Break Down Silos and Unlock Trapped Data with ETL Extract-Transform-Load Data for Improved Decision-Making Summary

Data is essential for the day-to-day operations of an enterprise. However, to harness and derive value from it, it is important to break data silos and ETL helps accomplish that! It extracts information from disparate systems, transforms it into the required format, and loads it on to a destination for reporting and analysis.

Go through this eBook to get in-depth knowledge about the Extract-Transform-Load process. We’ll walk you through the basic concepts of ETL and the benefits of adopting this approach to optimize your data processes. Furthermore, we’ll give you a round-up of features that businesses should look for in an enterprise-grade, high-performance ETL tool.

ETL: Unlocking Data Silos for Improved Decision-Making Table of Contents

THE ETL TOOLKIT: GETTING STARTED WITH THE BASICS ...... 04 What is ETL? 05 ETL Process Implementation: The Three Steps 05 Challenges of ETL 07 2SWLPL]H%XVLQHVV3URFHVVHVZLWK(7/ 08

ETL VS. ELT: A COMPARISON...... 09 What is ELT? 10 ETL and ELT: Comparing Two Approaches 10 Key Takeaway 12

GOING DEEPER: UNDERSTANDING ETL PROCESSES...... 13 (7/DQG'DWDΖQWHJUDWLRQ:KDW$UHWKH'L΍HUHQFHV" 14 'L΍HUHQFHEHWZHHQ(7/3LSHOLQHVDQG'DWD3LSHOLQHV 14 )DFWRUV$΍HFWLQJ(7/3URFHVVHV 15

ETL USE CASES...... 17 Enterprise Applications of ETL 18

SELECTING AN ETL TOOL FOR YOUR ENTERPRISE TECHNOLOGY STACK ...... 20 ETL Tool: What to Look for? 21 Astera Centerprise: An Automated, Scalable ETL Tool 22

CONCLUSION ...... 2 The ETL Toolkit: Getting Started with the Basics

ETL: Unlocking Data Silos for Improved Decision-Making | 04 What is ETL? ETL (Extract, Transform and Load) is an integration process that extracts relevant information from raw data, converts it into a format that fulfils business requirements, and loads it into a target system.

The extraction, transformation, and loading processes work together to create an optimized ETL pipeline that allows for efficient migration, cleansing, and enrichment of critical business data.

ETL Process Implementation: The Three Steps When it comes to the implementation of the ETL process, the itinerary of tasks can be divvied into the full form of its acronym:

1. E – Extraction 2. T – Transformation 3. L – Loading

Here’s how this process converts raw data into intelligible insights.

ETL: Unlocking Data Silos for Improved Decision-Making | 05 Step 1: Extraction The primary step involves pulling data from the relevant sources and compiling it. These sources may include on-premise , CRM systems, marketing automation platforms, unstructured and structured files, cloud applications, or any other source system that stores enterprise data.

Once all the critical information has been extracted, it will be available in varying structures and formats. This information will have to be organized in terms of date, size, and source to suit the transformation process. There is a certain level of consistency required in all the data so it can be fed into the system and converted in the next step. The complexity of this step can vary significantly, depending on data types, the volume of data, and data sources. Extraction Steps

• Unearth data from relevant sources • Organize data to make it consistent Step 2: Transformation is the second step of the ETL process. Here the compiled data is converted, reformatted, and cleansed in the staging area to be fed into the target in the next step. The transformation step involves executing a series of functions and applying sets of rules to the extracted data, to convert it into a standard format to meet the schema requirements of the target database.

The level of manipulation required in transformation depends solely on the data extracted and the business requirements. It includes everything from applying expressions to rules. Transformation Steps

• Convert data according to the business requirements • Reformat converted data to a standard format for compatibility • Cleanse irrelevant data from the datasets o Sort & filter data o Remove duplications o Translate where necessary

ETL: Unlocking Data Silos for Improved Decision-Making | 06 Step 3: Loading

The last step includes loading the transformed datasets into the target database. There are two ways to go about it: first is a nSQL insert routine that involves the manual insertion of each record in every row of your target database table. Second approach uses a process called bulk loading, reserved for massive loading of data.

