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Visual Importer Enterprise Serial key

Visual Importer Enterprise Serial key

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Visual Importer ETL Enterprise 64 Bit - Windows 8 Downloads

Visual Importer ETL Enterprise 64 Bit

An ETL tool automates database loading

Visual Importer Enterprise automates loading data into ANY Database from ANY Database or file. Plus automation of business processes such as FTP, Email: POP3 and SMTP, File operations, SQL scripts, Zip and Unzip. Works directly with most Databases

Visual Importer ETL Enterprise 64 Bit - Key details

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Visual Importer is an ETL device for putting away information. ETL is the name of the words Extract Transform Load, which means removing, refining, and stacking data. In the information stockroom territory, information distribution center is the place distinctive association information is put away after refinement. The data put away in the information stockroom is at last utilized for vital basic leadership by the supervisors of an association, while the database and even an ERP framework might be one of the crude, refined contributions for the information distribution center. Putting away information in an information distribution center requires a few stages: In the Extract step, crude information is first gathered from at least one distinct source. In the Transform neighborhood, information is refined and fit to be put away in the distribution center, and in the last advance, Load, the refined data is put away in the information stockroom with no change.Get visual importer enterprise license crack free downlaod.

Numerous product instruments have been created for this, including Visual Importer, one of the most dominant applications in the field. It incorporates graphical devices and wizards for building and troubleshooting computerization bundles, FTP tasks, executing SQL proclamations, overseeing documents, and removing information from different information sources. In contrast to DTS, SSIS and Oracle Warehouse build apparatuses, it can likewise send and get messages and connections. Visual Importer has helped several organizations mechanize their business procedures and day by day errands by joining diverse ETL ventures into a solitary programming bundle.

Visual Importer Enterprise Features key

  • Bringing in information to various databases
  • Execute SQL contents
  • Pack records and unfasten them
  • Pack records utilizing MD5
  • Duplicate, cut, rename and erase records
  • Transfer and download records
  • Get and process their messages and connections

Visual Importer Enterprise Technical Details and System Requirements

  • File Name: Visual Importer Enterprise
  • File Size: 68 MB
  • Laest Version: v8.3.10.29
  • License: Shareware
  • Setup Format: Exe
  • Setup Type: Offline Installer/Standalone Setup.
  • Supported OS: Windows
  • Minimum RAM: 2GB
  • Space: 100 MB
  • Developers:Etl-tools

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The Bangladesh-based pharma company is striding forward with its mission to make life-saving drugs available and affordable for people in every part of the world.

In November 2021, Bangladesh-based Beximco Pharmaceuticals made international headlines with the launch of the world’s first generic molnupiravir, an oral antivirul drug for the treatment of patients with mild to moderate forms of Covid-19 that was recently developed by U.S. firms Merck, Sharp & Dohme (MSD) and Ridgeback Biotherapeutics. Molnupiravir is a major achievement in bringing forward breakthrough medicines to address the world’s current greatest health challenge. Interim data published by MSD shows it reduces the risk of hospitalisation and death by around 50%. Beximco’s branded generic version of molnupiravir is beingmarketed as Emorivir.

This follows on from Beximco’s May 2020 launch, at the height of the pandemic, of the world’s first generic version of remdesivir—branded as Bemsivir—an antiviral drug developed by U.S. firm Gilead Sciences that has been effective in treating Covid-19 patients.

Beximco was allowed to produce these generic copies under a pharmaceutical patent waiver granted by the WTO’s Trade-Related Aspects of Intellectual Property Rights (TRIPS) for the least developed countries. The company, which is considered a pioneer in providing access to breakthrough drugs at affordable prices, leveraged its competitive cost advantages and strong experience to be able to make these potentially life-saving treatment options at substantially cheaper prices than the originator brands.

“Further to our launch of the first generic remdesivir at the start of the pandemic, the launch of a generic version of molnupiravir is another example of Beximco Pharma’s ability to rapidly respond to make affordable treatments available to patients suffering from Covid-19,” said Nazmul Hassan MP, Managing Director of Beximco Pharmaceuticals. “This is a great achievement for the company and one which we believe could play an important role in combating the pandemic, especially in low- and middle-income countries where access to vaccinnes has been limited.”

