Discover Top Posts Tagged with #apache software and project development help

Data Engineering Concepts, Tools, and Projects

All the associations in the world have large amounts of data. If not worked upon and anatomized, this data does not amount to anything. Data masterminds are the ones. who make this data pure for consideration. Data Engineering can nominate the process of developing, operating, and maintaining software systems that collect, dissect, and store the association’s data. In modern data analytics, data masterminds produce data channels, which are the structure armature.

How to become a data engineer:

While there is no specific degree requirement for data engineering, a bachelor's or master's degree in computer science, software engineering, information systems, or a related field can provide a solid foundation. Courses in databases, programming, data structures, algorithms, and statistics are particularly beneficial. Data engineers should have strong programming skills. Focus on languages commonly used in data engineering, such as Python, SQL, and Scala. Learn the basics of data manipulation, scripting, and querying databases.

Familiarize yourself with various database systems like MySQL, PostgreSQL, and NoSQL databases such as MongoDB or Apache Cassandra.Knowledge of data warehousing concepts, including schema design, indexing, and optimization techniques.

Data engineering tools recommendations:

Data Engineering makes sure to use a variety of languages and tools to negotiate its objects. These tools allow data masterminds to apply tasks like creating channels and algorithms in a much easier as well as effective manner.

1. Amazon Redshift: A widely used cloud data warehouse built by Amazon, Redshift is the go-to choice for many teams and businesses. It is a comprehensive tool that enables the setup and scaling of data warehouses, making it incredibly easy to use.

One of the most popular tools used for businesses purpose is Amazon Redshift, which provides a powerful platform for managing large amounts of data. It allows users to quickly analyze complex datasets, build models that can be used for predictive analytics, and create visualizations that make it easier to interpret results. With its scalability and flexibility, Amazon Redshift has become one of the go-to solutions when it comes to data engineering tasks.

2. Big Query: Just like Redshift, Big Query is a cloud data warehouse fully managed by Google. It's especially favored by companies that have experience with the Google Cloud Platform. BigQuery not only can scale but also has robust machine learning features that make data analysis much easier. 3. Tableau: A powerful BI tool, Tableau is the second most popular one from our survey. It helps extract and gather data stored in multiple locations and comes with an intuitive drag-and-drop interface. Tableau makes data across departments readily available for data engineers and managers to create useful dashboards. 4. Looker: An essential BI software, Looker helps visualize data more effectively. Unlike traditional BI tools, Looker has developed a LookML layer, which is a language for explaining data, aggregates, calculations, and relationships in a SQL database. A spectacle is a newly-released tool that assists in deploying the LookML layer, ensuring non-technical personnel have a much simpler time when utilizing company data.

5. Apache Spark: An open-source unified analytics engine, Apache Spark is excellent for processing large data sets. It also offers great distribution and runs easily alongside other distributed computing programs, making it essential for data mining and machine learning. 6. Airflow: With Airflow, programming, and scheduling can be done quickly and accurately, and users can keep an eye on it through the built-in UI. It is the most used workflow solution, as 25% of data teams reported using it. 7. Apache Hive: Another data warehouse project on Apache Hadoop, Hive simplifies data queries and analysis with its SQL-like interface. This language enables MapReduce tasks to be executed on Hadoop and is mainly used for data summarization, analysis, and query. 8. Segment: An efficient and comprehensive tool, Segment assists in collecting and using data from digital properties. It transforms, sends, and archives customer data, and also makes the entire process much more manageable. 9. Snowflake: This cloud data warehouse has become very popular lately due to its capabilities in storing and computing data. Snowflake’s unique shared data architecture allows for a wide range of applications, making it an ideal choice for large-scale data storage, data engineering, and data science. 10. DBT: A command-line tool that uses SQL to transform data, DBT is the perfect choice for data engineers and analysts. DBT streamlines the entire transformation process and is highly praised by many data engineers.

Data Engineering Projects:

Data engineering is an important process for businesses to understand and utilize to gain insights from their data. It involves designing, constructing, maintaining, and troubleshooting databases to ensure they are running optimally. There are many tools available for data engineers to use in their work such as My SQL, SQL server, oracle RDBMS, Open Refine, TRIFACTA, Data Ladder, Keras, Watson, TensorFlow, etc. Each tool has its strengths and weaknesses so it’s important to research each one thoroughly before making recommendations about which ones should be used for specific tasks or projects.

