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Introduction

Are you too busy fixing bugs in your C-level dashboards or are you spending too much time chasing them down? Do different departments struggle to agree on the data that is required throughout the company? Are you having trouble assessing the potential impact of a possible migration?

A data lineage may be the solution to your data quality problems. A data lineage system improves data visibility and traceability across the entire data stack. It also simplifies the task of communicating about the data your organization relies on.

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What is Data Lineage?

The Data Lineage shows the data flow through various systems and transformations. Data in modern data stacks is not only stored in application databases.

It flows from one application and then to the next, and finally to data warehouses where it can be transformed and consumed by any number of downstream applications.

This data flow allows each system access to data in a way that makes sense. Source applications can optimize to improve the performance of read/write transactions. Reporting clients have access to denormalized data, which makes it easy for queries.

This convenience comes at a cost of visibility and traceability. After the data leaves the source database, it is subject to any number of transformations.

This layer can mask the true data. Many reporting teams struggle to determine the source of their data or to identify the correct data to use in a report. They might ask the application team to clarify the situation.

The team may tell them that the data isn’t there because the terms used to describe a piece of data have changed after the transformation.

Solving bugs and problems can take longer and will require the involvement of three teams, the reporting team, and the data warehouse team. The data team typically takes on the task of finding the root cause of the problem. They will then have to go through the version control and try to solve it. This can also slow down the development process for new reports.

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Data Lineage: Why?

A data lineage system allows you to have your cake while still having it. You can have both separation of roles, the performance of a data warehouse, and clear data understanding across all of your systems and teams.

You can trace data throughout the system with clear data understanding and traceability. This can be used to confirm that no personally identifiable data (PII), is being exported from the system and being consumed in places it shouldn’t.

You can also see which data is being used downstream and the impact of possible changes or migrations. You can also identify any unutilized information and allow for easy cleanup of columns or tables.

Data lineage systems improve communication and reduce incident response time by increasing data understanding.

The data lineage system eliminates confusion about the source of data in reports and makes it easy for all parties to understand where it came from. This system speeds up the resolution of errors as well as new development.

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Different types of data lineage

There are two major types of data lineage systems: Active and Passive.

An active data-lineage system is considered “active” as you have to create it. This can be done by either programming the necessary source and transformation information into your system or tagging the data with the appropriate metadata.

Apache Atlas is an example of such an active system. An active data lineage system that is properly configured can give you traceability of your data down to the smallest detail.

These benefits require constant maintenance and updating. This can add complexity to your overall data infrastructure, and can also be time-consuming.

A passive system that attempts to understand your data by itself is the alternative. Passive systems examine the data coming out of the data warehouse.

A passive system uses pattern recognition to identify where the data comes from and what it is being transformed. This can be useful for simple data sets and simpler transformations. However, it can produce inaccurate results.

Another type of passive data lineage system is the parsing-based system. This generates lineage data through reverse-engineering your database warehouse.

A parsing-based system allows you to see exactly where your data is coming from and what it is being used for. Datafold illustrates this type of system. Datafold analyses all DQL code within your data warehouse and generates lineage graphs.

This lineage is much more detailed than table-level and allows you to see which column a piece of data was sourced from, and where it was consumed.

This detail allows for quicker outage response times, faster troubleshooting, as well as reducing the number of production-ready changes.

Datafold has many integrations. Datafold is easy to use and accessible via the Datafold HTTP1_ API. A parsing-based data lineage system, as long as it supports your data warehouse or related systems, is the best choice for implementation and maintenance.

It’s all great but how does it affect my day-to-day? Let’s take a look at this.

Solving bugs or problems can take longer, and it will require the involvement three teams: the reporting team and data warehouse team.

The data team typically takes on the task of finding the root cause of the problem. They will then have to go through the version control and try to solve it. This can also slow down the development process for new reports.

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Data Lineage: Why?

A data lineage system allows you to have your cake while still having it. You can have both separation of roles , the performance of a data warehouse, and clear data understanding across all of your systems and teams.

Data lineage systems improve communication and reduce incident response time by increasing data understanding.

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Different types of data lineage

There are two major types of data lineage systems: Active and Passive.

Apache Atlas is an example of such an active system. An active data lineage system that is properly configured can give you traceability of your data down to the smallest detail.

