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Data integration, which combines data from different sources, is essential in today’s data-driven economy because business competitiveness, customer satisfaction and operations depend on merging diverse data sets. As more organizations pursue digital transformation paths – using data integration tools – their ability to access and combine data becomes even more critical.

As data integration combines data from different inputs, it enables the user to drive more value from their data. This is central to Big Data work. Specifically, it provides a unified view across data sources and enables the analysis of combined data sets to unlock insights that were previously unavailable or not as economically feasible to obtain. Data integration is usually implemented in a data warehouse, cloud or hybrid environment where massive amounts of internal and perhaps external data reside.

In the case of mergers and acquisitions, data integration can result in the creation of a data warehouse that combines the information assets of the various entities so that those information assets can be leveraged more effectively.

Data integration platforms integrate enterprise data on-premises, in the cloud, or both. They provide users with a unified view of their data, which enables them to better understand their data assets. In addition, they may include various capabilities such as real-time, event-based and batch processing as well as support for legacy systems and Hadoop.

Although data integration platforms can vary in complexity and difficulty depending on the target audience, the general trend has been toward low-code and no-code tools that do not require specialized knowledge of query languages, programming languages, data management, data structure or data integration.

Importantly, these data integration platforms provide the ability to combine structured and unstructured data from internal data sources, as well as combine internal and external data sources. Structured data is data that’s stored in rows and columns in a relational database. Unstructured data is everything else, such as word processing documents, video, audio, graphics, etc.

In addition to enabling the combination of disparate data, some data integration platforms also enable users to cleanse data, monitor it, and transform it so the data is trustworthy and complies with data governance rules.

ETL platforms that extract data from a data source, transform it into a common format, and load it onto a target destination (may be part of a data integration solution or vice versa). Data integration and ETL tools can also be referred to synonymously.

Data catalogs that enable a common business language and facilitate the discovery, understanding and analysis of information.

Data governance tools that ensure the availability, usability, integrity and security of data.

Data cleansing tools that identify, correct, or remove incomplete, incorrect, inaccurate or irrelevant parts of the data.

Data replication tools capable of replicating data across SQL and NoSQL (relational and non-relational) databases for the purposes of improving transactional integrity and performance.

Data warehouses – centralized data repositories used for reporting and data analysis.

Data migration tools that transport data between computers, storage devices or formats.

Master data management tools that enable common data definitions and unified data management.

Metadata management tools that enable the establishment of policies and processes that ensure information can be accessed, analyzed, integrated, linked, maintained and shared across the organization.

Data connectors that import or export data or convert them to another format.

Data profiling tools for understanding data and its potential uses.

Data integration started in the 1980’s with discussions about “data exchange” between different applications. If a system could leverage the data in another system, then it would not be necessary to replicate the data in the other system. At the time, the cost of data storage was higher than it is today because everything had to be physically stored on-premises since cloud environments were not yet available.

Exchanging or integrating data between or among systems has been a difficult and expensive proposition traditionally since data formats, data types, and even the way data is organized varies from one system to another. “Point-to-point” integrations were the norm until middleware, data integration platforms, and APIs became fashionable. The latter solutions gained popularity over the former because point-to-point integrations are time-intensive, expensive, and don’t scale.

Meanwhile, data usage patterns have evolved from periodic reporting using historical data to predictive analytics. To facilitate more efficient use of data, new technologies and techniques have continued to emerge over time including:

Data warehouses. The general practice was to extract data from different data sources using ETL, transform the data into a common format and load it into a data warehouse. However, as the volume and variety of data continued to expand and the velocity of data generation and use accelerated, data warehouse limitations caused organizations to look for more cost-effective and scalable cloud solutions. While data warehouses are still in use, more organizations increasingly rely on cloud solutions.

Data mapping. The differences in data types and formats necessitated “data mapping,” which makes it easier to understand the relationships between data. For example, D. Smith and David Smith could be the same customer and the differences in references would be attributable to the applications fields in which the data was entered.

Semantic mapping. Another challenge has been “semantic mapping” in which a common reference such as “product” or “customer” holds different meaning in different systems. These differences necessitated ontologies that define schema terms and resolve the differences.

Data lakes. Meanwhile, the explosion of Big Data has resulted in the creation of data lakes that store vast amounts of raw data.