The SQL insert may be slow, but it conducts integrity checks with each entry. While the bulk load is suitable for large data volumes that are free of errors. Loading Step • Load well-transformed, clean datasets through bulk loading or SQL inserts Challenges of ETL

Implementing reliable ETL processes in today’s world of massive and complex amounts of data is no easy feat. Here are some of the challenges that may come up during ETL implementation:

Data volume: Today, data is growing exponentially in volume. And while some business systems need only incremental updates, others require a complete reload each time. ETL tools must scale for large amounts of both structured and unstructured (complex) data.

Data speed: Businesses today always need to be connected to enable real-time business insights and decisions and share the same information both externally and internally. As business intelligence analysis moves toward real-time, data warehouses and data marts need to be refreshed more often and more quickly. This requires real-time as well as batch processing.

Disparate sources: As information systems become more complex, the number of sources from which information must be extracted are growing. ETL software must have flexibility and connectivity to a wide range of systems, databases, files, and web services.

Diverse targets: Business intelligence systems and data warehouses, marts, and stores all have different structures that require a breadth of data transformation capabilities. Transformations involved in ETL processes can be highly complex. Data needs to be aggregated, parsed, computed, statistically processed, and more. Business intelligence-specific transformations are also required, such as slowly changing dimensions. Often data integration projects deal with multiple data sources and therefore need to handle issue of having multiple keys in order to make sense of the combined data.

ETL: Unlocking Data Silos for Improved Decision-Making | 07 Optimize Business Processes with ETL Improved BI and Reporting Poor data accessibility is a critical issue that can affect even the most well-designed reporting and analytics process. ETL tools make data readily available to the users who need it the most by simplifying the procedure of extraction, transformation, and loading. As a result of this enhanced accessibility, decision-makers can get their hands on more complete, accurate, and timely business intelligence (BI). ETL tools can also play a vital role in both predictive and prescriptive analytics processes, in which targeted records and datasets are used to drive future investments or planning. Higher ROI According to a report by International Data Corporation (IDC), implementing ETL data processing yielded a median five-year return on investment (ROI) of 112 percent with an average payback of 1.6 years. Around 54 percent of the businesses surveyed in this report had an ROI of 101 percent or more.

If done right, ETL implementation can save businesses significant costs and generate higher revenue. Improved Performance An ETL process can streamline the development of any high-volume data architecture. Today, numerous ETL tools are equipped with performance optimization technologies.

Many of the leading solutions providers in this space augment their ETL technologies with high-performance caching and indexing functionalities, and SQL hint optimizers. They are also built to support multi-processor and multi-core hardware and thus increase throughput during ETL jobs.

ETL: Unlocking Data Silos for Improved Decision-Making | 08 ETL vs. ELT: A Comparison

ETL: Unlocking Data Silos for Improved Decision-Making | 09 What is ELT? ELT is an acronym for Extract, Load, and Transform. It’s a process that transfers raw data from the source to target system, and the information is then transformed within the subsequent system for downstream applications.

This makes ELT most beneficial for handling enormous datasets, used for business intelligence and data analytics.

EXTRACT LOAD TRANSFORM

DATA WAREHOUSE SERVER

DATA WAREHOUSE PUSHDOWN JOB

ETL and ELT: Comparing Two Data Integration Approaches

Whether you should use ETL or ELT for a use-case depends primarily on three things: the fundamental storage technology, your use case, and data architecture.

To help you choose between the two, let’s discuss the advantages and drawbacks of each, one by one:

ETL: Unlocking Data Silos for Improved Decision-Making | 10 Advantages of ETL • ETL can balance the capacity and share the amount of work with the relational database management system (RDBMS).

• It can execute intricate operations in a single data flow diagram by of data maps.

• It can handle segregating and parallelism irrespective of the database design and source data model infrastructure.

• It can process data while it’s being transmitted from source to target (in stream) or even in batches.