Smart IoT Infrastructure:

As the IoT continues to develop, the measure of data consumed with high haste is growing at an intimidating rate. It creates challenges for companies regarding storehouses, analysis, and visualization.

Data Ingestion:

Data ingestion is moving data from one or further sources to a target point for further preparation and analysis. This target point is generally a data storehouse, a unique database designed for effective reporting.

Data Quality and Testing:

Understand the importance of data quality and testing in data engineering projects. Learn about techniques and tools to ensure data accuracy and consistency.

Streaming Data:

Familiarize yourself with real-time data processing and streaming frameworks like Apache Kafka and Apache Flink. Develop your problem-solving skills through practical exercises and challenges.

Conclusion:

Data engineers are using these tools for building data systems. My SQL, SQL server and Oracle RDBMS involve collecting, storing, managing, transforming, and analyzing large amounts of data to gain insights. Data engineers are responsible for designing efficient solutions that can handle high volumes of data while ensuring accuracy and reliability. They use a variety of technologies including databases, programming languages, machine learning algorithms, and more to create powerful applications that help businesses make better decisions based on their collected data.

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#data engineer #Streaming Data #Apache Hive #Tableau #Big Query

Going FOSS: An Intro to Open-Source software for studyblr (and also some privacy related bits)

Source for Header Image

Intro & attempt at TLDR

Hey everyone! Today I’d like to tell y’all something about Open Source Software, and also Why this should matter to you! This’ll probably be the first post of a series I intend to do, because I believe the Studyblr community, even the non-nerd folks, could really benefit from switching some things out in their digital environment. Since this is a long post, I attempted to summarise it below, please do read on if you have the spoons tho!

TLDR?

FOSS stands for “Free and Open Source Software” the “free” part doesn’t necessarily mean it’s free as in free pizza, but mostly means free as in freedom.

There’s a humongous amount of variants on this concept, but the core of FOSS specifically is the four freedoms:

1. To run the program however you want and for whatever you want

2. To study how the program works and to change it in whatever way you want

3. To be able to share it with whomever you feel like

4. To be able to share your modified version with whomever you want

There’s a whole host of software licenses built around these concepts, you can check those out at the Open Source Initiative website, or at Choose A License. Both have a good summary of what they all stand for.

Open Source software is used for a lot of products, nearly every single webserver is an Apache Linux server, Google chrome is built on top of their open source chromium (google is still the devil, but y’know, it’s an example), and even deep deep down, Apple computers run on top of a Linux Kernel. Many more can be listed, but I won’t do that otherwise this isn’t a TLDR anymore.

Now, Why is this important for you? The Open Source Initiative summed it up real nicely already, but heres a short paraphrase:

Control & Security. If software is open source then you can check if it really works the way it does, and to make sure it’s not spying on you. Even if you don’t have the skills for it, someone else who does will be able to check. Also if you don’t like how something works in a program, then you’ll be able to change it or find someone else’s changed version that you like more.

Training. People who want to learn programming can use the code to see what makes programs tick, as well as use it as a guide for their own projects.

Stability. Because everything’s out in the open, that means someone else can take up maintaining a project or make a successor of it, in case the original developers suddenly quit working on it. This is especially important when it’s software that’s absolutely critical for certain tasks.

Community. It’s not just one program. It’s a lot of people working together to make, test, use, and promote a project they really love. Lots of projects end up with a dedicated fanbase that helps support the developers in continuing to work on the software.

I’d like to add one more tho: Privacy, which ties in a lot with the security part. Nowadays with protests going on and everything being online due to the pandemic, folks have been and will be confronted much more with the impact of privacy, and lack thereof. Open Source software means that if any company or group tries to spy on you, then you and anyone who feels like checking, will be able to know and take action on it. Here’s the EFF page on privacy and why it should matter to you

If that got your attention then read on past the readmore button! Or, if nothing else maybe check out the Free and Open Source Software portal on Wikipedia? Or maybe the resources page of the Open Source Initiative?

How can a software development company help you with mobile apps?

A typical mobile application uses a network link to work with remote computing services, and mobile application creation is the process of developing software applications that run on a mobile device. As a result, the mobile development company India entails developing installable software packages (code, binaries, properties, and so on), integrating backend services including data access via an API, and testing the app on target devices.