These benefits require constant maintenance and updating. This can add complexity to your overall data infrastructure, and can also be time-consuming.

A passive system that attempts to understand your data by itself is the alternative. Passive systems examine the data coming out of the data warehouse.

Another type of passive data lineage system is the parsing based system. This generates lineage data through reverse-engineering your database warehouse.

A parsing-based system allows you to see exactly where your data is coming from and what it is being used for.

Datafold illustrates this type of system. Datafold analyses all DQL code within your data warehouse and generates lineage graphs.

This lineage is much more detailed than table-level and allows you see which column a piece of data was sourced from, and where it was consumed. This detail allows for quicker outage response times, faster troubleshooting, as well as reducing the number of production-ready changes.

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How can Data Lineage ensure day-to-day data quality?

A data lineage system provides visibility and traceability that is better than ever. Three clear benefits can be seen in your day-to-day operations.

It increases your team’s response time. It doesn’t take hours to find the root cause of an error in a reporting. This is possible with the cooperation of multiple teams. Errors can be quickly identified and corrected if you have complete visibility of the data flow across your entire data stack.

It allows the creation and maintenance of a common vocabulary. The application team understands what views are and where they come from when the report team discusses them.

The application team can see what data has been aggregated to create the dashboard that informs company outlook and decisions.

Over time, terminology discrepancies can be reduced or eliminated, which allows for better communication throughout the company.

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Wrap-up

This article explains what data lineage is and why you might use it. We also explain the various types of data lineage available, as well as how data lineage can help improve data quality every day. The addition of a data-lineage system to your data stack will increase transparency and reduce headaches for the entire organization.

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Cyanogen Takes Cyanogenmod Down With It, Lineage Is Born

Cyanogen takes CyanogenMod down with it, Lineage is born

It was a bittersweet Christmas for users and fans of the most popular custom Android ROM, CyanogenMod, and for the custom ROM scene in general. Cyanogen, Inc. announced that services and servers related Cyanogen’s nightly builds will cease at the end of this year. Unfortunately that move has affected the open source CyanogenMod project, who later confirmed what many feared the most. CyanogenMod was also shutting down, due to technical as well as potential legal problems that might follow. Its spirit, however, will live on in a new “grass roots” effort called “Lineage”.

It was a sorry and confusing development in what was a fairy tale turned nightmare. CyanogenMod exists as an independent entity from Cyanogen, the company. At least in theory. In practice, however, Cyanogen has become somewhat tied to CyanogenMod, providing server infrastructure, especially for nightly builds, and even hiring some of its developers. So when the company announced that it was practically shuttering its development of its CyanogenOS, it was also practically putting a bullet through CyanogenMod’s head, not Google’s.

Cyanogen’s failure wasn’t really a surprise, though few probably could have predicted how spectacular it would be. Perhaps its downhill journey started when it practically screwed its first hardware partner, OnePlus, out of a business deal in India. CEO Kirt McMaster would later announce war against Google itself for control over Android, poising itself as a David, albeit an ambitious one, fighting a big Goliath.

Cyanogen, however, bit off more than it could chew. It wasn’t able to convince enough big names in the smartphone market to adopt its Cyanogen OS. Unwilling to admit utter defeat, it said it would shift its focus on developing mods for Android instead of a complete ROM. And now it is shutting down everything related to that ROM and is taking down CyanogenMod along with it.

In theory, CyanogenMod could still carry on. It would just need to replace the servers and the people that Cyanogen Inc would be taking away. However, that would be a substantial cost that the project, mostly made up of unpaid volunteers and surviving through donations, could bear. But there’s and even uglier side to the drama. When Steve “cyanogen” Kondik formed Cyanogen Inc with others, he brought along the brand with him and made Cyanogen the owner. In short, Cyanogen owns the “cyanogen” trademark and at any point in time could sell or close it off. Given its previous treatment of OnePlus, that is a distinct possibility. Plus, it makes sense to break away from the negative image that the brand now has.

So while the name of the ROM has changed, its real spirit lives on. CyanogenMod started out as a community, not a commercial, endeavor, and that is what the new LineageOS wants to recapture. In practice, it is in the same boat as CyanogenMod, which means it will be completely reliant on donations and volunteers. Getting infrastructure for hosting and building images will be a tall order, given how its user base and supported devices have grown exponentially compared to the early days. Whether LineageOS will be able to fill in the rather huge shoes left by CyanogenMod remains to be seen in the coming weeks. We wish them the best of luck!

