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Overview of Spark Components

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Top Components of Spark

Currently, we have 6 components in Spark Ecosystem: Spark Core, Spark SQL, Spark Streaming, Spark MLlib, Spark GraphX, and SparkR. Let’s see what each of these components do.

1. Spark Core

As the name suggests, Spark Core is the core unit of a Spark process. It handles task scheduling, fault recovery, memory management, input-output operations, etc. Think of it as something similar to a CPU to a computer. It supports programming languages like Java, Scala, Python, and R and provides APIs for respective languages using which you can build your ETL job or do analytics. All the other Spark components have their APIs built on Spark Core. Spark can handle any workload because of its parallel processing capabilities and in-memory computation.

Spark Core comes with a special kind of data structure called RDD (Resilient Distributed Dataset) which distributes the data across all the nodes within a cluster. RDDs work on a Lazy evaluation paradigm where the computation is memorized and only executed when necessary. This helps in optimizing the process by only computing the necessary objects.

2. Spark SQL

If you have worked with Databases, you understand the importance of SQL. Wouldn’t it be extremely overwhelming if the same SQL code works N times faster, even on a larger dataset? Spark SQL helps you manipulate data on Spark using SQL. It supports JDBC and ODBC connections that connect Java objects and existing databases, data warehouses, and business intelligence tools. Spark incorporates something called Dataframes, which are structured collections of data in the form of columns and rows.

Spark allows you to work on this data with SQL. Dataframes are equivalent to relational tables, and they can be constructed from any external databases, structured files, or existing RDDs. Dataframes have all the features of RDD, such as immutable, resilient, and in-memory, but with the extra feature of being structured and easy to work with. Dataframe API is also available in Scala, Python, R, and Java.

3. Spark Streaming

Streaming is Netflix, Pinterest, and Uber. Apache Kafka can integrate with Spark Streaming, allowing for the decoupling and buffering of input streams. Spark Streaming algorithms process real-time streams using Kafka as the central hub.

4. Spark MLLib

Spark’s major attraction is scaling up the computation massively, and this feature is the most important requirement for any Machine Learning Project. Spark MLLib is Spark’s machine learning component, which contains Machine Learning algorithms such as classification, regression, clustering, and collaborative filtering. It also offers a place for feature extraction, dimensionality reduction, transformation, etc.

You can also save and run your models on larger datasets without worrying about sizing issues. It also contains utilities for linear algebra, statistics, and data handling. Because of Spark’s in-memory processing, fault tolerance, scalability, and ease of programming, with the help of this library, you can run iterative ML algorithms easily.

5. GraphX

It finds the distance between two locations and gives an optimal route suggestion. Another example can be Facebook friends’ suggestions. GraphX works with both graphs and computations. Spark offers a range of graph algorithms like page rank, connected components, label propagation, SVD++, strongly connected components, and triangle count.

6. SparkR

More than 10,000 packages are available for different purposes in R, making it the most widely used statistical language. It uses data frames API, which makes it convenient to work with and provides powerful visualizations for data scientists to analyze their data thoroughly. R does not support parallel processing and limits itself to the memory available in a single machine. This is where SparkR comes into the picture.

Spark developed a package known as SparkR, which solves the scalability issue of R. It is based on distributed data frames and also provides the same syntax as R. Spark’s distributed Processing engine and R’s unparalleled interactivity, packages; visualization combines to give Data Scientists what they want for their analyses.


Since Spark is a general-purpose framework, it finds itself in many applications. Spark is extensively used in most big data applications because of its performance and reliability. The developers update all these components of Spark with new features in every new release, making our lives easier.

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Learn How To Create A Spark Dataset With Examples?

Introduction to Spark Dataset

Spark Dataset is one of the basic data structures by SparkSQL. It helps in storing the intermediate data for spark data processing. Spark dataset with row type is very similar to Data frames that work as a tabular form on the Resilient distributed dataset(RDD). The Datasets in Spark are known for their specific features such as type-safety, immutability, schemas, performance optimization, lazy evaluation, Serialization, and Garbage Collection. The Datasets are supported through Scala and Java programming APIs. Spark’s dataset supports both compile-time safety and optimizations, making it a preferred choice for implementation in the spark framework.

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Why do we need Spark Dataset?

RDD is the core of Spark. Inspired by SQL and to make things easier, Dataframe was created on top of RDD. Dataframe is equivalent to a table in a relational database or a DataFrame in Python.