• You can preserve current data source platforms without worrying about data synchronization as ETL doesn’t necessitate co-location of data sets.

• It extracts huge amounts of metadata and can run on SMP or MPP hardware that can be managed and used more efficiently, without performance conflict with the database.

• In the ETL process, the information is processed one row at a time. So, it performs well with data integration into 3rd party systems.

• Owing to parallel processing, it offers remarkable performance and scalability. Drawbacks of ETL • ETL requires extra hardware outlay, unless you run it on the database server.

• Due to the row-based approach, there’s a possibility of reduced performance in ETL.

• You’ll need expert skills and experience for implementing a proprietary ETL tool.

• There’s a possibility of reduced flexibility because of dependence on the ETL tool vendor.

• There’s no programmed error control or retrieval mechanism in traditional ETL processes. Advantages of ELT

• For better scalability, the ELT process uses an RDBMS engine.

• It offers better performance and safety as it operates with high-end data devices.

• ELT needs lesser time and resources as compared to ETL because the data is transformed and loaded in parallel.

• ELT process doesn’t need a discrete transformation block as this work is performed by the target system itself.

• Given that source and target data are in the same database, ELT retains all data in the RDBMS permanently. Drawbacks of ELT • There are limited tools available that offer complete support for ELT processes.

• In case of ELT, there’s a loss of comprehensive run-time monitoring and information.

• There’s also a lack of modularity because of set-based design for optimal performance and the lack of functionality and flexibility resulting from it.

ETL: Unlocking Data Silos for Improved Decision-Making | 11 As compared to the ETL process, ELT considerably reduces the load times. It’s a more resource-efficient process as it leverages the processing capability developed into a database, decreasing the time spent in data transfer. Key Takeaway ETL and ELT are the two different methods that are used to fulfil the same requirement, i.e. processing data so that it can be analyzed and used for superior business decision making.

Both these approaches vary enormously in terms of architecture and execution, and the whole thing depends on ‘T’ - transfor- mation. The key factor that differentiates the two is when and where the transformation step is executed.

Implementing an ELT process is more intricate as compared to ETL, however, it is now being favored. The design and execution of ELT may necessitate some more exertions, but it offers more benefits in the long run. Overall, ELT is an economical process as it requires fewer resources and takes a smaller amount of time to analyze large data volumes with less upkeep.

However, if the target system is not robust enough for ELT, ETL might be a more suited choice.

Comparison Parameters ETL ELT

ETL is a well-developed process used for over 20 years, ELT is a new technology, so it can be difficult to find Ease of adoption to the tool and ETL experts are easily available. experts and develop an ELT pipeline

Data size ETL is better suited for dealing with smaller data sets ELT is better suited when dealing with massive that require complex transformations. amounts of structured and unstructured data.

Data transformations happens after extraction in the Data is extracted, loaded into the target system, Order of the process staging area. After transformation, the data is loaded and then transformed. into the destination system.

Transformation process The staging area is located on the ETL solution's server. The staging area is located on the source or target database.

ETL load times are longer than ELT because it's a multi- Data loading happens faster because there's no Load time stage process: (1) data loads into the staging area, (2) waiting for transformations and the data only loads transformations take place, (3) data loads into the data one time into the target data system. warehouse.

ETL: Unlocking Data Silos for Improved Decision-Making | 1212 Going Deeper: Understanding ETL Processes

ETL: Unlocking Data Silos for Improved Decision-Making | 13 ETL and Data Integration: What Are the Differences? People often confuse ETL with data integration, while these processes are complementary, they differ significantly in execution. Data integration is the process of fusing data from several sources to offer a cohesive view of data. On the contrary, ETL involves the actual retrieval of data from those disparate locations, its subsequent cleansing, and transformation, and finally the loading of these enhanced datasets into a storage, reporting, or analytics structure.

Essentially, data integration is a downstream process that takes enriched data and turns it into relevant and useful information. Today, data integration combines numerous processes, such as ETL, ELT, and data federation.. Whereas, data federation combines data from multiple sources in a virtual database and is generally used for BI.