Working according to the latest trends

In today's smartphone market, there are two main channels. Apple Inc.'s iOS application is one of them. The iOS platform is the operating system that runs on Apple's iconic iPhone handsets. Google's Android is the second. Many other OEMs, in addition to Google, use the Android operating system to create their own smartphones and other smart devices.

While there are some parallels in terms of designing apps for these two platforms, developing for iOS vs. developing for Android needs different software development kits (SDKs) and development tool chains with help of Software Company in sector 63 Noida.

The connection of trends with hybrid mobile apps

Hybrid mobile apps are packaged as software installation kits and designed with standard web technologies including JavaScript, CSS, and HTML5. Hybrid apps, unlike native apps, run on a 'internet container,' which includes a browser runtime as well as a bridge to native device APIs through Apache Cordova.

By avoiding app store distribution and installation, PWAs provide an alternative to conventional mobile app production. PWAs are web applications that use a series of browser features to provide a 'app-like' user interface, such as operating offline, running a background process, and adding a connection to the device's home screen.

The role played by software development

Software development is the method of developing and maintaining programmers, frameworks, and other software components by conceiving, specifying, designing, programming, logging, checking, and bug fixing. Software creation is the process of writing and preserving source code, but it often encompasses anything that happens between the conception of a desired software and its final manifestation, often in a prepared and organized manner.

·Alignment: How does this project fit into the broader mission and priorities of your company?

·Availability and distribution of resources: Do you have the people and tools you'll need to take on this project?

·Project scheduling: How does this project fit into the priorities and other responsibilities of your company?

·Estimated costs: How far would it set you back?

In order to decide what exactly the specifications are, stakeholders, end users, and the project team often gather and discuss in the early stages. The project team must then determine how future software will be developed and which programming languages will be used. Want to know more about it then give us a call Cyber Matrix Solution and get all your queries resolved.

#mobile development company India #software company sector 63 Noida #app #developer #development #services #webdevelopment

Bullet Updates - Windowing, Apache Pulsar PubSub, Configuration-based Data Ingestion, and More

By Akshay Sarma, Principal Engineer, Verizon Media & Brian Xiao, Software Engineer, Verizon Media

This is the first of an ongoing series of blog posts sharing releases and announcements for Bullet, an open-sourced lightweight, scalable, pluggable, multi-tenant query system.

Bullet allows you to query any data flowing through a streaming system without having to store it first through its UI or API. The queries are injected into the running system and have minimal overhead. Running hundreds of queries generally fit into the overhead of just reading the streaming data. Bullet requires running an instance of its backend on your data. This backend runs on common stream processing frameworks (Storm and Spark Streaming currently supported).

The data on which Bullet sits determines what it is used for. For example, our team runs an instance of Bullet on user engagement data (~1M events/sec) to let developers find their own events to validate their code that produces this data. We also use this instance to interactively explore data, throw up quick dashboards to monitor live releases, count unique users, debug issues, and more.

Since open sourcing Bullet in 2017, we’ve been hard at work adding many new features! We’ll highlight some of these here and continue sharing update posts for future releases.

Windowing

Bullet used to operate in a request-response fashion - you would submit a query and wait for the query to meet its termination conditions (usually duration) before receiving results. For short-lived queries, say, a few seconds, this was fine. But as we started fielding more interactive and iterative queries, waiting even a minute for results became too cumbersome.

Enter windowing! Bullet now supports time and record-based windowing. With time windowing, you can break up your query into chunks of time over its duration and retrieve results for each chunk. For example, you can calculate the average of a field, and stream back results every second:

In the above example, the aggregation is operating on all the data since the beginning of the query, but you can also do aggregations on just the windows themselves. This is often called a Tumbling window:

With record windowing, you can get the intermediate aggregation for each record that matches your query (a Sliding window). Or you can do a Tumbling window on records rather than time. For example, you could get results back every three records:

Overlapping windows in other ways (Hopping windows) or windows that reset based on different criteria (Session windows, Cascading windows) are currently being worked on. Stay tuned!

Apache Pulsar support as a native PubSub

Bullet uses a PubSub (publish-subscribe) message queue to send queries and results between the Web Service and Backend. As with everything else in Bullet, the PubSub is pluggable. You can use your favorite pubsub by implementing a few interfaces if you don’t want to use the ones we provide. Until now, we’ve maintained and supported a REST-based PubSub and an Apache Kafka PubSub. Now we are excited to announce supporting Apache Pulsar as well! Bullet Pulsar will be useful to those users who want to use Pulsar as their underlying messaging service.