RDD provides compile-time type safety, but there is an absence of automatic optimization in RDD.

Dataframe provides automatic optimization, but it lacks compile-time type safety.

Dataset is added as an extension of the Dataframe. Dataset combines both RDD features (i.e. compile-time type safety ) and Dataframe (i.e. Spark SQL automatic optimization ).

As Dataset has compile-time safety, it is only supported in a compiled language( Java & Scala ) but not in an interpreted language(R & Python). But Spark Dataframe API is available in all four languages( Java, Scala, Python & R ) supported by Spark.

Language supported by Spark. Dataframe API Dataset API

Compiled Language (Java & Scala) YES YES

Interpreted Language (R & Python) YES NO

How to Create a Spark Dataset?

There are multiple ways of creating a Dataset based on the use cases.

1. First Create SparkSession


To create a dataset using basic data structure like Range, Sequence, List, etc.:

To create a dataset using the sequence of case classes by calling the .toDS() method :

To create dataset from RDD using .toDS():

To create the dataset from Dataframe using Case Class:

To create the dataset from Dataframe using Tuples :

2. Operations on Spark Dataset

1. Word Count Example

2. Convert Spark Dataset to Dataframe

We can also convert Spark Dataset to Datafame and utilize Dataframe APIs as below :

Features of Spark Dataset

1. Type Safety: Dataset provides compile-time type safety. It means that the application’s syntax and analysis errors will be checked at compile time before it runs.

2. Immutability: Dataset is also immutable like RDD and Dataframe. It means we can not change the created Dataset. Every time a new dataset is created when any transformation is applied to the dataset.

3. Schema: Dataset is an in-memory tabular structure that has rows and named columns.

4. Performance and Optimization: Like Dataframe, the Dataset also uses Catalyst Optimization to generate an optimized logical and physical query plan. 

5. Programming language: The dataset api is only present in Java and Scala, which are compiled languages but not in Python, which is an interpreted language.

6. Lazy Evaluation: Like RDD and Dataframe, the Dataset also performs the lazy evaluation. It means the computation happens only when action is performed. Spark makes only plans during the transformation phase.

7. Serialization and Garbage Collection: The spark dataset does not use standard serializers(Kryo or Java serialization). Instead, it uses Tungsten’s fast in-memory encoders, which understand the internal structure of the data and can efficiently transform objects into internal binary storage. It uses off-heap data serialization using a Tungsten encoder, and hence there is no need for garbage collection.


Dataset is the best of both RDD and Dataframe. RDD provides compile-time type safety, but there is an absence of automatic optimization. Dataframe provides automatic optimization, but it lacks compile-time type safety. Dataset provides both compile-time type safety as well as automatic optimization. Hence, the dataset is the best choice for Spark developers using Java or Scala.

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Top 15 Components Of 8085 Architecture

Introduction to 8085 Architecture

Eighty-eighty-five is an 8-bit microprocessor created in 1971 by Intel. It requires less circuit and makes the computer system to be simpler and easy to be built. It uses 5-volt power supply and has depletion mode transistors. Hence, 8085 could be compared with 8080 derived CPU. Hence these can be used in the systems with CP/M operating system. It has a DIP package with 40 pins. There is a data bus in the processor to fully utilize the functions of pins. There is built in serial I/O and 5 interrupts so that 8085 has long life similar to the controller used.

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Architecture of 8085:

Components of 8085 Architecture

1. It consists of timing and control unit, accumulator, arithmetic and logic unit, general purpose register, program counter, stack pointer, temporary register, flag register, instruction register and decoder, controls, address buffer and address and data bus.

2. The timing and control unit provides proper signals to the microprocessor to perform functions in the system. We have control signals, status signals, DMA signals and RESET signals. This also controls the internal and external circuits in the system.

3. The accumulator is a register that performs all the arithmetic and logic operations in 8-bit processor. It connects the data bus and ALU unit of the processor.

4. ALU performs all the operations that includes arithmetic and logic operations such as addition, subtraction, multiplication, division, and the logical operations in the system.

5. General purpose registers used in the processor include B, C, D, E, H and L registers. Each register holds the data and also these could be made to work in pairs. Hence these can hold 16-bit data in the processor.

7. Stack pointer works like a stack with a 16-bit register. It performs push or pop operations and is either incremented or decremented by 2 in the register.