The explosion of enterprise data coupled with the availability of third-party data sets enables insights and predictions that were too difficult, time consuming, or practical to do before. For example, consider the following use cases:

Companies combine data from sales, marketing, finance, fulfillment, customer support and technical support – or some combination of those elements – to understand customer journeys.

Public attractions such as zoos combine weather data with historical attendance data to better predict staffing requirements on specific dates.

Hotels use weather data and data about major events (e.g., professional sports playoff games, championships, or rock concerts) to more precisely allocate resources and maximize profits through dynamic pricing.

Data integration theories are a subset of database theories. They are based on first-order logic, which is a collection of formal systems used in mathematics, philosophy, linguistics and computer science. Data integration theories indicate the difficulty and feasibility of data integration problems.

Data integration is necessary for business competitiveness. Still, particularly in established businesses, data remains locked in systems and difficult to access. To help liberate that data, more types of data integration products have become available. Liberating the data enables companies to better understand:

Their operations and how to improve operational efficiencies.

The competitors.

Their customers and how to improve customer satisfaction/reduce churn.

Partners.

Merger and acquisition targets.

Their target markets and the relative attractiveness of new markets.

How well their products and services are performing and whether the mix of products and services should change.

Business opportunities.

Business risks.

More effective collaboration.

Faster access to combined data sets than traditional methods such as manual integrations.

More comprehensive visibility into and across data assets.

Data syncing to ensure the delivery of timely, accurate data.

Error reduction as opposed to manual integrations.

Higher data quality over time.

Data integration combines data but does not necessarily result in a data warehouse. It provides a unified view of the data; however, the data may reside in different places.

Data integration results in a data warehouse when the data from two or more entities is combined into a central repository.

While data integration tools and techniques have improved over time, organizations can nevertheless face several challenges which can include:

Data created and housed in different systems tends to be in different formats and organized differently.

Data may be missing. For example, internal data may have more detail than external data or data residing in a mainframe may lack time and data information about activities.

Historically, data and applications have been tightly-coupled. That model is changing. Specifically, the application and data layers are being decoupled to enable more flexible data use.

Data integration isn’t just an IT problem; it’s a business problem.

Data itself can be problematic if it’s biased, corrupted, unavailable, or unusable (including uses precluded by data governance).

The data is not available at all or for the specific purpose for which it will be used.

Data use restrictions – whether the data be used at all, or for the specific purpose.

Extraction rules may limit data availability.

Lack of a business purpose. Data integrations should support business objectives.

Service-level integrity falls short of the SLA.

Cost – will one entity bear the cost or will the cost be shared?

Short-term versus long-term value.

Software-related issues (function, performance, quality).

Testing is inadequate.

APIs aren’t perfect. Some are well-documented and functionally sound, while others are not.

Data integration implementations can be accomplished in several different ways including:

Manual integrations between source systems.

Application integrations that require the application publishers overcome the integration challenges of their respective systems.

Common storage integration data from different systems is replicated and stored in a common, independent system.

Middleware which transfers the data integration logic from the application to a separate middleware layer.

Virtual data integration or uniform access integration, which provide views of the data, but data remains in its original repository.

APIs which is a software intermediary that enables applications to communicate and share data.

You're reading What Is Data Integration & How Does It Work?

Handling It And Data Integration In M&A: What The Borg Teaches Us

by Simon Moss

Mergers and acquisitions can enable companies to expand or create new services, enter new geographical markets, and enhance their product portfolios. They represent enormous opportunity. There were 44,000 mergers and acquisitions with a total value of more than $4.5 trillion in 2024 alone, according to the Institute for Mergers, Acquisitions and Alliances. However, it’s the post M&A integration and execution that really delivers the business value.

Mergers, acquisitions and conversion justification is predominately based on competitive synergies, customer base leverage, critical mass in the go-to-market strategy or balance sheet strengthening. Yet these foundational elements to the business thesis are often slowed, or even undermined by the need to physically integrate large numbers of legacy systems and data. The business case is compelling. The execution of that business case is often fraught with integration difficulties, opacity and execution costs that are all too often underestimated in the original business case.

As a result, the value of the merger is often delayed, is amortized over a much longer period of time or the costs of managing the merged or converted entity is much less efficient and valuable. In conversions this risk is usually covered by some impressive legal contract. In M&A it’s straight caveat emptor.

What’s the challenge?