If you aren’t familiar with Pulsar, setting up a local standalone is very simple, and by default, any Pulsar topics written to will automatically be created. Setting up an instance of Bullet with Pulsar instead of REST or Kafka is just as easy. You can refer to our documentation for more details.

Plug your data into Bullet without code

While Bullet worked on any data source located in any persistence layer, you still had to implement an interface to connect your data source to the Backend and convert it into a record container format that Bullet understands. For instance, your data might be located in Kafka and be in the Avro format. If you were using Bullet on Storm, you would perhaps write a Storm Spout to read from Kafka, deserialize, and convert the Avro data into the Bullet record format. This was the only interface in Bullet that required our customers to write their own code. Not anymore! Bullet DSL is a text/configuration-based format for users to plug in their data to the Bullet Backend without having to write a single line of code.

Bullet DSL abstracts away the two major components for plugging data into the Bullet Backend. A Connector piece to read from arbitrary data-sources and a Converter piece to convert that read data into the Bullet record container. We currently support and maintain a few of these - Kafka and Pulsar for Connectors and Avro, Maps and arbitrary Java POJOs for Converters. The Converters understand typed data and can even do a bit of minor ETL (Extract, Transform and Load) if you need to change your data around before feeding it into Bullet. As always, the DSL components are pluggable and you can write your own (and contribute it back!) if you need one that we don’t support.

We appreciate your feedback and contributions! Explore Bullet on GitHub, use and help contribute to the project, and chat with us on Google Groups. To get started, try our Quickstarts on Spark or Storm to set up an instance of Bullet on some fake data and play around with it.

#open source #big data #java #javascript

Opportunities in Data Management With Hadoop

Every day, each minute, many photos, videos and alternative sorts of data are being dropped on to the net via websites like Facebook, YouTube etc. Ever puzzled where this data is being kept to be used effectively year once a year? The growing variety of data sources like social media are difficult the large data technologies. Being the newest sensation, media giants like Google, Facebook and Yahoo have determined to decide on Hadoop for his or her data management predicaments.

Any enterprise wish to leverage its data and analytics is suggested to put in the Hadoop framework; open supply software that enables the low-cost process of enormous data over clusters of computers.

History of Hadoop

Hadoop was created back in 2005 by pc scientists Doug Reed Cutting and Mike Cafarella. Hadoop was named by Doug once his son's stuffed toy elephant and is currently being managed by Apache software package Foundation. In 2006 Dough joined Yahoo! that dedicated a team to develop Hadoop. By 2008, Hadoop was getting used by alternative firms besides Yahoo! like Facebook, New York Times and Last.fm.

The Hadoop design is formed of the Hadoop Common, Hadoop distributed file system (HDFS) and a Map Reduce engine. Map Reduce and HDFS are designed to handle any node failures. The design distributes data into chunks across several servers for the programmer’s simply to analyze and visualize easily. You may gain success in moving forward to your career with the help of Big Data Hadoop training in Bangalore.

Demand for Hadoop

The marketplace for Hadoop is projected to rise from a $1.5 billion in 2012 to a calculable $16.1 billion by 2020 as per a report by Allied marketing research. The profits are foreseen to be created by the business Hadoop firms like Amazon net Services, Cloud era, Horton works etc.

The reason for the success of this platform is its low-value implementation that helps firms to adopt this technology a lot of handily. It’s additionally adept at mechanically handling node failures and data replications and will all the diligence.

It is clear that the data management trade has swollen from software and get into retail, hospitals, government etc. This creates a large demand for scalable and price effective platforms for data storage like Hadoop. Therefore it comes as a no surprise that a ability in Hadoop is most desired as of currently. the longer term of data storage is endless because it is extremely unlikely that the businesses can stop storing their data or notice an alternate to try and do therefore any time before long.

Training in Hadoop basics is certain to go a long approach and can pay off within the long haul as firms are willing to offer competitive salaries for candidates with desired skill-sets. Banking on this demand will certainly prove helpful. If you want a more detail about Big Data Hadoop. Please visit Big Data courses in Bangalore.