Why Big Data Needs To Become Smart Data?

This article was published as a part of the Data Science Blogathon.

Businesses have always sought the perfect tools to improve their processes and optimize their assets. The need to maximize company efficiency and profitability has led the world to leverage data as a powerful tool. Data is reusable, everywhere, replicable, easily transferable, and has exponential benefits for the business. It can provide useful business insights on customer lifecycle, anomaly or issue detection, real-time data analysis, etc. However, even if data could be a fantastic tool, it is limited if you can extract and interpret the knowledge from the information.

The question now relies on how to process, understand data, and infer useful insights more efficiently and acceleratedly.

This article looks into Big Data and how it develops into Smart Data. Additionally, we will look into the concept of Smart Data and its benefits for businesses.

Source: shapelets.io

What is Big Data?

Five main characteristics often describe big data: volume, value, veracity, velocity, and variety, aka the five V’s. Many experts also consider an additional one: variability. All these attributes compose what we know as “Big Data.” Each of them is key for understanding and analysis of the data.

This concept is not new for companies, as they collect a great volume of information that increases daily. As I understand it, we collect and analyze large amounts of data to obtain actionable insights businesses use to enhance their processes. This is why Big Data is so important for any industry sector.

Did you know that it is estimated that the volume of data generated worldwide will exceed 180 zettabytes in 2025? According to Seagate’s report, that same year, 6 billion consumers, or 75% of the world population, will interact every day with data, and each connected person will have at least one data interaction every 18 seconds. In other words, the volume and the velocity of information will force businesses to increase their data processing speed. Consequently, over the next few years, Big Data will continue to be a key support for strategic development, decision making, enhanced streamlining operation/ business operations, and customer relationships.

Nevertheless, the volume, value, veracity, velocity, and variety of information will force companies to focus on adapting and starting to use tools that help them process the data quicker and smarter. This is where the concept of “Smart Data” emerges.

Source: shapelets.io

What is Smart Data?

Smart Data tools help pre-process the data when ingested to reduce the time of the analysis. What makes “smart data” smart is that the data collection points are intelligent enough to understand the data immediately. Not all data provides the same value to companies; in this scenario, the quality of the information will prevail over the amount of stored data. For example, it allows a device sensor to output useful human-readable data before sending it to a database for storage and/or detailed analysis.

Consequently, Smart Data analytics is the natural evolution of Big Data that aims to treat volumes of data intelligently, as it allows companies to obtain, among others, the following key benefits:

Time-saving

The volume of information that companies ingest doesn’t have any value raw. It needs to be cleaned and then curated to extract any knowledge. By implementing smart software, the data stream or batch will already come partially curated, which could be extremely important when there is a time restriction. For example, a self-driving car can’t afford to wait for data to be sent to the cloud, analyzed, and sent it back. It requires the data to be gathered through a sensor considered “smart,” so the data can be immediately analyzed and then sent to actuators (all internally) who are going to take whatever decision is required at this moment.

It is a great opportunity for SMEs.

Variety of data is as important as the volume and the velocity because many different types of data are available; it can be challenging to treat it if the data quality is not nearly perfect. When creating a smart data strategy, businesses must be careful about the type and quality of data ingested. Bad data quality can cost 12% of the business revenue. Here, Smart Data helps to improve the quality of the information by pre-cleaning it.

For this reason, if small and medium-sized companies use a vast amount of data in a short period, implementing a smart data strategy will help them carefully select the data they are looking for and have a better quality of analysis.

Better customer or consumer service

Traditionally in analytics, the data was amassed, groomed, and then processed at a fixed time (during the week or the day). That workflow means that the data was already obsolete because of the time series analysis.

Prevent problems A step toward automation

Tools that automate the collection and transformation of data are vital, and the need will only grow as you try to extract value from the ever-growing data volumes coming from an ever-increasing number of sources. Smart data is the tool that will allow you to automate your collection and let you focus on more important tasks.

Know the competition better

Intelligent data analysis allows companies to obtain information about the market, the sector in which they operate, and the competitive situation, providing them with useful tools to improve their position, such as price monitoring or change trends.