Over the past 12 months, Beximco has provided Bemsivir to public and private healthcare facilities in Bangladesh, and has also donated large quantities of the drug in several other countries. To date, the company has supplied Bemsivir to 22 countries including India, Azerbaijan, Pakistan, Nigeria, the Philippines, Venezuela and Lebanon.

Exports to 50 Countries

Founded in 1978, Beximco started out importing medicines from multinational corporations (MNCs) such as U.S.-based Upjohn and Germany’s Bayer, before manufacturing the drugs locally under license. Today, Beximco has emerged as a leading exporter of medicines, with a global footprint in 50 countries around the world. Its success story is built on its unwavering commitment to quality and the dedication of its 5,000-strong workforce, driven by the company’s aspiration to be among the world’s most admired pharmaceutical companies.

Beximco began its export operations in 1992, exporting active pharmaceutical ingredients (APIs) to Hong Kong, with Russia becoming its first export destination for formulation products the following year. Since then, the company has gradually expanded its overseas business, entering Singapore, one of the most stringent markets in Asia, in 2001. As a testament to its success, the company has won Bangladesh’s prestigious National Export Trophy (Gold) five times for its outstanding contribution to the country’s export.

Spanning an area of 23 acres in Dhaka, Bangladesh, Beximco’s state-of-the-art manufacturing facilities have been accredited by regulatory authorities in Australia, Canada, Europe, the Middle East and the U.S., among others. Through these facilities, the company has made great strides in its ability to produce high-quality drugs at prices up to 99% cheaper than their branded counterparts, thus making treatments and medicines accessible to millions of patients in developing countries.

In 2015, the company launched the world’s first generic version of Harvoni (Sofosbuvir plus Ledipasvir), the revolutionary drug to treat hepatitis C, and began selling it for around US$10 versus the originator’s price of US$1,130. It did the same when it launched the generic version of another groundbreaking hepatitis C drug, Sovaldi (Sofosbuvir).

Covid-19 Pledge

Out-of-pocket expenditure accounts for the bulk of the healthcare expenses in most low- and middle-income countries where access to breakthrough and highly expensive treatments is almost impossible. Since the beginning of the pandemic, there has been an urgent need to find immediate solutions or medical interventions to save human lives. Rising to the challenge, in November 2020 Beximco and 17 leading global generic drug companies pledged to work together via the United Nations-backed Medicines Patent Pool (MPP) to accelerate access to Covid-19 treatments for low- and middle-income countries. Among the other signatories to the MPP pledge are world-leading generic manufacturers such as Lupin, Aurobindo Pharma, Zydus Cadila, Dr Reddy’s Laboratories, Sun Pharmaceutical Industries and Celltrion. 

Bangladesh’s pharmaceutical industry has been at the forefront of driving the nation’s progress, with the country transforming itself from a net importer of medicines to an exporting nation over the past three decades—and Beximco has played a pioneering role. At present, Beximco is the country’s sole exporter of medicines to the U.S., which is also the largest export market for the company.

Looking to the future, Beximco aims to strengthen its presence in key emerging and developed markets. The company is also building a robust pipeline of value-added generic products for these markets, including a differentiated portfolio of metered dose inhalers, dry-powder inhalers and sterile ophthalmics. By collaborating with leading MNCs, it has developed new skills and conceived and implemented advanced, state-of-the-art technologies.

Rising healthcare costs have become a major challenge globally, with the high cost of medicines a serious concern for governments around the world. To address this, governments are promoting the use of generic drugs, which creates huge opportunities for generic drug producers like Beximco.

With its robust and highly compliant infrastructure, cost competitiveness, diverse portfolio and skilled manpower, Beximco has already emerged as an important generic drug player in Asia. As patents for branded or originator drugs expire, Beximco will be able to reinforce its differentiated value proposition, taking the opportunity to produce generic versions at significant scale and at a much lower cost, touching the lives of millions around the world by providing affordable access to life-saving medicines.