Let’s assume a solid business case and commercial justification are made. The execution then focuses on organization – a cultural assimilation of teams and individuals to rapidly build a joint culture. Some firms are amazing at organizational economies of scale, others encourage a culture of competition between acquired entities and so have considerable organizational overlap. It becomes more of a cultural execution than anything else and often it’s based on strength of leadership and commonly agreed objectives on the merger across both enterprises. Execution when it comes to systems and data integration however is considerably more complex and problematic.

On the whole these activities can be broken into several, though related categories: rationalize the M&A technical architecture; establish common data and reporting standards from the customer, product and pricing information, through to consolidated risk, compliance and AML; standardize systems and applications platforms; and put in place customer management and on-boarding across the new architecture.

The key to a successful M&A and the resulting operational effectiveness of the merger lies in how quickly and effectively IT integration and conversions can be achieved in order to achieve the cost and competitive efficiencies expected.

It’s not easy. The failure rate for M&As is somewhere between 70% and 90% according to Harvard Business Review. The reasons that the expected profits don’t materialize are often complex, but IT and data integration and conversion activities are a big part of it. This IT element is often a multi-year systems integration project, with significant, often misunderstood variable costs and a high degree of risk.

So how do you remove that risk, drive down the cost of the merger and achieve the time to value and margin on a merger?

First, let’s remove the hype in the market and define the problem correctly. To start with, the problem of M&A is not a “Big Data” problem like some say.  Far from it actually.  The idea that enterprises are sold about how to extract value from their data is based on the assumption that most enterprises are technically homogenous like Amazon, Google, or digital retailing, social media companies or other “young” enterprises. This is a pipedream for the vast majority of enterprises.

We must accept that realizing the value of M&A, conversions and a huge category of other business problems are not Big Data challenges and cannot be solved with traditional deployments. Rather these challenge are complex because of distribution and diversity. They are only “Big” when the only way to solve that distribution and diversity problem is to put everything in one place – a self-fulfilling, and ultimately futile approach.

Solving the diversity problem and extracting value despite distribution and diversity

So what if you forget about moving all the data into one central location? What if you halt the impossible task of normalizing data from disparate sources? What if you accept that the complexity is only going to increase over time as companies adopt new cloud services alongside legacy systems? Maybe on M&A, reporting, business intelligence, customer awareness, supply chain transparency and a vast number of other problems, you don’t need to solve a big data problem at all, but rather a diversity and distribution problem.

What if, instead of bringing the data to the analytics, through all the tough integration challenges that that presents, you send the analytics to the data? As a result, the business value, the merger business thesis can be decoupled from the data and systems integration requirements.

The business value – the reason for the M&A action – becomes virtualized, abstracted from the source system and data integration requirements, residing as a “fabric” over both companies’ data centers and technology, targeting data and applications, and only providing information that is needed. A flexible, configurable fabric that can be layered on top of multiple silos across companies can cut to the chase and start extracting value immediately. There’s no need to refactor IT systems and governance control is easy to deploy and maintain. It’s a non-invasive approach that enables you to leverage all of your existing systems.

Staying on the right path

Sure, the technology needs to be integrated eventually. But delivering on the business value first takes the constant pressure off the IT merger, and that can then be done with diligence and rigor, without constant demands for haste from the business.

So an assimilation “fabric” that targets required systems and data, analyzes and creates results, products, even new operating models without the need to centralize, homogenize or move data from operating systems enables an M&A to fulfill business value in weeks, rather than years. The Borg would be most proud.

For more insight on this topic, see an interview with the Author that appeared in Barron’s.

About the Author

What Is Data Lake? It’s Architecture: Data Lake Tutorial

What is Data Lake?

A Data Lake is a storage repository that can store large amount of structured, semi-structured, and unstructured data. It is a place to store every type of data in its native format with no fixed limits on account size or file. It offers high data quantity to increase analytic performance and native integration.

Data Lake is like a large container which is very similar to real lake and rivers. Just like in a lake you have multiple tributaries coming in, a data lake has structured data, unstructured data, machine to machine, logs flowing through in real-time.

Data Lake

The Data Lake democratizes data and is a cost-effective way to store all data of an organization for later processing. Research Analyst can focus on finding meaning patterns in data and not data itself.

Unlike a hierarchal Data Warehouse where data is stored in Files and Folder, Data lake has a flat architecture. Every data elements in a Data Lake is given a unique identifier and tagged with a set of metadata information.