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Despite big data currently ranking among top business intelligence and data analytics trends, businesses continue to suffer from a lack of data-savvy talent. Research from BARC shows half of respondents reporting a lack of analytical or technical know-how for big data analytics. This is good news for tech beginners, however, whose knowledge and skills are being welcomed by companies who want to reap the benefits of big data.

If you find data science a tempting opportunity, you’ll benefit from this overview of big data basics for beginners. Below, we’ll discuss what the requirements for jobs are and which skills you should master in order to start a successful data science career.

What is Big Data?

Instead of reciting a definition or giving a generic overview, let’s look at big data’s key features through the lens of something that is well known to all of us: recommendation engines. These are tools widely used in e-commerce to aid in customer experience, but that also help gather data about consumers. Web store visitors search for products, view them, add and remove them from their carts, make purchases like, etc. – and every activity is an entry in a database. The entry may look like “Customer X opened Product Y page.” Millions of customers exist, and they perform dozens of activities per visit, which means that a retailer needs impressive storage capacity to log all these actions.

Distributed data storage has become a solution to this problem. According to this principle, data is stored on numerous standard computers rather than on one custom-built powerful machine. This allows companies to achieve high scalability: when the number of records increases, the retailer can just add extra machines.

Each time a visitor starts a new tour on the website, the analytical system tracks all their activities and compares them with previous activities of this particular visitor and those of other visitors. In order to perform this task quickly, the analytical system divides the tasks among numerous machines to enable parallel data processing. The analysis results lay the basis for personalized recommendations.

Summing it up: Big data is data sets that resemble a log of events by nature and require distributed data storage, parallel data processing and special approaches and methods. You can learn more about big data use cases in this primer.

Big Data Technology Stack

You should generally expect to master multiple technologies to become an expert in big data. We’ve selected the most popular frameworks and programming languages for a beginner to get acquainted with. The list is not exhaustive: so, feel free to go beyond it whenever you are ready.

Big data frameworks

Apache Hadoop is a framework for parallel data processing and distributed data storage.

Apache Spark is a parallel data processing framework.

Apache Kafka is a stream processing framework.

Apache Cassandra is a distributed NoSQL database management system.

Big data programming languages

Java

Scala

Python

R (not obligatorily, but good to know)

What are the Programming Paradigms Used in Big Data?

It’s advisable to grasp general programming concepts (such as declarative and imperative), as well as big data-specific paradigms (MapReduce).

Declarative paradigm is the approach to programming that is focused on declaring what the task is and the expected results are, without describing the control flow. This approach is used in database programming. For example, SQL (Structured Query Language) is a declarative language.

Imperative programming is the approach focused on describing the commands that should be executed for the program to change its state. It is used for backend development (for instance, in Java).

For example: Copy a directory from A to B shows a declarative approach, while if it’s enriched with such commands as check if there are existing files with the same name and copy only new ones – it’s an imperative approach.

MapReduce paradigm is the concept of parallel processing of distributed data. It allows for dealing with large data sets by applying map function for data filtering, sorting or parameterization and reduce function for summarizing the interim results.

Jobs in Big Data

Now for the burning question: What kinds of big data jobs exist? The good news: there is quite a choice.

Data analysts closely interact with the end users to identify their needs, analyze and interpret data, build reports and visualize data.

Data scientists assess data sources and establish data collection procedures, apply algorithms and machine-learning techniques to mine data.

Data architects design databases and develop relevant documentation and policies.

Database managers control database performance, troubleshoot the corporate databases and upgrade hardware and software.

Big data engineers design, implement and support big data solutions.

Don’t be misled by the fact that only one of the jobs – a big data engineer – refers to big data directly. With good knowledge of big data, you have more value for any job in data analytics. With the lack of such knowledge, you may have limited opportunities in terms of the tasks or projects assigned.

Big data is evolving as more and more businesses see its benefits. However, research clearly shows a lack of big data experts. It’s time to bridge this gap by educating the next wave of tech beginners. To pave your way into the big data world, it’s important to get a strong grasp of the basics first. A newbie should cover both big data-specific technologies and general ones. Feel free to refer back to this article on your education journey, and best of luck!

#big data

I did it my way. (With a Little Help from My Friends)

‘I planned each charted course Each careful step along the byway And more, much more than this I did it my way’ - Sinatra

Over the last three years, I’ve built out some proof-of-concept data visualization applications for some large scale Enterprise clients, across a multitude of vertical markets.

