Ben Watson

Ben Watson


QuickBooks Auto Data Recovery- Recover Lost & Damaged Data

As we all are aware, how important it is to keep the backup of the data file containing all your financial information, and slighter loss in the data can affect the overall business operations. Thus, it hampers the work and can cause a severe loss to the company. However, to deal with such dreadful instances, you must access the QuickBooks Auto Data Recovery Tool. This tool automatically creates a local backup of the data file. In this blog, we have explained all the facts and details required to access this feature.

Wondering how to recover lost data with the help of QuickBooks Auto Data Recovery? Don’t worry and get in touch with our experts by dialing the Toll-Free number 855-856-0053.

How can you recover the lost data by Auto Data Recovery?

Here we have listed alternative methods that will help you to recover your lost company data using the Auto Data Recovery tool quickly.

Method 1: Use .TLG file with .QBW.adr file

  • The first and foremost step is to make a new folder on the desktop named “QBTest” and then launch the folder where you have placed the data file.
  • Soon after this, search for the.TLG file from the data file’s folder. If you are unable to locate the .tlg file, you need to perform the following steps:
    1. Go to the Windows File Explorer, click Organize, followed by the Folder and search options.
    2. After this, select Hide extensions for known file types > Apply > OK.
  • Now, copy the corresponding “.TLG” File of the data file and then paste it to the QBTest folder.
  • After this, launch the QuickBooksAutoDataRecovery folder and then copy the .QBW.adr file and paste it to the QBTest folder. Ensure that you must have a .QBW.adr and .tlg file on your QBTest folder.
  • Go to the QBTest folder, right-click the File named “.QBW.adr” and click Rename.
  • Remove the file extension .adr at the end of the file.
  • Launch QB application, and then open the data file stored in the QBTest folder. Further, access the account register to check whether all transactions are there.
  • Subsequently, click File> Utilities and then Verify Data to verify the recovered file’s data integrity.
  • If you find that this copy of the data file is good, you can transfer the damaged data file to another location and then transmit the copy from QBTest to its original location.

Method 2: Utilize .QBW.adr and .TLG.adr files

  • To begin with, you must make a new folder QBTest on the desktop and then launch the folder where you have stored the data file. Then, navigate to the QuickBooksAutoDataRecovery folder.
  • Afterwards, search for the .TLG.adr and .QBW.adr files.
  • You are required to copy the.TLG.adr and .QBW.adr files and then paste them to the QBTest folder.
  • From the QBTest folder, delete the file extension .adr from the end of the file name and then open QuickBooks.
  • Also, open the data file stored on the QBTest folder and access the account to check whether all the transactions are there or not.
  • To test the data integrity of the recovered file, go to File > Utilities and then click Verify Data.
  • If this copy of the data file seems to be good, you can move the damaged company file to another location. Later, transmit the copy from QBTest to its original location.

Hopefully, we assure you that the methods mentioned above will help you recover the lost data file using QuickBooks Auto Data Recovery. If still the issue persists, consult the experts by placing a call at the support number 855-856-0053.

#quickbooks auto data recovery

What is GEEK

Buddha Community

QuickBooks Auto Data Recovery- Recover Lost & Damaged Data

Noah Green


There is a lot of third-party software available on the internet, but if you want a recommendation, I would suggest using this expert Quickbooks Data Recovery Service to fix any corrupted or damaged Quickbooks files. It can retrieve Quickbooks data that has been deleted or lost. It can also automatically correct all Quickbooks problems. This software allows users to pick single or several files based on their needs, and then the software repairs all of the selected Quickbooks data. It generates a log report with full information on each and every step of the recovery process, which you may save to any location.

 iOS App Dev

iOS App Dev


Your Data Architecture: Simple Best Practices for Your Data Strategy

If you accumulate data on which you base your decision-making as an organization, you should probably think about your data architecture and possible best practices.

If you accumulate data on which you base your decision-making as an organization, you most probably need to think about your data architecture and consider possible best practices. Gaining a competitive edge, remaining customer-centric to the greatest extent possible, and streamlining processes to get on-the-button outcomes can all be traced back to an organization’s capacity to build a future-ready data architecture.

In what follows, we offer a short overview of the overarching capabilities of data architecture. These include user-centricity, elasticity, robustness, and the capacity to ensure the seamless flow of data at all times. Added to these are automation enablement, plus security and data governance considerations. These points from our checklist for what we perceive to be an anticipatory analytics ecosystem.