In this tutorial, you will learn-

Why Data Lake?

The main objective of building a data lake is to offer an unrefined view of data to data scientists.

Reasons for using Data Lake are:

With the onset of storage engines like Hadoop storing disparate information has become easy. There is no need to model data into an enterprise-wide schema with a Data Lake.

With the increase in data volume, data quality, and metadata, the quality of analyses also increases.

Data Lake offers business Agility

Machine Learning and Artificial Intelligence can be used to make profitable predictions.

There is no data silo structure. Data Lake gives 360 degrees view of customers and makes analysis more robust.

Data Lake Architecture

Data Lake Architecture

The figure shows the architecture of a Business Data Lake. The lower levels represent data that is mostly at rest while the upper levels show real-time transactional data. This data flow through the system with no or little latency. Following are important tiers in Data Lake Architecture:

Ingestion Tier: The tiers on the left side depict the data sources. The data could be loaded into the data lake in batches or in real-time

Insights Tier: The tiers on the right represent the research side where insights from the system are used. SQL, NoSQL queries, or even excel could be used for data analysis.

HDFS is a cost-effective solution for both structured and unstructured data. It is a landing zone for all data that is at rest in the system.

Distillation tier takes data from the storage tire and converts it to structured data for easier analysis.

Processing tier run analytical algorithms and users queries with varying real time, interactive, batch to generate structured data for easier analysis.

Unified operations tier governs system management and monitoring. It includes auditing and proficiency management, data management, workflow management.

Key Data Lake Concepts

Following are Key Data Lake concepts that one needs to understand to completely understand the Data Lake Architecture

Key Concepts of Data Lake

Data Ingestion

Data Ingestion allows connectors to get data from a different data sources and load into the Data lake.

Data Ingestion supports:

All types of Structured, Semi-Structured, and Unstructured data.

Multiple ingestions like Batch, Real-Time, One-time load.

Many types of data sources like Databases, Webservers, Emails, IoT, and FTP.

Data Storage

Data storage should be scalable, offers cost-effective storage and allow fast access to data exploration. It should support various data formats.

Data Governance

Data governance is a process of managing availability, usability, security, and integrity of data used in an organization.

Security

Security needs to be implemented in every layer of the Data lake. It starts with Storage, Unearthing, and Consumption. The basic need is to stop access for unauthorized users. It should support different tools to access data with easy to navigate GUI and Dashboards.

Authentication, Accounting, Authorization and Data Protection are some important features of data lake security.

Data Quality:

Data quality is an essential component of Data Lake architecture. Data is used to exact business value. Extracting insights from poor quality data will lead to poor quality insights.

Data Discovery

Data Discovery is another important stage before you can begin preparing data or analysis. In this stage, tagging technique is used to express the data understanding, by organizing and interpreting the data ingested in the Data lake.

Data Auditing

Two major Data auditing tasks are tracking changes to the key dataset.

Tracking changes to important dataset elements

Captures how/ when/ and who changes to these elements.

Data auditing helps to evaluate risk and compliance.

Data Lineage

This component deals with data’s origins. It mainly deals with where it movers over time and what happens to it. It eases errors corrections in a data analytics process from origin to destination.

Data Exploration

It is the beginning stage of data analysis. It helps to identify right dataset is vital before starting Data Exploration.

All given components need to work together to play an important part in Data lake building easily evolve and explore the environment.

Maturity stages of Data Lake

The Definition of Data Lake Maturity stages differs from textbook to other. Though the crux remains the same. Following maturity, stage definition is from a layman point of view.

Maturity stages of Data Lake

Stage 1: Handle and ingest data at scale

This first stage of Data Maturity Involves improving the ability to transform and analyze data. Here, business owners need to find the tools according to their skillset for obtaining more data and build analytical applications.

Stage 2: Building the analytical muscle

This is a second stage which involves improving the ability to transform and analyze data. In this stage, companies use the tool which is most appropriate to their skillset. They start acquiring more data and building applications. Here, capabilities of the enterprise data warehouse and data lake are used together.

Stage 3: EDW and Data Lake work in unison

This step involves getting data and analytics into the hands of as many people as possible. In this stage, the data lake and the enterprise data warehouse start to work in a union. Both playing their part in analytics

Stage 4: Enterprise capability in the lake

In this maturity stage of the data lake, enterprise capabilities are added to the Data Lake. Adoption of information governance, information lifecycle management capabilities, and Metadata management. However, very few organizations can reach this level of maturity, but this tally will increase in the future.

