Best Data Extraction Software – 2021 Reviews & Pricing
For small businesses, data is a highly critical factor in determining customer needs, building sales and marketing strategies as well as understanding market trends.
Luckily for your small business, data is ubiquitous in the form of emails, program code, documentation, configuration files, websites etc. All of these can help you understand consumer habits and drive revenue. This data will also give you a competitive edge in the market.
For this reason, you should find ways to connect with your customers. However, small businesses often find it challenging to correctly identify customer behavior—how they select, buy and use your products.
Data extraction software can help you understand these customer actions. The software automates the collection of data from various websites and sources. It makes it easy to organize, store, retrieve and use this information to research and analyze customers.
But finding the right data extraction software can be tough for small businesses like yours. Knowing which features you need and fully realizing the benefits of those features will help you purchase the right software for your business.
This guide will help you understand data extraction software, its features and benefits.
Here’s what we’ll cover:
What Is Data Extraction Software? Common Features of Data Extraction Software What Type of Buyer Are You? Benefits of Data Extraction SoftwareKey Considerations
What is Data Extraction Software?
Data extraction tools help businesses scrape data from a website or server. The data could be in the form of images, URLs, email addresses, phone numbers, etc.
The software can help you acquire data regarding the market, your customers and the general state of the economy every day, week or month. It can extract a variety of data, ranging from financial data (such as stock prices and bonds) to contact information (such as email IDs, phone numbers and social media profiles).
The data extraction process involves the following steps:
Load the data from the source page
Transform the source page for the extraction process
Identify the appearing elements (images, email IDs, etc. )
Filter these elements
Export of the final data to an output format (Excel, Word, etc. )
Schedule extraction feature in Octoparse (Source)
Common Features of Data Extraction Software
In this section, we cover the key software features that a buyer should be aware of before they purchase a solution. Most small businesses will need some (or all) of these features in their data extraction software:
Email address extraction
Collect email addresses from web pages, data files or any email account.
Web data extraction
Collect content structures in the form of product catalogs, search results, URLs, etc., from various websites and store it in the company database.
Set intervals (once a day, month or quarter) to scrape the most recent data whenever the tool detects updates or new content.
IP address extraction
Extract IP addresses from files, folders, URLs and text snippets.
Extract images of all sizes and types, including pictures, graphics and photos, from any kind of text file.
Phone number extraction
Extract phone numbers from web pages and text files using an inbuilt logic that filters out the required information using a comma, colon or another character based per your preference.
Import data from tables and lists from websites, then export these into different formats such as Microsoft Excel or Word.
Organize collected data and store it on a server or in the cloud.
To further understand these features and the vendors in this category, call our advisors at (844) 687-6771 for free, no-obligation guidance. They’ll help you narrow down your options by understanding your requirements and recommending the best-suited solutions for your business.
What Type of Buyer Are You?
As you begin shortlisting your options for data extraction software, you need to understand the type of buyer you are. This will help you better analyze your requirements and the priority of software features into “must-have” and “optional. ”
This section breaks down the most common buyer types. Here are the three main types of buyers in this category:
E-commerce companies: These buyers need to study visitor demographics to deliver engaging customer experiences. They need data on the maximum viewed product categories, products delivering most sales, etc. Based on this data, they need to develop a strategy for customizing their offerings and promotions.
Government agencies: This buyer type needs data extraction software to control economic and infrastructural changes in their region. For instance, a district government body can analyze traffic data of a certain area with a high volume of road traffic. This could help them build better infrastructure models to ease the traffic situation in nearby areas as well.
Service providers: They require data extraction tools to improve their service offerings. Cable and internet service providers extract customer data to analyze their customers’ needs and develop strategies to create the most effective up-sell opportunities.
Benefits of Data Extraction Software
So far, we’ve discussed that data extraction tools benefit businesses by automating the process of extracting data and reducing the overall scraping time. Here are some more benefits of using data extraction tools in your small business:
Extracts organic search results data for competitor analysis. The tool can pull data, such as title tags, meta keywords tags and backlinks, from competitor websites. The data allows you to do a competitor analysis of keywords that are driving traffic to a website, content categories that are attracting links and user engagement as well as the kind of resources you need to rank your site.
Enhances lead generation. A HubSpot survey found that “generating traffic and leads” was the top marketing challenge for 63 percent of marketers in 2018. Data extraction tools can enhance this process by extracting primary data (email IDs, contact information, etc. ) based on your chosen criteria.
Now that you’re aware of the features and benefits of data extraction software, you should be better equipped to explore the solutions in the market. But before you purchase a solution, consider these key factors to make the right decisions:
Increasing data demands require scalability. Your data requirements will increase over time, so the solution should be able to handle future business expansion. A desktop as a service (DaaS) solution is ideal for small businesses and startups. It lets you scale up without having to invest a lot on hardware. DaaS also allows you to quickly make updates and upgrades at a relatively low cost than a traditional workstation infrastructure.