8. Temporary data of the operations in ALU is handled in a temporary register which is 8-bit.

9. The 8-bit register with 1-bit flip-flops is called Flag register. There are 5 flip-flops and it holds logic data from the accumulator register. The logic data can be 0 or 1. The five flip-flops are Sign, Zero, Auxiliary Carry, Parity and Carry.

10. Instruction register and decoder is also an 8-bit register where instructions are stored after taking it from the memory. Decoder encrypts the instruction stored in the register.

11. The signal is given to the microprocessor through the timing and control unit to do the operations. There are different time and control signals to perform the operations. They are control signals, status signals, DMA signals, and RESET signals.

13. Serial data communication is controlled using serial input data and serial output data.

14. Stack pointer and program counter load the data into the address buffer and data buffer so that it communicates with the CPU. The chips are connected here and CPU transfers data via these chips to the data buses.

15. Data to be stored is saved in the data bus and it transfers data to different address services.

Features of 8085 Architecture

Any 8-bit data could be processed, accepted, or provided in the microprocessor simultaneously. The power supply is a single 5-volt supply and operates on a 50% duty cycle.

The clock generator in the processor is internal that needs a tuned circuit, either LC or RC or crystal. The frequency is divided by 2 so that the clock signal is generated to synchronize external devices of the system.

3 MHz frequency can be used to operate the processor. The maximum frequency in which 8085 operates is 5 MHz

16 address lines are provided in the processor so that it can access 64 Kbytes of memory in the system. Also, 8 bit I/O addresses are provided to access 256 I/O ports.

The address bus and data bus in the processor is multiplexed so that the number of external pins can be reduced. External hardware is needed to separate the address and data lines in the processor. 74 instructions are supported in the processor with different address modes. The address modes are immediate, register, direct, indirect, and implied modes.


The general-purpose electronic processing devices in the system to execute various tasks are called the microprocessors. All the logic and arithmetic operations are performed here and the results are stored in the registers. This helps the CPU to fetch information whenever needed in the system.

Data could be fetched and moved easily to various locations with the help of registers in the microprocessor.

Operands are delivered easily from the microprocessor and this is easy to do than to restore the operands from the memory. Program variables are stored easily in the registers and hence developers prefer to work with processors in the system.

Serial communication is provided with serial control and hardware interrupts are available to deliver urgent requests. It handles the interruptions in a skilled manner so that the process is kept on hold until the urgent requests are fulfilled. Control signals are available so that bus cycles are controlled. This rule out the chance of an external bus controller.

The system bus is shared with Direct Memory Access to transfer huge data from device to memory or vice versa.

Trainer kits are provided in the institutions to learn about microprocessors so that complete documentation is provided to the students regarding the microprocessors. Also, simulators are available for the execution of the codes in the graphical interface. Assembly language programming is added in the microprocessor course so that it helps the students.

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Top 10 Components Of Iot Architecture In 2023

In this article, we aim to examine the concept of IoT architecture, explain the difference between IoT ecosystem and IoT architecture, demonstrate its ten different components, and finally provide a real-life example for contextualization.

What is IoT architecture?

IoT architecture comprises several IoT building blocks connected to ensure that sensor-generated data is collected, transferred, stored, and processed in order for the actuators to perform their designated tasks.

What is the difference between IoT ecosystem and IoT architecture?

IoT ecosystem is the encompassing term attributed to the five general components of devices, communication protocols, the cloud, monitoring, and the end-user in the IoT system.

IoT architecture is the breakdown of the inner workings of these building blocks to make the ecosystem function.

What are the different elements of IoT architecture?

For the sake of brevity, we will only explore the ten most important parts of an IoT architecture.

1- Devices

IoT devices are equipped with sensors that gather the data, which will be transferred over a network. The sensors do not necessarily need to be physically attached to the equipment. In some instances, they are remotely positioned to gather data about the closest environment to the IoT device. Some examples of IoT devices include:

Temperature detectors

Smoke detectors

Cameras and CCTVs

2- Actuators

Actuators are devices that produce motions with the aim of carrying out preprogrammed tasks, for example:

Smart lights turning on or off

Smart locks opening or closing

Thermostat increasing or decreasing the temperature

3- Gateways 4- Cloud gateways 5- Data lake

A data lake is a data storage space that stores all sorts of structured and non-structured data such as images, videos, and audio, generated by IoT devices, which will then be filtered and cleaned to be sent to a data warehouse for further use.

6- Data warehouse

For meaningful insight, data should be extracted from the data lake to the data warehouse, either manually, or by using data warehouse automation tools. A data warehouse contains cleaned, filtered, and mostly structured information, which is all destined for further use.