These have included, in no particular order:

Semi-conductor manufacturing

Wearable technology manufacturing

Pharmaceutical distribution

Financial

Oil & Gas

Retail

Consumer Hardware & Software

Mobile Communications

Energy Utility

Without exception, every Enterprise client presented similar challenges - namely, how to visually represent data at scale in an insightful, and actionable format.

Here is my methodology.

I adopted a Data strategy:

Data as a service,

ETL as a service,

Data Science as a service, and

Data Visualization as a service.

Data as a Service (DaaS)

Data Acquisition

Technology is making acquiring data in an more automated manner arguably easier and relatively cheaper, increasing the volume and velocity of data produced.

SCADA (Supervisory control and data acquisition devices), Bank ATM’s, merchant credit card swipe terminals, website forms, and sensors - such as Infra-red, Radar and Sonar - even when you compose a tweet...all examples of data acquisition.

With more and more IoT (Internet of Things) devices becoming available, automation of data collection is becoming more even more universal and ubiquitous.

Data Storage

If a record has a time-stamp, it can be recognized as an event, or a transaction; i.e. something happened at this time, on this day, in this month, in this year. These records are (normally) stored in a database.

That was my bread and butter, making sense of events that have happened - or, what was happening in (near) real-time. In recent engagements, it’s the latter that seemed to be more pervasive - sustaining ‘live’ data connections that are capable of very fast refresh rates - sometimes on a per second basis (but more often than not, updated daily).

Data as a Service at the Enterprise meant I’d be connecting to a “Data Lake” such as Hadoop/Hive, a Teradata warehouse on-premise database, or a cloud database like Redshift on the Amazon Web Services platform.

Alternatively (or sometimes additionally), I’d be connecting to ‘NoSQL’ databases like Mongo and Cassandra, while location data was held in GIS (Geo-spatial Intelligence Software) databases like PostGIS or ESRI.

There were also databases that are designed to take advantage of in-memory technologies, and are suited to analytical applications; such as SAP Hana, Memqsl, and Snowflake.

My preferred solution for the foundation of a Data as a Service based architecture is Exasol, because it is proven to be capable of performing analytical tasks at scale, leveraging massively parallel processing and in-memory technologies, enabling rapid responses to intensive queries over massive data sets.

ETL (Extract, Transform, Load) as a Service

‘Extracting’ reads data from a source database (and potentially multiple other sources), ‘Transforming’ is the process of converting this data (joining, unions, performing calculations, cleansing and aggregating) and ‘Loading’ writes to the target database (or writing back to the source).

Business Intelligence applications such as Tableau, Qlik, and Microstrategy often require data to be ‘shaped’ or ‘structured’ in a certain way; usually in a columnar format.

This used to be an arduous task - involving writing batch scripts - but no longer. There are a plethora of enterprise ETL solutions available such as AWS Glue, Apache Kafka and Informatica.

My preferred solution for the basis of an ETL as a Service based architecture is Alteryx, because it is proven to be capable of extracting data from multiple sources - including Hadoop/Hive, Mongo, ESRI and Exasol.

Using an intuitive drag and drop GUI (Graphical User Interface) - it is possible to develop a repeatable, re-usable data transformation as an automated process (also known as a workflow) that can be run on a scheduled basis.

Data Science as a Service

Traditionally, Enterprises would refer complex analytical and statistical tasks such as predicting, modelling, forecasting and so forth to highly skilled data scientists.

It is now possible to automate some of these complex tasks - on Platforms like IBM DSx (accessing tools like Watson ML & Apache Spark), and AWS Domino (accessing tools like Python, Julia & Matlab) but my preference is again, Alteryx, because it is proven to be capable of generating highly accurate predictive models, simulations and forecasts (using the open source R) at scale, as an automated process.

Data Visualization as a Service

There are many Data Visualization tools and libraries available: IBM Cognos, Plotly, Microsoft PowerBI - but here I have three preferences, and sometimes, depending on the scenario and use-cases, I’ll combine all three.

Tableau is proven to be capable of plotting huge amounts of data points on a HTML Canvas. The Server JavaScript and REST APIs (Application Programming Interfaces) allow integration with responsive design Bootstrap web applications and a consistent library of user interfaces. Combined with an Exasol database connection, Tableau is capable of querying multi-million high granularity records - for example transactions - allowing for interactivity over multiple plots/charts.