Mass data extraction requires a robust engine. The engine used for the data extraction process should be capable of managing the entire process: sorting, filtering and making advanced extraction algorithm. It should also be able to accommodate HTML structure changes, build a proper workflow for the process, log and track any failures as well as be resilient to changes and updates.
Data interface is essential. A graphical user interface (GUI) is essential to extracting data from visual sources such as websites. GUI lets you separate editing from viewing and gives a high degree of ease when configuring and extracting the data. If your tools lack GUI, it’ll be difficult to create a direct relationship between the content you see and the HTML code or configuration files.
Keep these factors in mind when you are searching for a data extraction tool. Once you have fully understood your end-to-end requirements, shortlisting vendors will be easy.
Tag Archives: Financial Data Extraction – Sage X3 – Greytrix
The Purpose of the Financial Data Extraction is representing Financial Data of an Organization/Company in form of Financial Reporting to their concerned stakeholders. This will help to evaluate the Company’s performance over specific period (Half-yearly/Yearly), eventually helping the management to take effective decisions based on the financial reports generated. Financial reports, Balance Sheet and Profit… Read More »
Financial reporting involves the revelation of financial data to company stakeholders, this also helps people to understand the company’s performance over a specific period of rmally the Financial reports are extracted Quarterly or Annually basis. The primary purpose of Financial Reports can be segregated in 2 points: Since the FR’s provide a picture of the financial… Read More »
As continued from our earlier blog now we are already acquainted with a configuration of the FDE setup/ Calculation part. Now we are remaining with only the last part to View the data in FDE. Let’s discuss the same in this blog: FDE Inquiry Function Path: Financials > Reporting > Financial Data Extraction > Inquiry This option makes… Read More »
In our earlier blog we have got an overview about an essential feature “Financial Data Extraction (FDE)”, now we would proceed with the configuration of FDE in Sage ERP X3 in bit elaborate way. FDE Set-up: Function Path: Parameters > Financials > Accounting forms > Financial data extraction This function is used to carry out financial… Read More »
Sage ERP X3 provides a powerful, financial-oriented data extraction facility for all inquiry and reporting needs. Used in conjunction with information, users can easily design and generate all company-specific operating statements, balance sheets, and other financial reports and inquiries. Sage ERP X3 provides a number of report-building tools, such as row and column content and… Read More »
Data extraction for ETL simplified | BryteFlow
What is Data Extraction?
Data extraction refers to the method by which organizations get data from databases or SaaS platforms in order to replicate it to a data warehouse or data lake for Reporting, Analytics or Machine Learning purposes. When data is extracted from various sources, it has to be cleaned, merged and transformed into a consumable format and stored in the data repository for querying. This process is the ETL process or Extract Transform Load.
Data Extraction refers to the ‘E’ of the Extract Transform Load process
Data extraction as the name suggests is the first step of the Extract Transform Load sequence. The process of data extraction involves retrieval of data from various data sources. The source of data, which is usually a database, or files, XMLs, JSON, API etc. is crawled through to retrieve relevant information in a specific pattern. Data ETL includes processing which involves adding metadata information and other data integration processes that are all part of the ETL workflow.
The purpose is to prepare and process the data further, migrate the data to a data repository or to further analyse it. In short, to make the most use of the data present. Learn more about BryteFlow for AWS ETL
Why is Data Extraction so important?
In order to achieve big data goals, data extraction becomes the most important step as everything else is going to be derived from the data that is retrieved from the source. Big data is used for everything and anything including decision making, sales trends forecasting, sourcing new customers, customer service enhancement, medical research, optimal cost cutting, Machine Learning, AI and more. If data extraction is not done properly, the data will be flawed. After all, only high quality data leads to high quality insights.
What to keep in mind when preparing for data extraction during data ETL
Impact on the source: Retrieving information from the source may impact the source system/database. The system may slow down and frustrate other users accessing it at the time. This should be thought of when planning for data extraction. The performance of the source system shouldn’t be compromised. You should opt for a data extraction approach that has minimal impact on the source.
Volume: Data extraction involves ingesting large volumes of data which the process should be able to handle efficiently. Analyze the source volume and plan accordingly. Data extraction of large volumes calls for a multi-threaded approach and might also need virtual grouping/partitioning of data into smaller chunks or slices for faster data ingestion.
Secrets of Bulk Loading Data Fast to Cloud Data Warehouses
Data completeness: For continually changing data sources, the extraction approach should cater to capture the changes in data effectively, be it directly from the source or via logs, API, date stamps, triggers etc.