Source: shapelets.io

In short, Smart Data is a complementary value to Big Data, enabling it to make faster analyses with better data quality and automating data collection and processing. Smart data solutions and strategies will be time-saving thanks to their decentralized data collection and analysis. Additionally, it is an opportunity for SMEs because it will help to select and clean data for a better quality of analysis. It will improve customer service thanks to the hyper-personalization of clients’ data. It will help to detect anomalies before it even occurs, and its automation will let the business focus on more important tasks.

In this article, we dive into the concept of Smart Data and its value in the business sector and data science world. In essence, this feature covers the following:

– Concept of Big Data and why it’s important.

– Concept of Smart data.

– Key benefits of Smart Data in business and data science.

Remember: business intelligence is now key to development and success! Don’t wait, and start reshaping your world!

If you wish to understand more about the application of smart data, big data, or data science, I recommend you have a quick look at the following articles:

Shapelets Data Science studies and applications.

A quick introduction to Big Data.

The media shown in this article is not owned by Analytics Vidhya and is used at the Author’s discretion.

Why Chief Data Scientist Is Important For An Organization?

A chief data scientist bridges the gap between the organization and data scientists.

Organizations often engaged in conversations, where the role and deliverance by the data scientists are scrutinised under the lens of uncertainty. Often the topic about the necessity of chief data scientists is discussed amongst organizations. What most organizations fail to comprehend is, the profile of chief data scientist is not confined to work as an employee in the organization, but they unburden the Chief Technology Officers’ (CTO’s) job, by monitoring Data scientists Data scientists are one of the most valuable entities of an organization. They are the modern-day, data-hungry miners of the tech world, who can convert the data coal into valuable insights. They are researchers who explore every option, look at every algorithm before giving a green flag for the insights. Truthfully, in traditional organizations, the possibility of data scientists getting the required amount of guidance and monitoring by CTO becomes less. Ira Cohen, CTO of Anodot says , “The reason why you need a Chief Data Scientist in the first place is you need somebody who can bridge the gap between management and [the data scientists], and what machine learning can do and cannot do. You need somebody who understands what it is in a deeper form than a CTO, who might have a broader knowledge of a lot of things, but not necessarily machine learning.” The machine learning algorithm is backed by a huge amount of data. But the journey from harnessing data, deploying algorithms, and gaining valuable insights, is not exactly a smooth sale. Different departments have silos, which thwarts the trusts amongst different organizations. The outcomes are often not exactly according to people’s expectation. A part of chief data scientist’s job is to make sure that machine learning models are working well, and that data is transferred seamlessly. Data scientists experiment with data. They dug down the Rabbit hole , search for the problem, fix it and then provide insights. This often involves success and failures. The success of an organization is often driven by the capabilities of the data scientists. While some organizations provide room to data scientists to excavate, and experiment with data and seek a novel solution, many organizations consider this unnecessary. It is then the job of chief data scientists to embellish this aspect of data scientists and help them figure out the time management for data excavation. Ira adds , “When you’re a researcher, it’s very easy for you to go down rabbit holes. You go down lots of rabbit holes. I’ve had to pull my people out of rabbit holes. This is part of what we do. ‘You’ve done enough, pull out. If we have time, we’ll go down that rabbit hole again. But let’s move to the next hole.” This also impacts the confidence and enthusiasm of data scientists and helps them in building trust with the organization. Thus the job of chief data scientists has more to with monitoring the internal environment of a company than to observe the external infrastructure. To reap the maximum benefits from the capabilities of data scientists and to overcome the silos between different departments, organizations must consider keeping chief data scientists as part of an organization.

Pentaho Data Integration Tutorial: What Is, Pentaho Etl Tool

What is Pentaho BI?

Pentaho is a Business Intelligence tool which provides a wide range of business intelligence solutions to the customers. It is capable of reporting, data analysis, data integration, data mining, etc. Pentaho also offers a comprehensive set of BI features which allows you to improve business performance and efficiency.