#big data #data science #big data analytics #data analysis #data architecture #data transformation #data platform #data strategy #cloud data platform #data acquisition

Gerhard  Brink

Gerhard Brink


Getting Started With Data Lakes

Frameworks for Efficient Enterprise Analytics

The opportunities big data offers also come with very real challenges that many organizations are facing today. Often, it’s finding the most cost-effective, scalable way to store and process boundless volumes of data in multiple formats that come from a growing number of sources. Then organizations need the analytical capabilities and flexibility to turn this data into insights that can meet their specific business objectives.

This Refcard dives into how a data lake helps tackle these challenges at both ends — from its enhanced architecture that’s designed for efficient data ingestion, storage, and management to its advanced analytics functionality and performance flexibility. You’ll also explore key benefits and common use cases.


As technology continues to evolve with new data sources, such as IoT sensors and social media churning out large volumes of data, there has never been a better time to discuss the possibilities and challenges of managing such data for varying analytical insights. In this Refcard, we dig deep into how data lakes solve the problem of storing and processing enormous amounts of data. While doing so, we also explore the benefits of data lakes, their use cases, and how they differ from data warehouses (DWHs).

This is a preview of the Getting Started With Data Lakes Refcard. To read the entire Refcard, please download the PDF from the link above.

#big data #data analytics #data analysis #business analytics #data warehouse #data storage #data lake #data lake architecture #data lake governance #data lake management

Cyrus  Kreiger

Cyrus Kreiger


How Has COVID-19 Impacted Data Science?

The COVID-19 pandemic disrupted supply chains and brought economies around the world to a standstill. In turn, businesses need access to accurate, timely data more than ever before. As a result, the demand for data analytics is skyrocketing as businesses try to navigate an uncertain future. However, the sudden surge in demand comes with its own set of challenges.

Here is how the COVID-19 pandemic is affecting the data industry and how enterprises can prepare for the data challenges to come in 2021 and beyond.

#big data #data #data analysis #data security #data integration #etl #data warehouse #data breach #elt

Macey  Kling

Macey Kling


Applications Of Data Science On 3D Imagery Data

CVDC 2020, the Computer Vision conference of the year, is scheduled for 13th and 14th of August to bring together the leading experts on Computer Vision from around the world. Organised by the Association of Data Scientists (ADaSCi), the premier global professional body of data science and machine learning professionals, it is a first-of-its-kind virtual conference on Computer Vision.

The second day of the conference started with quite an informative talk on the current pandemic situation. Speaking of talks, the second session “Application of Data Science Algorithms on 3D Imagery Data” was presented by Ramana M, who is the Principal Data Scientist in Analytics at Cyient Ltd.

Ramana talked about one of the most important assets of organisations, data and how the digital world is moving from using 2D data to 3D data for highly accurate information along with realistic user experiences.

The agenda of the talk included an introduction to 3D data, its applications and case studies, 3D data alignment, 3D data for object detection and two general case studies, which are-

  • Industrial metrology for quality assurance.
  • 3d object detection and its volumetric analysis.

This talk discussed the recent advances in 3D data processing, feature extraction methods, object type detection, object segmentation, and object measurements in different body cross-sections. It also covered the 3D imagery concepts, the various algorithms for faster data processing on the GPU environment, and the application of deep learning techniques for object detection and segmentation.

#developers corner #3d data #3d data alignment #applications of data science on 3d imagery data #computer vision #cvdc 2020 #deep learning techniques for 3d data #mesh data #point cloud data #uav data

Uriah  Dietrich

Uriah Dietrich


What Is ETLT? Merging the Best of ETL and ELT Into a Single ETLT Data Integration Strategy

Data integration solutions typically advocate that one approach – either ETL or ELT – is better than the other. In reality, both ETL (extract, transform, load) and ELT (extract, load, transform) serve indispensable roles in the data integration space:

  • ETL is valuable when it comes to data quality, data security, and data compliance. It can also save money on data warehousing costs. However, ETL is slow when ingesting unstructured data, and it can lack flexibility.
  • ELT is fast when ingesting large amounts of raw, unstructured data. It also brings flexibility to your data integration and data analytics strategies. However, ELT sacrifices data quality, security, and compliance in many cases.

Because ETL and ELT present different strengths and weaknesses, many organizations are using a hybrid “ETLT” approach to get the best of both worlds. In this guide, we’ll help you understand the “why, what, and how” of ETLT, so you can determine if it’s right for your use-case.

#data science #data #data security #data integration #etl #data warehouse #data breach #elt #bid data