Automated data extraction: let BryteFlow do the heavy hitting
BryteFlow can do all the thinking and planning to get your data extracted smartly for Data Warehouse ETL. Its ticks all the checkboxes above and is very effective in migrating data from any structured/semi-structured sources onto a Cloud DW or Data Lake. Build an S3 Data Lake in Minutes
Types of Data Extraction
Coming back to data extraction, there are two types of data extraction: Logical and Physical extraction.
The most commonly used data extraction method is Logical Extraction which is further classified into two categories:
In this method, data is completely extracted from the source system. The source data will be provided as is and no additional logical information is necessary on the source system. Since it is complete extraction, there is no need to track the source system for changes.
For e. g., exporting a complete table in the form of a flat file.
In incremental extraction, the changes in source data need to be tracked since the last successful extraction. Only these changes in data will be retrieved and loaded. There can be various ways to detect changes in the source system, maybe from the specific column in the source system that has the last changed timestamp. You can also create a change table in the source system, which keeps track of the changes in the source data. It can also be done via logs if the redo logs are available for the rdbms sources. Another method for tracking changes is by implementing triggers in the source database.
Physical extraction has two methods: Online and Offline extraction:
In this process, the extraction process directly connects to the source system and extracts the source data.
The data is not extracted directly from the source system but is staged explicitly outside the original source system. You can consider the following common structure in offline extraction:
Flat file: Is in a generic format
Dump file: Database specific file
Remote extraction from database transaction logs
There can be several ways to extract data offline, but the most efficient of them all is to do via remote data extraction from database transaction logs. Database archive logs can be shipped to a remote server where data is extracted. This has zero impact on the source system and is high performing. The extracted data is loaded into a destination that serves as a platform for AI, ML or BI reporting, such as a cloud data warehouse like Amazon Redshift, Azure SQL Data Warehouse or Snowflake. The load process needs to be specific to the destination.
Data extraction with Change Data Capture
Incremental extraction is best done with Change Data Capture or CDC. If you need to extract data regularly from a transactional database that has frequent changes, Change Data Capture is the way to go. With CDC, only the data that has changed since the last data extraction is loaded to the data warehouse not the full refresh which is extremely time-consuming and taxing on resources. Change Data Capture enables access of near real-time data or on-time data warehousing. Change Data Capture is inherently more efficient since a much smaller volume of data needs to be extracted. However mechanisms to identify the recently modified data may be challenging to put in place, that’s where a data extraction tool like BryteFlow can help. It provides automated CDC replication so there is no coding involved and data extraction and replication is super-fast even from traditional legacy databases like SAP and Oracle.
Automated Data Extraction with BryteFlow for Data Warehouse ETL
BryteFlow uses a remote extraction process with Change Data Capture and provides automated data replication with:
Zero impact on source
High performance: multi threaded configurable extraction and loading and provides the highest throughput in the market when compared with competitors
Zero coding: for extraction, merging, masking or type 2 history
Support for terabytes of data ingestion, both initial and incremental
Time series your data
Self-recovery from connection dropouts
Smart catch-up features in case of down-time
CDC with Transaction Log Replication
Automated Data Reconciliation to check for Data Completeness
Simplify data extraction and integration with an automated data extraction tool
BryteFlow integrates data from any API, any flat files and from legacy databases like SAP, Oracle, SQL Server, MySQL and delivers ready-to-use data to S3, Redshift, Snowflake, Azure Synapse and SQL Server at super-fast speeds. It is completely self-service, needs no coding, and low maintenance. It can handle huge petabytes of data easily with smart partitioning and parallel multi-thread loading.
BryteFlow is ideal for Data Warehouse ETL
BryteFlow Ingest uses an easy-to-use point and click interface to set up real-time database replication to your destination with high parallelism for the best performance. BryteFlow is secure. It is a cloud-based solution that specializes in securely extracting, transforming, and loading your data. As a part of the data warehouse ETL process, if you need to mask sensitive information or split columns on the fly, it can be done with simple configuration using BryteFlow. Learn how BryteFlow Data Replication Software works
Want to know more about easy real-time data extraction and replication? Get a free trial of BryteFlow
Frequently Asked Questions about financial data extraction software
What is data extraction software?
Data extraction software allows companies to retrieve structured, poorly structured, and unstructured data from a variety of sources for storage or processing. Data extraction tools can pull data off of forms, scrape information from websites, extract data from emails, and more.
What is financial data extraction?
The Purpose of the Financial Data Extraction is representing Financial Data of an Organization/Company in form of Financial Reporting to their concerned stakeholders.
What are the two types of data extraction?
Coming back to data extraction, there are two types of data extraction: Logical and Physical extraction.