7- Data analytics

Data analytics is the practice of finding trends and patterns within a data warehouse in order to gain actionable insights and make data-driven decisions about business processes. After having been laid out and visualized, data and IoT analytics tools help identify inefficiencies and work out ways to improve the IoT ecosystem.

8- Control applications

Previously, we mentioned how actuators make “actions” happen. Control applications are a medium which, through them, it’s possible to send out the relevant commands and alerts which will make actuators function. An example of a control application could be soil sensors signaling a dryness in the lawns, and consequently, the actuators turning on the sprinkles to start irrigation.

9- User applications

They are software components (e.g. smartphone apps) of an IoT system that allow users to control the functioning of the IoT network. User applications allow the user to send commands, turn the device on or off, or access other features.

10- Machine learning

Machine learning, if available, gives the opportunity to create more precise and efficient models for control applications. ML models pick up on patterns in order to predict future outcomes, processes, and behavior by making use of historical data that’s accumulated in the data warehouse. Once the applicability and efficiency of the new models are tested and approved by data analysts, new models are adopted.

What is a real-life example IoT architecture?

The sensors take relevant data, such as daylight or people’s movement. The lamps on the other end, are equipped with actuators to switch the light on and off. The data lake stores these raw data coming from the sensors, while a data warehouse houses the inhabitants’ behavior on various days of the week, energy costs, and more. All these data, through field and cloud gateways, are transferred to computing databases (on-premise or cloud).

The users have access to the user application through an app. The app allows them to see which lights are on and off, or it gives them the ability to pass on commands to the control applications. If there is a gap in algorithms, such as when the system mistakenly switches off the lights and the user has to switch it on manually, data analytics can help address these problems at its core.

When daylights get lower than the established threshold, it’s the control applications commanding the actuators to turn the lights on. At other times, if the lights are on power-saving mode and would only be turned on if a user walks past the lawn, it’s the cloud that receives the data of a passerby walking and after identification, alerts the actuators to turn the lights on. This makes sure that false alarms are detected and the power is conserved.

But the control application does not only function with already-established commands. By leveraging machine learning, algorithms would learn more about usage patterns and customize the functionality accordingly. For example, if the inhabitants leave home at 7 am and come back at 5 pm, after some time, the lights would turn off and on in between this interval autonomously. These smart adjustments would, furthermore, reduce the need for human intervention and make for seamless continuity.

For more on the internet of things

To learn more about the technical side of internet of things, read:

Finally, If you believe your business will benefit from an IoT solution, feel free to check our data-driven hub of IoT solutions and tools.

And we can guide you through the process:

He primarily writes about RPA and process automation, MSPs, Ordinal Inscriptions, IoT, and to jazz it up a bit, sometimes FinTech.





The Most Amazing Science Images Of The Week, February 6

Max Read over at Gawker turned us on to this amazing Shutterstock series, mysteriously titled “Cyber Woman With a Corn.” What could you use this photo to illustrate? What couldn’t you use it for? Read more at Gawker

There are lots of amazing images in this week’s roundup; there’s the likely discovery of a massive former ocean on Mars, there’s a purple squirrel, there’s an incredible augmented reality project, and lots more. But we can’t stop looking at–and thinking about–the noble Cyber Woman With A Corn.

Sticky Horses

The purpose of the zebra’s striped coat is a deep mystery to biologists. But according to a new study, they evolved that way to confuse and keep away a certain type of blood-sucking fly. The test involved life-sized sticky horse-models. Read more here.

Ancient Cluster

This deep sky object, known as NGC 6572, is over 10 billion years old–one of the most ancient collection of stars ever seen. In fact, it’s more than twice as old as our own solar system. Read more at NASA.

LEGO’s Offices

The PopSci offices are pretty cool; there are usually robots and all kinds of gadgets around to play with. But these LEGO offices look amazing, and we’ve been in a LEGO-friendly mood ever since our own Corinne Iozzio began her LEGO master training. Read more about the offices over at FastCoDesign.

Get the Ball!

Seth Casteel’s photographs of underwater dogs collected here (his site appears to be down due to excessive traffic) are amazing. I don’t even like dogs, really, but look at how happy and determined they are! I hope they get the ball.

Cyber Woman With Corn

Max Read over at Gawker turned us on to this amazing Shutterstock series, mysteriously titled “Cyber Woman With a Corn.” What could you use this photo to illustrate? What couldn’t you use it for? Read more at Gawker.