D3 is my preference if I am using low granularity or summary data. Instead of a server responding to a query and returning that response, d3 downloads and processes data client side, in a browser. D3 is capable of drawing elements on an HTML Canvas or rendering SVG (Scaleable Vector Graphics). It is cross-browser, platform agnostic, and ultimately, the most flexible library which allows for full customization.

Mapbox is my preference if I am using location data. It is capable of rendering multi-million data points using vector tiles, which can be queried client side in a browser.

User Experience/User Interface (UX/UI)

jQuery UI is my preference for a consistent User Interface library. I use Bootstrap to develop responsive design web applications. I typically use client CSS and style guides to comply with typography, color palette and brand guidelines for the application.

Charts and graphs typically remain in a grayscale color palette, with chart types conforming with Tufte/Few guidelines.

Example #1

Scenario: Four Dimensional Seismic Survey

Use-case: Predict magnitude of seismic activity over time for the different formations (horizons) in the anticline, and compare with actual values.

Example #2

Scenario: Fleet Credit Cards

Use-case: Predict churn and retention over time for different retail sites and compare with actual values, making the last data point actionable (alert site manager upon difference to target and/or outside normal parameters).

Example #3

Scenario: Demand and supply of products over time for different markets

Use-case: Predict origin and destination locations of logistical assets and compare with actual values over time, to inform a forecast model of product supply and demand.

Of course, I was being my normal flippant self when I sang ‘I did it my way’. I had more than a little help from my friends - you know who you are of course, because I’ve tipped my hat to you on many occasions in previous blog posts.

‘What would you do if I sang out of tune? Would you stand up and walk out on me? Lend me your ears and I'll sing you a song I will try not to sing out of key’ - Lennon/McCartney

The communities

Over the last three years, I’ve learned a lot from developers in various communities:

The twitter-verse of data visualization practitioners,

The Tableau community forum,

The Alteryx community forum,

GIS Stack Exchange,

Stack Overflow,

GitHub,

Behance,

Dribbble, and

Codepen

‘What would you do if I sing out of tune, Would you stand up and walk out on me?‘

The Tableau ‘community’ as of late 2017, seems to be going through a radical period of introspection. When I say ‘Tableau community’ - I’m really referring to the ‘Tableau twitterati’ - not the community forum participants per-se, but cliques such as MakeoverMonday and Women + Data, and the ultimate Tableau coterie - Zen Masters.

In fact, Tableau themselves referred to these groups as ‘tribes’.

A culture, or sub-culture, can form behaviors and attitudes that stem from attachment and perceived loyalty to that clique. Sectarianism is synonymous with tribalism, and is often an consequence of division.

When I read tweets haranguing other practitioners about plagiarism, and read blog posts with political statements to underpin an opinion, or examples of promoting gratuitous and egotistical self promotion, it gives me great pause for concern, and it’s very tempting to stand up, and walk out the door in disgust at what the community not only regards as acceptable, but normal.

‘Lend me your ears and I'll sing you a song, I will try not to sing out of key’

I recommend that Tableau shutters the Zen Master program, and instead, promotes and grows the Tableau Foundation Data Fellowship.

I recommend that the Makeover Monday leadership and participants re-focus their efforts by contributing to the Tableau Foundation Projects and develop towards meeting the Sustainable Development Goals, volunteering their time to the Tableau Service Corps.

I recommend that Tableau welcome Women + Data members on the board of their diversity committees and judging panels of the ‘IronViz’ competitions and feeder contests.

I believe that these recommendations would foster an inclusive, collaborative culture, rather than accepting and promoting sectarianism as a norm; and would re-energize the wider Tableau community.

#tableau #data visualization

Do you know what could be the key differentiator between a market leader and a has-been leader?