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Extract, transform, load

In computing, extract, transform, load (ETL) is the general procedure of copying data from one or more sources into a destination system which represents the data differently from the source(s) or in a different context than the source(s). The ETL process became a popular concept in the 1970s and is often used in data warehousing.[1]

Data extraction involves extracting data from homogeneous or heterogeneous sources; data transformation processes data by data cleaning and transforming it into a proper storage format/structure for the purposes of querying and analysis; finally, data loading describes the insertion of data into the final target database such as an operational data store, a data mart, data lake or a data warehouse.[2][3]

A properly designed ETL system extracts data from the source systems, enforces data quality and consistency standards, conforms data so that separate sources can be used together, and finally delivers data in a presentation-ready format so that application developers can build applications and end users can make decisions.[4]

Since the data extraction takes time, it is common to execute the three phases in pipeline. While the data is being extracted, another transformation process executes while processing the data already received and prepares it for loading while the data loading begins without waiting for the completion of the previous phases.

ETL systems commonly integrate data from multiple applications (systems), typically developed and supported by different vendors or hosted on separate computer hardware. The separate systems containing the original data are frequently managed and operated by different employees. For example, a cost accounting system may combine data from payroll, sales, and purchasing.


The first part of an ETL process involves extracting the data from the source system(s). In many cases, this represents the most important aspect of ETL, since extracting data correctly sets the stage for the success of subsequent processes. Most data-warehousing projects combine data from different source systems. Each separate system may also use a different data organization and/or format. Common data-source formats include relational databases, XML, JSON and flat files, but may also include non-relational database structures such as Information Management System (IMS) or other data structures such as Virtual Storage Access Method (VSAM) or Indexed Sequential Access Method (ISAM), or even formats fetched from outside sources by means such as web spidering or screen-scraping. The streaming of the extracted data source and loading on-the-fly to the destination database is another way of performing ETL when no intermediate data storage is required.

An intrinsic part of the extraction involves data validation to confirm whether the data pulled from the sources has the correct/expected values in a given domain (such as a pattern/default or list of values). If the data fails the validation rules, it is rejected entirely or in part. The rejected data is ideally reported back to the source system for further analysis to identify and to rectify the incorrect records.


In the data transformation stage, a series of rules or functions are applied to the extracted data in order to prepare it for loading into the end target.

An important function of transformation is data cleansing, which aims to pass only "proper" data to the target. The challenge when different systems interact is in the relevant systems' interfacing and communicating. Character sets that may be available in one system may not be so in others.

In other cases, one or more of the following transformation types may be required to meet the business and technical needs of the server or data warehouse:

  • Selecting only certain columns to load: (or selecting null columns not to load). For example, if the source data has three columns (aka "attributes"), roll_no, age, and salary, then the selection may take only roll_no and salary. Or, the selection mechanism may ignore all those records where salary is not present (salary = null).
  • Translating coded values: (e.g., if the source system codes male as "1" and female as "2", but the warehouse codes male as "M" and female as "F")
  • Encoding free-form values: (e.g., mapping "Male" to "M")
  • Deriving a new calculated value: (e.g., sale_amount = qty * unit_price)
  • Sorting or ordering the data based on a list of columns to improve search performance
  • Joining data from multiple sources (e.g., lookup, merge) and deduplicating the data
  • Aggregating (for example, rollup — summarizing multiple rows of data — total sales for each store, and for each region, etc.)
  • Generating surrogate-key values
  • Transposing or pivoting (turning multiple columns into multiple rows or vice versa)
  • Splitting a column into multiple columns (e.g., converting a comma-separated list, specified as a string in one column, into individual values in different columns)
  • Disaggregating repeating columns
  • Looking up and validating the relevant data from tables or referential files
  • Applying any form of data validation; failed validation may result in a full rejection of the data, partial rejection, or no rejection at all, and thus none, some, or all of the data is handed over to the next step depending on the rule design and exception handling; many of the above transformations may result in exceptions, e.g., when a code translation parses an unknown code in the extracted data