Best practices for Data Lake Implementation:

Architectural components, their interaction and identified products should support native data types

Design of Data Lake should be driven by what is available instead of what is required. The schema and data requirement is not defined until it is queried

Design should be guided by disposable components integrated with service API.

Data discovery, ingestion, storage, administration, quality, transformation, and visualization should be managed independently.

The Data Lake architecture should be tailored to a specific industry. It should ensure that capabilities necessary for that domain are an inherent part of the design

Faster on-boarding of newly discovered data sources is important

Data Lake helps customized management to extract maximum value

The Data Lake should support existing enterprise data management techniques and methods

Challenges of building a data lake:

In Data Lake, Data volume is higher, so the process must be more reliant on programmatic administration

It is difficult to deal with sparse, incomplete, volatile data

Wider scope of dataset and source needs larger data governance & support

Difference between Data lakes and Data warehouse

Parameters Data Lakes Data Warehouse

Data Data lakes store everything. Data Warehouse focuses only on Business Processes.

Processing Data are mainly unprocessed Highly processed data.

Type of Data It can be Unstructured, semi-structured and structured. It is mostly in tabular form & structure.

Task Share data stewardship Optimized for data retrieval

Agility Highly agile, configure and reconfigure as needed. Compare to Data lake it is less agile and has fixed configuration.

Users Data Lake is mostly used by Data Scientist Business professionals widely use data Warehouse

Storage Data lakes design for low-cost storage. Expensive storage that give fast response times are used

Security Offers lesser control. Allows better control of the data.

Replacement of EDW Data lake can be source for EDW Complementary to EDW (not replacement)

Schema Schema on reading (no predefined schemas) Schema on write (predefined schemas)

Data Processing Helps for fast ingestion of new data. Time-consuming to introduce new content.

Data Granularity Data at a low level of detail or granularity. Data at the summary or aggregated level of detail.

Tools Can use open source/tools like Hadoop/ Map Reduce Mostly commercial tools.

Benefits and Risks of using Data Lake:

Here are some major benefits in using a Data Lake:

Offers cost-effective scalability and flexibility

Offers value from unlimited data types

Reduces long-term cost of ownership

Allows economic storage of files

Quickly adaptable to changes

Users, from various departments, may be scattered around the globe can have flexible access to the data

Risk of Using Data Lake:

After some time, Data Lake may lose relevance and momentum

There is larger amount risk involved while designing Data Lake

Unstructured Data may lead to Ungoverned Chao, Unusable Data, Disparate & Complex Tools, Enterprise-Wide Collaboration, Unified, Consistent, and Common

It also increases storage & computes costs

There is no way to get insights from others who have worked with the data because there is no account of the lineage of findings by previous analysts

The biggest risk of data lakes is security and access control. Sometimes data can be placed into a lake without any oversight, as some of the data may have privacy and regulatory need

Summary:

A Data Lake is a storage repository that can store large amount of structured, semi-structured, and unstructured data.

The main objective of building a data lake is to offer an unrefined view of data to data scientists.

Unified operations tier, Processing tier, Distillation tier and HDFS are important layers of Data Lake Architecture

Data Ingestion, Data storage, Data quality, Data Auditing, Data exploration, Data discover are some important components of Data Lake Architecture

Design of Data Lake should be driven by what is available instead of what is required.

Data Lake reduces long-term cost of ownership and allows economic storage of files

The biggest risk of data lakes is security and access control. Sometimes data can be placed into a lake without any oversight, as some of the data may have privacy and regulatory need.

What Is Google Data Studio? (Beginners Guide)

Do you want to customize and visualize your data from a variety of data sources?

Google Data Studio (now called Looker Studio) is free dashboarding software that helps you easily connect your sources, update your data, and generate meaningful reports. 

What Is Google Data Studio?

For an experienced marketer, it’s quite easy to analyze data in the tool where it is collected.

But clients are more interested in knowing results. Rather than look at actual data, they want to see the big picture. In that case, it’s better to display customized reports based on their requirements instead of overwhelming them with data.

And this is where Google Data Studio comes into the picture. With this tool, you can simply connect data sources like Google Analytics, Google Ads, or Facebook Ads and update your data reports automatically.