By contrast, ETL encompasses a relatively narrow set of operations that are performed before storing data in the target system. Difference between ETL Pipelines and Data Pipelines

Although ETL and data pipelines are related, they are quite different from one another. However, people often use the two terms interchangeably. Data and ETL pipelines are both move data from one system to another; the key difference is in the application for which the pipeline is designed.

ETL pipeline basically includes a series of processes that extract data from a source, transform it, and then load it into some output destination.

On the other hand, a data pipeline is a somewhat broader terminology, which includes ETL pipeline as a subset. It includes a set of processing tools that transfers data from one system to another, however, the data may or may not be trans- formed.

The purpose of a data pipeline is to transfer data from disparate sources, such as business processes, event tracking systems, and databanks, into a data warehouse for business intelligence and analytics. Whereas, ETL pipeline is a kind of data pipeline in which data is extracted, transformed, and then loaded into a target system. The sequence is critical; after from the source, you must fit it into a data model that’s generated as per your business intelligence requirements by accumulating, cleaning, and then transforming the data. Ultimately, the resulting data is then loaded into a data warehouse or database.

Another difference between the two is that an ETL pipeline typically works in batches which means that the data is moved in one big chunk at a particular time to the destination system. For example, the pipeline can be run once every twelve hours. You can even organize the batches to run at a specific time daily when there’s low system traffic.

ETL: Unlocking Data Silos for Improved Decision-Making | 14 Contrarily, a data pipeline can also be run as a real-time process instead of in batches. During data streaming, it is handled as an incessant flow which is suitable for data that requires continuous updating. For example, to transfer data collected from a sensor tracking traffic.

Moreover, a data pipeline doesn’t have to end in the loading of data to a databank or a data warehouse. And, it is possible to load data to any number of destination systems, for instance an SQL Server or a delimited file. Factors Affecting ETL Processes

Below are some of the determinants that affect ETL: Difference Between Source and Destination Data Arrangements The disparity between the source and target data has a direct impact on the complexity of the ETL system. Because of this difference in data structures, the loading process normally has to deconstruct the records, alter and validate values, and replace code values. Data Quality If the data has poor quality, such as missing values, incorrect code values, or reliability problems, it can affect the ELT process as it’s useless to load poor quality data into a reporting and analytics structure or a target system. For instance, if you intend to use your data warehouse or an operational system to gather marketing intelligence for your sales team and your current marketing databases contain error-ridden data, then your organization may need to dedicate a significant amount of time to validate things like emails, phone numbers, and company details. System Crash Incomplete loads can become a potential concern if source systems fail while the ETL process is being executed. As a result, you can either cold-start or warm-start the ETL job, depending on the specifics of the destination system.

Cold-start restarts an ETL process from scratch, while a warm-start is employed to resume the operation from the last identified records that were loaded successfully.

ETL: Unlocking Data Silos for Improved Decision-Making | 15 Organization’s Approach Towards Technology

The unfamiliarity of the stakeholders with data warehouse design or zero technical knowledge may motivate them to stick with manual coding for implementation of ETL processes. Thus, the management should be willing to explore the latest technology to generate maximum benefits out of it. Internal Proficiency Another factor that governs the implementation of ETL process is the proficiency of the organization’s in-house team. While the IT team may be familiar with coding for specific databases, they may be less capable of developing extraction processes for cloud-based storage systems. It should also be noted that ETL is a continuing process that requires consistent maintenance and optimization as more sources, records, and destinations are added into an organization’s data environment. Data Volume, Loading Frequency, and Disk Space A large data volume tends to shrink the batch window as jobs will take longer to run, and there will be less time between each one. The volume and frequency of data extraction and loading can also impact the performance of source and target systems.

In terms of the former, the strain of processing day-to-day transactional queries as well as ETL processes may cause systems to lock up. While target structures may lack the necessary storage space to handle rapidly expanding data loads. The creation of staging areas and temporary files can also consume a lot of disk space in the intermediary server.

