight the difference between them.

Table of Contents

  1. What is a Data Lake?
  2. What is a Data Warehouse?
  3. What are the differences between Data Lake and Data Warehouse?
  4. Which one to use?

What is a Data Lake?

A Data Lake is a common repository that is capable to store a huge amount of data without maintaining any specified structure of the data. You can store data whose purpose may or may not yet be defined. Its purposes include- building dashboards, machine learning, or real-time analytics.

data warehouse data lake

Now, when you store a huge amount of data at a single place from multiple sources, it is important that it should be in a usable form. It should have some rules and regulations so as to maintain data security and data accessibility.

Otherwise, only the team who designed the data lake knows how to access a particular type of data. Without proper information, it would be very difficult to distinguish between the data you want and the data you are retrieving. So it is important that your data lake does not turn into a data swamp.

data warehouse data lake

Image Source: here

What is a Data Warehouse?

A Data Warehouse is another database that only stores the pre-processed data. Here the structure of the data is well-defined, optimized for SQL queries, and ready to be used for analytics purposes. Some of the other names of the Data Warehouse are Business Intelligence Solution and Decision Support System.

What are the differences between Data Lake and Data Warehouse?

Data Lake Data Warehouse
Data Storage and Quality The Data Lake captures all types of data like structure, unstructured in their raw format. It contains the data which might be useful in some current use-case and also that is likely to be used in the future. It contains only high-quality data that is already pre-processed and ready to be used by the team.
Purpose The purpose of the Data Lake is not fixed. Sometimes organizations have a future use-case in mind. Its general uses include data discovery, user profiling, and machine learning. The data warehouse has data that has already been designed for some use-case. Its uses include Business Intelligence, Visualizations, and Batch Reporting.
Users Data Scientists use data lakes to find out the patterns and useful information that can help businesses. Business Analysts use data warehouses to create visualizations and reports.
Pricing It is comparatively low-cost storage as we do not give much attention to storing in the structured format. Storing data is a bit costlier and also a time-consuming process.

Which one to use?

We have seen what are the differences between a data lake and a data warehouse. Now, we will see which one should we use?

If your organization deals with healthcare or social media, the data you capture will be mostly unstructured (documents, images). The volume of structured data is very less. So here, the data lake is a good fit as it can handle both types of data and will give more flexibility for analysis.

If your online business is divided into multiple pillars, you obviously want to get summarized dashboards of all of them. The data warehouses will be helpful in this case in making informed decisions. It will maintain the data quality, consistency, and accuracy of the data.

Most of the time organizations use a combination of both. They do the data exploration and analysis over the data lake and move the rich data to the data warehouses for quick and advance reporting.

End Notes

In this article, we have seen the differences between data lake and data warehouse on the basis of data storage, purpose to use, which one to use. Understanding this concept will help the big data engineer choose the right data storage mechanism and thus optimize the cost and processes of the organization.

The following are some additional data engineering resources that I strongly recommend you go through-

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Source: https://www.analyticsvidhya.com/blog/2020/10/what-is-the-difference-between-data-lake-and-data-warehouse/

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