This brings all your tracking data in one place. All you have to do is drag and drop any of the preferred charts on your report and configure it to your needs. 

Moreover, you can also style your dashboard by using built-in dropdowns and filters to make it interactive. 

If you’re new to Google Data Studio, we have an in-depth beginner guide to help you get started!

And since it runs on a web browser, you can easily share your work and collaborate with your colleagues. 

Let’s see how! 

Sharing Data from Google Data Studio

Once you have created your dashboard, you can either send out a static version of your dashboard via the Email with a PDF option, or you can share access to the online version. 

The online version will automatically keep itself up to date, and it will maintain any interactivity that you’ve built in with dropdowns or filters!

The best part about sharing your dashboard is that your clients or colleagues won’t need access to the data source. Instead, they’ll be able to view meaningful representations of the data through your Google Data Studio dashboard.

Google Data Studio Functionalities

There is more to this tool’s functionalities than just creating custom reports.

It provides a one-stop solution to present data from various sources, including Google BigQuery. By connecting to Google BigQuery, you’ll be able to extract data from your own data warehouse. This is a very helpful feature for marketers who collect huge amounts of data from different sources using different tools.

You can also combine data with the data blending feature, then analyze aggregated data by using case statements and calculated fields. This can help you (and clients) see the bigger picture behind your data.

Lastly, you can create more sophisticated, interactive reports by adding tooltips, date filters, and pivot tables to your data visualizations.

Thus, Google Data Studio’s utility doesn’t end at creating stylized, custom reports; it can also help you combine, process, and convey data that is collected from varied sources.

FAQ How does Google Data Studio work?

Google Data Studio allows you to connect multiple data sources such as Google Analytics, Google Ads, Facebook Ads, and more. You can drag and drop charts onto your report and configure them according to your needs. The tool also provides built-in dropdowns and filters to style your dashboard and make it interactive.

How can I share data from Google Data Studio?

You can share your Google Data Studio dashboard in two ways. First, you can send out a static version of your dashboard via email as a PDF. Second, you can share access to the online version of the dashboard, which will automatically update itself and maintain any interactivity you’ve added with dropdowns or filters. This allows your clients or colleagues to view meaningful representations of the data without needing access to the data source.

What are the additional functionalities of Google Data Studio?

Google Data Studio offers more than just creating custom reports. It provides a one-stop solution for presenting data from various sources, including Google BigQuery. You can extract data from your own data warehouse by connecting to Google BigQuery. The tool also allows you to combine data using the data blending feature and analyze aggregated data using case statements and calculated fields. Additionally, you can enhance your reports with tooltips, date filters, and pivot tables to create more interactive visualizations.

Summary 

In short, Google Data Studio is a tool that can aggregate all of your tracking data in one place, then synthesize them into a customized data report that you can share with clients and colleagues. 

Data Automation In 2023: What It Is & Why You Need It

What is data automation? 

Data automation refers to optimizing data uploading and delivery procedures using automation tools that eliminate manual work. The traditional practice required manual labor from the IT department to manage and administer data updates on open data portals. Sometimes the responsibility would fall on the employees in different departments that had to handle the data while carrying on their other duties. The manual process is time-consuming and labor-intensive for enterprises. In addition, manual handling of data is error-prone and can affect strategic business insights derived from the data. Hence, data automation is a vital tool for any enterprise looking to upgrade its data integration to a more efficient level.

Do you need data automation in your business?

There are four clues to look for when deciding if you need to automate data automation in your business:

What are the approaches to data automation?

ETL is one of the most common data engineering approaches used by data professionals. According to the procedure, the data automation process includes three steps based on the function of the used tools. These three stages are commonly known by the abbreviation of ETL (Extract, Transform, Load). These stages include:

Roadmap to Data Automation 

Identify problems: Determine where the repetition occurs and prioritize the data sets based on their added value. It is significant to prioritize the datasets that create the most value for the company as they take more manual effort. 

Define data ownership within the organization: Determine which teams will handle different stages of the data automation process. There are three main approaches to data access and processing within an organization: 

With the centralized approach, the IT team handles the data automation process from A to Z. 

In a decentralized method, each agency processes their data, from extracting the data from source systems to loading them to data portals. 

There is also a combination of the two methods. The hybrid method allows different departments to work with the IT team. IT teams are responsible for loading the data into data portals through a hybrid approach.  