In this Pentaho tutorial for beginners, you will learn:

Features of Pentaho

Following, are important features of Pentaho:

ETL capabilities for business intelligence needs

Understanding Pentaho Report Designer

Product Expertise

Offers Side-by-side subreports

Unlocking new capabilities

Professional Support

Query and Reporting

Offers Enhanced Functionality

Full runtime metadata support from data sources

Pentaho BI suite

Now, we will learn about Pentaho BI suite in this Pentaho tutorial:

Pentaho BI Suite

Pentaho BI Suite includes the following components:

Pentaho Reporting

Pentaho Reporting depends on the JFreeReport project. It helps you to fulfill your business reporting needs. This component also offers both scheduled and on-demand report publishing in popular formats such as XLS, PDF, TXT, and HTML.

Analysis

It offers a wide range of analysis a wide range of features that includes a pivot table view. The tool provides enhanced GUI features (using Flash or SVG), integrated dashboard widgets, portal, and workflow integration.

Dashboards

The dashboard offers Reporting and Analysis, which contribute content to Pentaho Dashboards. The self-service dashboard designer includes extensive built-in dashboard templates and layout. It allows business users to build personalized dashboards with little training.

Data Mining

Data mining tool discovers hidden patterns and indicators of future performance. It offers the most comprehensive set of machine learning algorithms from the Weka project, which includes clustering, decision trees, random forests, principal component analysis, neural networks.

It allows you to view data graphically, interact with it programmatically, or use multiple data sources for reports, further analysis, and other processes.

Pentaho Data Integration

This component is used to integrate data wherever it exists.

Rich transformation library with over 150 out-of-the-box mapping objects.

It supports a wide range of data source which includes more than 30 open source and proprietary database platforms, flat files. It also helps Big Data analytics with integration and management of Hadoop data.

Who are using Pentaho BI?

Pentaho BI is a widely used tool by may software professionals like:

Open source software programs

Business analyst and researcher

College students

Business intelligence councilor

How to Install Pentaho in AWS

Following is a step by step process on How to Install Pentaho in AWS.

On next page, Accept License Agreement

Proceed for Configuration

Check the usage instructions and wait

Copy Public IP of the instance.

Paste public IP of the instance to access Pentaho.

Prerequisite of Pentaho

Hardware requirements

Software requirements

Downloading and installing Bl suite

Starting the Bl suite

Administration of the Bl suite

Hardware requirement:

The Pentaho Bl Suite software does not have any fix limits on a computer or network hardware as long as you can meet the minimum software requirements. It is easy to install this Business intelligence tool. However, a recommended set of system specifications:

RAM Minimum 2GB

Hard drive space Minimum 1GB

Processor Dual-core EM64T or AMD64

Software requirements

Installation of Sun JRE 5.0

The environment can be either 32-bit or 64-bit

Supported Operating systems: Linux, Solaris, Windows, Mac

A workstation that has a modern web browser interface such as Chrome, Internet Explorer, Firefox

To start Bl-server

On Linux OS run start-pentaho script on /biserver-ce/directory

To start the administrator server:

For Linux: goto the command window and run the start-up script in /biserver-ce/administration-console/directory.

To Stop administrator server:

On Linux. You need to go to the terminal and goto installed directory and run stop.bat

Pentaho Administration Console Report Designer: Design Studio:

It is an Eclipse-based tool. It allows you to hand-edit a report or analysis. It is widely used to add modifications to an existing report that cannot be added with Report Designer.

Aggregation Designer:

This graphical tool allows you to improve Mondrian cube efficiency.

Metadata Editor:

It is used to add custom metadata layer to any existing data source.

Pentaho Data Integration:

The Kettle extract, transform, and load (ETL) tool, which enables

Pentaho Tool vs. BI stack

Pentaho Tool BI Stack

Data Integration (PDI) ETL

It offers metadata Editor It provides metadata management

Pentaho BA Analytics

Reports Designer Operational Reporting

Saiku Ad-hoc Reporting

CDE Dashboards

Pentaho User Console (PUC) Governance/Monitoring

Advantages of Pentaho

Pentaho BI is a very intuitive tool. With some basic concepts, you can work with it.

Simple and easy to use Business Intelligence tool

Offers a wide range of BI capabilities which includes reporting, dashboard, interactive analysis, data integration, data mining, etc.

Comes with a user-friendly interface and provides various tools to Retrieve data from multiple data sources

Offers single package to work on Data

Has a community edition with a lot of contributors along with Enterprise edition.