Wolf Face


Squirrel Purple

In a small town in Pennsylvania, a couple found a purple squirrel. chúng tôi of all places, found the story, and is investigating to their full abilities. Make sure to read their coverage to see what their senior meteorologists think of this purple squirrel.

“Like a Fat DeLorean”

The new Tesla Model X is a crossover, based on the same platform as the Model S, which is not out yet. It’ll be a plug-in electric vehicle, and yeah, it has gullwing doors. Read more over at Jalopnik.

Martian Ocean

Says the ESA: “New results from the MARSIS radar on Mars Express give strong evidence for a former ocean of Mars.” It’d be a massive ocean, covering a major part of the northern hemisphere of the planet. Read more here.

Learn Top 23 Useful Hadoop Commands

Introduction of Hadoop Commands

Hadoop commands are mainly used to execute several operations. HDFS operations and supervise the file available in the HDFS cluster. Hadoop HDFS is a distributed file system that provides redundant storage for large-sized files to be stored. It is used to store petabyte files in the terabyte range. HDFS is the primary or main component of this ecosystem that is responsible for storing large data sets of structured or unstructured data across various nodes and thus maintaining the metadata in the form of log files. All the commands are executed by the bin shell scripts.

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HDFS Commands

Here we discussed various HDFS commands which are used for HDFS File Operations.





copy from local






move from local

move to local










append to file



1. version

That command is used to check the Hadoop version.


hdfs dfs version

2. mkdir

This Hadoop command is used to make new directories and takes the URI path as parameters


3. ls

This Hadoop Command is used to displays the list of the contents of a particular directory given by the user. It also contents name, permission, size and owner and last edit date.


hdfs dfs -ls /usr/local/firstdir

4. put

This Hadoop Command is used to copies the content from the local file system to the other location within DFS.


hdfs dfs -put  source_dir   destination_dir

5. copyFromLocal

This Hadoop command is the same as put command but here one difference is here like in case this command source directory is restricted to local file reference.


hdfs dfs -copyFromLocal  local_src  destination_dir

6. get

This Hadoop Command fetches all files that match the src dir which is entered by the user in HDFS and generates a copy of them in the local file system.


hdfs dfs -get  source_dir  local_dir?

7. copyTOLocal

This Hadoop Command is using the same as getting command but one difference is that in this the destination is limited to a local file path.


hdfs -dfs  -copyToLocal  src_dir  local_dir

8. cat

This Hadoop Command displays the content of the file name on the console.


hdfs dfs -cat  dir_path

9. mv

This Hadoop Command moves the file and directory one location to another location within hdfs.


hdfs fs -mv source_dir_filename  destination_dir

10. cp

This Hadoop command copies the file and directory one location to other locations within hdfs.


hdfs fs -cp source_dir_filename  destination_dir

11. moveFromLocal

It copies content from the local file system to a destination within HDFS but the copy is a success then deletes content from the local file system.


12. move to local

This Hadoop command runs as -get commands but one difference is that when the copy operation is a success then delete the file from HDFS location.


move to local source_dir  local_dir

13. tail

It displays 1 KB content on the console of the file.


hdfs dfs -tail file_path

14. rm

It removes files and directory from the specified path.


hdfs dfs -rm dir_name

15. expunge

This is used to empty the trash.


hdfs dfs -expunge

16. chown

It used to change the owner of files. We can also use it by -R for recursively.


hdfs dfs -chown  owner_name  dir_name

17. chgrp

This is used to change the group of files. We can also use it by -R for recursively.


18. du

This displays disk usage for all files available in the present directory with the path given by the user and prints information in bytes format.


hdfs dfs -du  dir_name

19. df

This Hadoop Command displays free space.


hdfs dfs -df -h

20. touchz

This is used to create a file with a path and includes current time as timestamp and is also the path is exiting if exits then fail to create process.


hdfs dfs -touchz dir_name

21. appendToFile

It appends one and multiple sources from the local file system to the destination.


hdfs dfs -append to file local_src….  Destination_dir_name

22. count

This is used to counts the number of directories and files.


hdfs dfs -count dir_name

23. checksum

It returns checksum information of a particular file.


hdfs dfs -checksum file_name

Recommended Articles

This is a guide to Hadoop Commands. Here we discuss the introduction, various HDFS Commands in Hadoop that are used for HDFS File Operations. You can also go through our other suggested articles to learn more –

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