The load phase loads the data into the end target, which can be any data store including a simple delimited flat file or a data warehouse.[5] Depending on the requirements of the organization, this process varies widely. Some data warehouses may overwrite existing information with cumulative information; updating extracted data is frequently done on a daily, weekly, or monthly basis. Other data warehouses (or even other parts of the same data warehouse) may add new data in a historical form at regular intervals — for example, hourly. To understand this, consider a data warehouse that is required to maintain sales records of the last year. This data warehouse overwrites any data older than a year with newer data. However, the entry of data for any one year window is made in a historical manner. The timing and scope to replace or append are strategic design choices dependent on the time available and the business needs. More complex systems can maintain a history and audit trail of all changes to the data loaded in the data warehouse.[6]

As the load phase interacts with a database, the constraints defined in the database schema — as well as in triggers activated upon data load — apply (for example, uniqueness, referential integrity, mandatory fields), which also contribute to the overall data quality performance of the ETL process.

  • For example, a financial institution might have information on a customer in several departments and each department might have that customer's information listed in a different way. The membership department might list the customer by name, whereas the accounting department might list the customer by number. ETL can bundle all of these data elements and consolidate them into a uniform presentation, such as for storing in a database or data warehouse.
  • Another way that companies use ETL is to move information to another application permanently. For instance, the new application might use another database vendor and most likely a very different database schema. ETL can be used to transform the data into a format suitable for the new application to use.
  • An example would be an Expense and Cost Recovery System (ECRS) such as used by accountancies, consultancies, and legal firms. The data usually ends up in the time and billing system, although some businesses may also utilize the raw data for employee productivity reports to Human Resources (personnel dept.) or equipment usage reports to Facilities Management.

Real-life ETL cycle[edit]

The typical real-life ETL cycle consists of the following execution steps:

  1. Cycle initiation
  2. Build reference data
  3. Extract (from sources)
  4. Validate
  5. Transform (clean, apply business rules, check for data integrity, create aggregates or disaggregates)
  6. Stage (load into staging tables, if used)
  7. Audit reports (for example, on compliance with business rules. Also, in case of failure, helps to diagnose/repair)
  8. Publish (to target tables)
  9. Archive


ETL processes can involve considerable complexity, and significant operational problems can occur with improperly designed ETL systems.

The range of data values or data quality in an operational system may exceed the expectations of designers at the time validation and transformation rules are specified. Data profiling of a source during data analysis can identify the data conditions that must be managed by transform rules specifications, leading to an amendment of validation rules explicitly and implicitly implemented in the ETL process.

Data warehouses are typically assembled from a variety of data sources with different formats and purposes. As such, ETL is a key process to bring all the data together in a standard, homogeneous environment.

Design analysis[7] should establish the scalability of an ETL system across the lifetime of its usage — including understanding the volumes of data that must be processed within service level agreements. The time available to extract from source systems may change, which may mean the same amount of data may have to be processed in less time. Some ETL systems have to scale to process terabytes of data to update data warehouses with tens of terabytes of data. Increasing volumes of data may require designs that can scale from daily batch to multiple-day micro batch to integration with message queues or real-time change-data-capture for continuous transformation and update.


ETL vendors benchmark their record-systems at multiple TB (terabytes) per hour (or ~1 GB per second) using powerful servers with multiple CPUs, multiple hard drives, multiple gigabit-network connections, and much memory.

In real life, the slowest part of an ETL process usually occurs in the database load phase. Databases may perform slowly because they have to take care of concurrency, integrity maintenance, and indices. Thus, for better performance, it may make sense to employ:

  • Direct path extract method or bulk unload whenever is possible (instead of querying the database) to reduce the load on source system while getting high-speed extract
  • Most of the transformation processing outside of the database
  • Bulk load operations whenever possible

Still, even using bulk operations, database access is usually the bottleneck in the ETL process. Some common methods used to increase performance are:

  • Partition tables (and indices): try to keep partitions similar in size (watch for values that can skew the partitioning)
  • Do all validation in the ETL layer before the load: disable integrity checking ( ...) in the target database tables during the load
  • Disable triggers ( ...) in the target database tables during the load: simulate their effect as a separate step
  • Generate IDs in the ETL layer (not in the database)
  • Drop the indices (on a table or partition) before the load - and recreate them after the load (SQL: ... ...)
  • Use parallel bulk load when possible — works well when the table is partitioned or there are no indices (Note: attempting to do parallel loads into the same table (partition) usually causes locks — if not on the data rows, then on indices)
  • If a requirement exists to do insertions, updates, or deletions, find out which rows should be processed in which way in the ETL layer, and then process these three operations in the database separately; you often can do bulk load for inserts, but updates and deletes commonly go through an API (using SQL)

Whether to do certain operations in the database or outside may involve a trade-off. For example, removing duplicates using may be slow in the database; thus, it makes sense to do it outside. On the other side, if using significantly (x100) decreases the number of rows to be extracted, then it makes sense to remove duplications as early as possible in the database before unloading data.

A common source of problems in ETL is a big number of dependencies among ETL jobs. For example, job "B" cannot start while job "A" is not finished. One can usually achieve better performance by visualizing all processes on a graph, and trying to reduce the graph making maximum use of parallelism, and making "chains" of consecutive processing as short as possible. Again, partitioning of big tables and their indices can really help.

Another common issue occurs when the data are spread among several databases, and processing is done in those databases sequentially. Sometimes database replication may be involved as a method of copying data between databases — it can significantly slow down the whole process. The common solution is to reduce the processing graph to only three layers:

  • Sources
  • Central ETL layer
  • Targets

This approach allows processing to take maximum advantage of parallelism. For example, if you need to load data into two databases, you can run the loads in parallel (instead of loading into the first — and then replicating into the second).

Sometimes processing must take place sequentially. For example, dimensional (reference) data are needed before one can get and validate the rows for main "fact" tables.

Parallel processing[edit]

A recent[update] development in ETL software is the implementation of parallel processing. It has enabled a number of methods to improve overall performance of ETL when dealing with large volumes of data.

ETL applications implement three main types of parallelism:

  • Data: By splitting a single sequential file into smaller data files to provide parallel access
  • Pipeline: allowing the simultaneous running of several components on the same data stream, e.g. looking up a value on record 1 at the same time as adding two fields on record 2
  • Component: The simultaneous running of multiple processes on different data streams in the same job, e.g. sorting one input file while removing duplicates on another file

All three types of parallelism usually operate combined in a single job or task.

An additional difficulty comes with making sure that the data being uploaded is relatively consistent. Because multiple source databases may have different update cycles (some may be updated every few minutes, while others may take days or weeks), an ETL system may be required to hold back certain data until all sources are synchronized. Likewise, where a warehouse may have to be reconciled to the contents in a source system or with the general ledger, establishing synchronization and reconciliation points becomes necessary.

Rerunnability, recoverability[edit]

Data warehousing procedures usually subdivide a big ETL process into smaller pieces running sequentially or in parallel. To keep track of data flows, it makes sense to tag each data row with "row_id", and tag each piece of the process with "run_id". In case of a failure, having these IDs help to roll back and rerun the failed piece.

Best practice also calls for checkpoints, which are states when certain phases of the process are completed. Once at a checkpoint, it is a good idea to write everything to disk, clean out some temporary files, log the state, etc.

Virtual ETL[edit]

As of 2010[update], data virtualization had begun to advance ETL processing. The application of data virtualization to ETL allowed solving the most common ETL tasks of data migration and application integration for multiple dispersed data sources. Virtual ETL operates with the abstracted representation of the objects or entities gathered from the variety of relational, semi-structured, and unstructured data sources. ETL tools can leverage object-oriented modeling and work with entities' representations persistently stored in a centrally located hub-and-spoke architecture. Such a collection that contains representations of the entities or objects gathered from the data sources for ETL processing is called a metadata repository and it can reside in memory[8] or be made persistent. By using a persistent metadata repository, ETL tools can transition from one-time projects to persistent middleware, performing data harmonization and data profiling consistently and in near-real time.[9]

Dealing with keys[edit]

Unique keys play an important part in all relational databases, as they tie everything together. A unique key is a column that identifies a given entity, whereas a foreign key is a column in another table that refers to a primary key. Keys can comprise several columns, in which case they are composite keys. In many cases, the primary key is an auto-generated integer that has no meaning for the business entity being represented, but solely exists for the purpose of the relational database - commonly referred to as a surrogate key.