Data Volume

Data Quality

Loading Internal Frequency Proficiency Disk Space Source and Destination Data Arrangements

System Crash

Organization’s Approach Towards Technology

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ETL: Unlocking Data Silos for Improved Decision-Making | 17 Enterprise Applications of ETL The use of ETL is broadening across the globe beyond simple data movements. Here are a few common use-cases of ETL: Data migration is the process of transferring data between databases, data formats, or enterprise applications. There are various reasons why an organization may decide to migrate data to a new environment, such as to replace legacy applications with modern tools, switch to high-end servers, or consolidate data post-merger or acquisition. Regardless of the underlying reason, ETL remains a proven method that many organizations rely on to respond to data migration needs. Using ETL tools, businesses can surface data from different repositories as well as consolidate data from external and internal sources to offer business users a unified, well-rounded view of all business operations. Data Warehousing Data warehousing is a complex process as it involves integrating, rearranging, and consolidating massive volumes of data captured within disparate systems to provide a unified source of BI and insights. In addition, data warehouses must be updated regularly to fuel BI processes with fresh data and insights.

ETL is a key process used to load disparate data in a homogenized format into a data repository. Besides, with incremental loads, ETL also enables near real-time data warehousing, thereby providing business users and decision makers fresh data for reporting and analysis. Data Quality

From erroneous data received from online forms to lack of integration between data sources and ambiguous nature of data itself, there are several factors that impact the quality of incoming data streams, thereby diminishing the value businesses can extract from its data assets. ETL is a key data management process that helps enterprises ensure that only clean and consistent data makes it to their data repository and BI tools. Here are some of the ways businesses can use ETL to enhance data quality:

•Data profiling and standardization • Duplication • Data enhancement •Data cleansing and verification

ETL: Unlocking Data Silos for Improved Decision-Making | 18 Application Integration

To get a better view of enterprise information assets, it is critical to integrate data stored in disparate applications, such as Salesforce.com and MS Dynamics. An end-to-end ETL process helps integrate data from cloud applications, massage the data for ensuring data quality, and load it into a target destination, such as a data warehouse or database.

ETL: Unlocking Data Silos for Improved Decision-Making | 19 Selecting an ETL Tool for Your Enterprise Technology Stack

ETL: Unlocking Data Silos for Improved Decision-Making | 20 ETL Tool: What to Look for?

Although a fairly simple process to understand, ETL can grow in complexity as the volume, variety, and veracity of source data increases. The following factors can impact the scope and complexity of an ETL task:

• The number and variety of data sources and destinations involved • The number of tables created • The type of transformations required. This may range from simple look-ups to more complex transformations, such as flattening the hierarchy of an XML, JSON, or COBOL file or normalizing data

To successfully address these challenges and use ETL to create a comprehensive, accurate view of enterprise data, businesses need high-performance ETL tools. Ideally ones that offer native connectivity to all the required data sources, capabilities to handle structured, semi-structured, and unstructured data, and built-in job scheduling and workflow automation features to save the developer resources and time spent on managing data.

Here is a round-up of features businesses should look for in an enterprise-ready, high-performance ETL tool:

Library of Connectors – A well-built ETL tool should offer native connectivity to a range of structured and unstructured, modern and legacy, and on-premise and cloud data sources. This is important because one of the core jobs of an ETL tool is to enable bi-directional movement of data between the vast variety of internal and external data sources that an enterprise utilizes.

Ease of Use – Managing custom-coded ETL mappings is a complex process that requires development expertise. To save devel- oper resources and transfer data from the hands of developers to business users, you need an ETL solution that offers an intuitive, code-free environment to extract, transform, and load data.

Data Transformations – To cater to the data manipulation needs of a business, the ETL tool should offer a range of both simple and advanced built-in transformations.

Data Quality, Profiling, and Cleansing – Data is of no use unless it is validated before being loaded into a data repository. To ensure this, look for an ETL solution that offers data quality, profiling, and cleansing capabilities to determine the consistency, accuracy, and completeness of the enterprise data.

Automation – Large enterprises handle hundreds of ETL jobs daily. Automating these tasks will make the process of extracting insights faster and easier. Therefore, look for an ETL solution with job scheduling, process orchestration, and automation capabilities.