The capability of running on the Hadoop cluster

JavaScript code written in the step components can be reused in other components.

Here, are cons/drawbacks of using Pentaho BI tool:

The design of the interface can be weak, and there is no unified interface for all components.

Much slower tool evolution compared to other BI tools.

Pentaho Business analytics offers a limited number of components.

Poor community support. So, if you don’t get a working component, you need to wait till the next version is released.

Summary:

Pentaho is a Business Intelligence tool which provides a wide range of business intelligence solutions to the customers

It offers ETL capabilities for business intelligence needs.

Pentaho suites offer components like Report, Analysis, Dashboard, and Data Mining

Pentaho Business Intelligence is widely used by 1) Business analyst 2) Open source software programmers 3) Researcher and 4) College Students.

The installation process of Pentaho includes: 1)Hardware requirements 2) Software requirements, 3) Downloading Bl suite, 4) Starting the Bl suite, and 5) Administration of the Bl suite

Important components of Pentaho Administration console are 1) Report Designer, 2) Design Studio, 3) Aggregation Designer 4) Metadata Editor 5) Pentaho Data Integration

Pentaho is a Data Integration (PDI) tool while BI stack is an ETL Tool.

The main drawback of Pentaho is that it is a much slower tool evolution compared to other BI tools

Difference Between Data Type And Data Structure

Computer programming entirely revolves around data. It is data over which all the business logic gets implemented and it’s the flow of data which comprises the functionality of an application or project. Hence, it becomes critical to organize and store the data for its optimized use and perform effective programming with good data model.

From the surface, both data type and data structure appear to be the same thing, as both deal with the nature and organizing of data, but there is a big difference between the two. One describes the type and nature of data, while the other represents the collections in which that data can be stored.

In this article, we will highlight how a data type is different from a data structure. Let’s start with a basic overview of the two before getting into the differences.

What is Data Type?

The Data Type is the concept that defines the nature of the data or value assigned to a variable in programming. The data type is basically a classification of data. In computer programming, the data type helps the compiler to select a proper machine representation of data.

The implementation of data type is referred to as “abstract implementation“. This means, different programming languages provide the definition of a data type in different ways. The data type does not store any value, but it defines that which type of value can be stored in a variable. Some common data types include− int (integers), float (floating point), char (characters), etc.

What is Data Structure?

A Data Structure is the collection that holds data which can be manipulated and used in programming so that operations and algorithms can be more easily applied. Thus, a data structure is a group of data types. It is a collection of data on which certain types of operations can be executed.

The implementation of data structure is known as “concrete implementation”. It is because, the definition of a data structure is already defined by the programming language. Data structures have the ability to hold different types of data within a single object. Some operations and algorithms like pop, push, etc. are used to assign values to a data structure.

Data structures suffer from the problem of time complexity. Some common examples of data structures include− Tree, Queue, Linked List, etc.

Difference between Data Type and Data Structure

The following table highlights the important differences between a Data Type and a Data Structure −

Key Data Type Data Structure

Definition A data type represents the nature and type of data. All the data that belong to a common data type share some common properties. For example an integer data type describes every integer that the computers can handle. CoA data structure is the collection that holds data which can be manipulated and used in programming so that operations and algorithms can be more easily applied. For example, tree type data structures often allow for efficient searching algorithms.ntent

Implementation Data types are implemented in an abstract manner. Their definitions are provided by different languages in different ways. Data structures are implemented in a concrete manner. Their definition includes what type of data they are going to store and deal with.

Storage Data types don’t store the value of data; they represent only the type of data that is stored. Data structures hold the data along with their values. They occupy space in the main memory of the computer. Also, data structures can hold different types of data within one single object.

Assignment Data types represent the type of value that can be stored, so values can directly be assigned to the data type variables. In case of data structures, the data is assigned using some set of algorithms and operations like push, pop, etc.

Performance There is no issue of time complexity because data types deal only with the type and nature of data. Time complexity plays an important role in data structures because they deal with manipulation and execution of logic over data that it stored.

Conclusion

The most significant difference between a data type and a data structure is that a data type is the representation of nature and type of data, whereas a data structure is a collection that holds different types of data which can be manipulated and used in programming so that different programming logic and operations can be applied in an efficient manner.

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