Do you know what could be the key differentiator between a market leader and a has-been leader? It is “data management” and any organization that cannot handle the influx of data and put it into good use is likely to give way to wiser companies that know how to make their data work. They are constantly on the quest for finding new strategies to make innovative use of Big Data. In fact, the power of Big data and mobility can truly elevate businesses to new levels. Hence, Big Data is the term given to huge amounts of data. As the data comes in from a variety of sources, it could be too diverse and too massive for conventional technologies to handle. This makes it very crucial to have the skills and infrastructure to handle it intelligently. This data must be analyzed computationally to reveal patterns and trends, thereby aiding in marketing and promotional campaigns. Here are a few examples of organizations that make use of Big Data: · Government organizations track social media insights to capture the onset or outbreak of a new disease. · Oil and gas companies integrate their drilling equipment with sensors to ensure safe and more productive drilling. · Retailers track web clicks to help identify the various behavioral trends to improve their ad campaigns. Now, let’s look at some of the trendy big data technologies that you can use to promote your business:

1. Apache Spark

With its built-in modules for streaming, machine learning, graph processing and SQL support, Apache Spark certainly deserves a mention as the fastest and general engine for big data processing. It supports all important Big Data languages including Python, Java, R and Scala. It complements the main intention why Hadoop was initially introduced. The main concern with data processing is speed, so you need something to diminish the waiting time between queries and the time it takes to run the program. Even though Spark was introduced to speed up the computational computing software process of Hadoop, it is not an extension of the latter. In fact, Spark uses Hadoop for two main purposes only — storage and processing. Use case: Apache Spark is a major boon to companies aiming to track fraudulent transactions in real time, for example, financial institutions, e-commerce industry and healthcare. Suppose a credit card was swiped for a huge amount, say, Rs. 50,000 whilst your wallet was lost and it wasn’t you who swiped, it is possible to detect where and when the fraud took place.

2. Apache Flink

If you have heard of Apache Spark and Apache Hadoop, then you will have heard about Apache Flink as well. Flink is a community driven open source framework, founded by Professor Volker Markl — Technische University, Germany. Flink meaning “swift” in German is high performing and extremely accurate data streaming. The capabilities of Flink is inspired by MPP database technology (for functioning like Declaratives, Query Optimizer, Parallel in-memory, out-of-core algorithms) and Hadoop MapReduce technology for functions like Massive scale out, User Defined functions, Schema on Read).

3. NiFi

NiFi is a powerful and scalable tool to possess, thanks to its capacity to store and process data from a variety of sources with minimal coding and a comfortable UI. And that’s not all. It can easily automate the data flow between different systems. If NiFi doesn’t contain any sources that you require, then the straightforward Java code lets you write your own Processor. The specialization of NiFi is data extraction and is a highly useful solution for filtering data. As NiFi is an NSA project, the security for this tool is commendable.

4. Kafka

Kafka is a must because it is a great glue between various systems right from Spark, NiFi to third party tools. And streams of data can be handled efficiently and in real time. Kafka is open source, horizontally scalable, is fault tolerant, extremely fast and a safe option. Being a distributed system, Kafka stores the messages (simple byte arrays and developers store any object in any format) in topics, and the topics themselves are partitioned and replicated across different nodes. When Kafka was first introduced, it was a distributed messaging system built initially at LinkedIn, but now is part of the Apache Software Foundation and is used continuously by thousands of companies. Use case: Pinterest uses Apache Kafka. The company built a platform called Secor using Kafka, Storm and Hadoop for real-time data analytics to ingest data into MemSQL.

5. Apache Samza

The main purpose of the conception of Apache Samza is to extend the capabilities of Kafka and is integrated with the feature alike Fault Tolerant, Durable messaging, Simple API, Managed State, Extensible, Processor Isolation and Scalable. It uses Apache Hadoop YARN for fault tolerance and Kafka for messaging. Thus, you can say it is a distributed stream processing framework. And it comes with a pluggable API to run Samza with other messaging systems.

6. Cloud Dataflow

Cloud Dataflow is a native Google cloud data processing service integrated with simple programming model for both batch based and streaming data processing tasks. With this tool, you no longer have to worry about operational tasks including performance optimization and resource management. Through its fully managed service, it is possible to dynamically provision the resources to maintain high utilization efficiency while minimizing latency. And you no longer have to worry about programming model switching cost through its unified programming model method. This method aids in batch and continuous stream processing, making it easy to express computational requirements without worrying about data source. Conclusion The big data ecosystem is constantly evolving and new technologies come into existence very frequently, many of them evolving further and further beyond the Hadoop-Spark stacks. These tools can be utilized to ensure seamless work with security and management, sans any hiccups. Data engineers require these tools to pull, clean and set patterns for data to help data scientists explore and examine them thoroughly, and build models.

#big data #technology