Define the required format for your data transformation: Define the required format for your data transformation. It is crucial to have a set data format policy, to secure data coherence for better insights. Moreover, ETL tools require users to define the preferred formatting of the data categorization.

Schedule updates: Dataset update allows businesses to make better decisions on their operations. Hence, It is crucial to schedule updates for consistent and up-to-date data for datasets. 

Determine the right vendors for your operations: Businesses can rely on automation consultants’ expertise to help them identify the best vendor according to the business needs and the business structure. 

Explore our ETL tools list if you believe your data integration tasks may benefit from automation.

To gain a more comprehensive overview of workload automation, download our whitepaper on the topic:

Contact us for guidance on the process: If you need more customized recommendations.

Cem regularly speaks at international technology conferences. He graduated from Bogazici University as a computer engineer and holds an MBA from Columbia Business School.

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Github Integration With Selenium: Complete Tutorial

Git Hub is a Collaboration platform. It is built on top of git. It allows you to keep both local and remote copies of your project. A project which you can publish it among your team members as they can use it and update it from there itself.

Advantages of Using Git Hub For Selenium.

When multiple people work on the same project they can update project details and inform other team members simultaneously.

Jenkins can help us to regularly build the project from the remote repository this helps us to keep track of failed builds.

In this tutorial, you will learn

Before we start selenium and git hub integration, we need to install the following components.

Jenkins Installation.

Maven Installation.

Tomcat Installation.

You can find this installation steps in the following links:

Git Binaries Installation

Now let us start by installing “Git Binaries”.

Step 2) Download the latest stable release.

Step 4) Go to the download location or icon and run the installer.

Another window will pop up,

Step 8) In this step,

Select the Directory where you want to install “Git Binaries” and

Step 11) In this step,

Select Use Git from the Windows Command Prompt to run Git from the command line and

Step 12) In this step,

Select Use Open SSH It will help us to execute the command from the command line, and it will set the environmental path.

Step 13) In this step,

Select “Checkout windows-style, commit Unix-style line ending”.(how the git hub should treat line endings in text files).

Step 14) In this step,

Select Use MinTTY is the default terminal of MSys2 for Git Bash

Once git is installed successfully, you can access the git.

Open Command prompt and type “git” and hit “Enter” If you see below screen means it is installed successfully

Jenkins Git Plugin Install

Now let’s start with Jenkins Git Plugin Installation.

Step 1) Launch the Browser and navigate to your Jenkins.

Step 5) In this step,

Select GitHub plugin then

Now it will install the following plugins.

Once the Installation is finished. Restart your Tomcat server by calling the “shutdown.bat” file

After Restarting the tomcat and Jenkins we can see plugins are installed in the “Installed” TAB.

Setting Up our Eclipse with GitHub Plugin

Now let’s install GitHub Plugin for Eclipse.

Step 1) Launch Eclipse and then

Step 3) In this step,

Type the name “EGIT” and

Then restart the eclipse.

Building a repository on Git

Step 3) In this step,

Enter the name of the repository and

Testing Example Of Using Selenium with Git Hub.

Step 1) Once we are done with the new repository, Launch Eclipse

Step 2) In this step,

Select Maven Project and browse the location.

Step 3) In this step,

Select project name and location then

Step 5) In this step,

Enter Group Id and

Artifact Id and

Step 6)

Now let’s create a sample script

Let’s push the code/local repository to Git Hub.

Step 7) In this step,

Open eclipse and then navigate to the project

Select share project

In this step,

Select the local repository and

Now it’s time to push our code to Git Hub Repository

Step 9) In this step,

Step 10) In this step,

Enter a commit message and

Select the files which we want to send to Git Hub repository

Once you are done with it, you could see the icons in the project is being changed it says that we have successfully pushed and committed our code to Git Hub

We can verify in the Git hub in the repository that our project is successfully pushed into repository

Now it’s time for executing our project from Git Hub in Jenkins

Step 11) Launch browser and open your Jenkins.

Step 13) In this step,

Enter Item name

Select Maven Project

Step 14) In this step, we will configure Git Hub in Jenkins

Enter the Repository URI

If you have multiple repositories in Git Hub, you need to add name Refspec field of the repository.

We can get the URI in Git Hub

Step 15) In this step,

Add the chúng tôi file location in the textbox and

Specify the goals and options for Maven then

Select option on how to run the test

Finally, we can verify that our build is successfully completed/executed.

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