As there is usually more than one data source getting loaded into the warehouse, the keys are an important concern to be addressed. For example: customers might be represented in several data sources, with their Social Security Number as the primary key in one source, their phone number in another, and a surrogate in the third. Yet a data warehouse may require the consolidation of all the customer information into one dimension.

A recommended way to deal with the concern involves adding a warehouse surrogate key, which is used as a foreign key from the fact table.[10]

Usually, updates occur to a dimension's source data, which obviously must be reflected in the data warehouse.

If the primary key of the source data is required for reporting, the dimension already contains that piece of information for each row. If the source data uses a surrogate key, the warehouse must keep track of it even though it is never used in queries or reports; it is done by creating a lookup table that contains the warehouse surrogate key and the originating key.[11] This way, the dimension is not polluted with surrogates from various source systems, while the ability to update is preserved.

The lookup table is used in different ways depending on the nature of the source data. There are 5 types to consider;[11] three are included here:

Type 1
The dimension row is simply updated to match the current state of the source system; the warehouse does not capture history; the lookup table is used to identify the dimension row to update or overwrite
Type 2
A new dimension row is added with the new state of the source system; a new surrogate key is assigned; source key is no longer unique in the lookup table
Fully logged
A new dimension row is added with the new state of the source system, while the previous dimension row is updated to reflect it is no longer active and time of deactivation.


An established ETL framework may improve connectivity and scalability.[citation needed] A good ETL tool must be able to communicate with the many different relational databases and read the various file formats used throughout an organization. ETL tools have started to migrate into Enterprise Application Integration, or even Enterprise Service Bus, systems that now cover much more than just the extraction, transformation, and loading of data. Many ETL vendors now have data profiling, data quality, and metadata capabilities. A common use case for ETL tools include converting CSV files to formats readable by relational databases. A typical translation of millions of records is facilitated by ETL tools that enable users to input csv-like data feeds/files and import them into a database with as little code as possible.

ETL tools are typically used by a broad range of professionals — from students in computer science looking to quickly import large data sets to database architects in charge of company account management, ETL tools have become a convenient tool that can be relied on to get maximum performance. ETL tools in most cases contain a GUI that helps users conveniently transform data, using a visual data mapper, as opposed to writing large programs to parse files and modify data types.

While ETL tools have traditionally been for developers and IT staff, research firm Gartner wrote that the new trend is to provide these capabilities to business users so they can themselves create connections and data integrations when needed, rather than going to the IT staff.[12] Gartner refers to these non-technical users as Citizen Integrators.[13]

ETL vs. ELT[edit]

Extract, load, transform (ELT) is a variant of ETL where the extracted data is loaded into the target system first.[14] The architecture for the analytics pipeline shall also consider where to cleanse and enrich data[14] as well as how to conform dimensions.[4]

Cloud-based data warehouses like Amazon Redshift, Google BigQuery, and Snowflake Computing have been able to provide highly scalable computing power. This lets businesses forgo preload transformations and replicate raw data into their data warehouses, where it can transform them as needed using SQL.

After having used ELT, data may be processed further and stored in a data mart.[15]

There are pros and cons to each approach.[16] Most data integration tools skew towards ETL, while ELT is popular in database and data warehouse appliances. Similarly, it is possible to perform TEL (Transform, Extract, Load) where data is first transformed on a blockchain (as a way of recording changes to data, e.g., token burning) before extracting and loading into another data store.[17]

See also[edit]


  1. ^Denney, MJ (2016). "Validating the extract, transform, load process used to populate a large clinical research database". International Journal of Medical Informatics. 94: 271–4. doi:10.1016/j.ijmedinf.2016.07.009. PMC 5556907. PMID 27506144.
  2. ^Zhao, Shirley (2017-10-20). "What is ETL? (Extract, Transform, Load)

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