While these are a few important features a good ETL tool must have, the right selection of ETL software will depend on the specific requirements of your organization.

ETL: Unlocking Data Silos for Improved Decision-Making | 21 Astera Centerprise: An Automated, Scalable ETL Tool Astera Centerprise is an enterprise-grade ETL solution that integrates data across multiple systems, enables data manipu- lation with a comprehensive set of built-in transformations, and helps move data to a centralized repository, all in a completely code-free, drag-and-drop manner. The ETL tool utilizes a high-performance cluster-based architecture, industrial-strength ETL engine, and advanced automation capabilities to simplify and streamline complex ETL processes.

With support for pushdown optimization, incremental data load, and connectivity to legacy and modern data sources, Astera Centerprise helps businesses integrate data of any format, size, or complexity with minimal IT support. What Makes Astera Centerprise a High Performance ETL Tool?

Astera Centerprise eases the ETL process in numerous ways:

Drag-and-Drop, Code-Free Mapping Environment: The solution features a visual, drag-and-drop UI that provides advance-level functionality for development debugging, and testing in a code-free environment.

REST Server Architecture: Astera Centerprise is based on a client-server architecture, with a REST-enabled server and lightweight, lean client application. The major part of processing and querying is handled by the server component, which communicates with the client using HTTPS commands.

ETL: Unlocking Data Silos for Improved Decision-Making | 22 Workflow Automation and Job Scheduling: With a built-in job scheduler, Astera Centerprise allows you to schedule anything from a simple data ETL job to a complex workflow comprising of several subflows.

Industrial-Strength, Parallel Processing Engine: Featuring a cluster-based architecture and a parallel processing ETL engine, Astera Centerprise allows multiple data transformation jobs to be run in parallel.

A Vast Selection of Connectors: The software has a vast collection of built-in connectors for both modern and traditional data sources, including databases, file formats, REST APIs, and more.

Instant Data Preview: With Instant Data Preview, Astera Centerprise provides you an insight into the validity of the data mappings you have created in real-time. It allows you to inspect a sample of the data being processed at each step of the transformation process.

REST Server Job Architecture Optimizer

Industrial- Strength, Parallel Security and Processing Engine Access Control

Workflow Pushdown Automation and Optimization Job Scheduling A STERA enterprise®

Vast Selection of Connectors Drag-and-Drop, Code-Free Mapping Environment

Data Instant Preview

Extensive Library of Pre-Built Transformations

ETL: Unlocking Data Silos for Improved Decision-Making | 23 Extensive Library of Pre-Built Transformations: Astera Centerprise dramatically simplifies the process of transforming complex data with its visual, drag-and-drop environment and broad selection of built-in transformations.

Data Validation: Using the built-in data quality, cleansing, and profiling features in Astera Centerprise, you can easily examine your source data and get detailed information about its structure, quality, and integrity

Pushdown Optimization for Maximum Performance: With Astera Centerprise, a data transformation job can be pushed down into a relational database, where appropriate, to make optimal use of database resources and improve performance.

SmartMatch Functionality: This feature provides an intuitive and scalable method of resolving naming conflicts and inconsistencies that arise during high-volume data integrations. It allows users to create a Synonym Dictionary File that contains alternative values appearing in the header field of an input table. Centerprise then automatically matches irregular headers to the correct column at run-time and extracts data from them as normal.

Security and Access Control: The solution also includes authorization and authentication features to secure your data process from unauthorized users.

Job Optimizer: Job Optimizer is another significant feature that modifies the dataflow at runtime to optimize performance and reduce job execution time.

ETL: Unlocking Data Silos for Improved Decision-Making | 24 Conclusion

In today’s world, where data keeps growing by the minute, collecting and storing terabytes of data is useless unless it can be leveraged in a meaningful way. ETL makes it possible for businesses to access meaning information from fragmented data, DQGXVHLWWRRSWLPL]HEXVLQHVVSURFHVVDQGJHWLQGHSWKLQVLJKWV

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ETL: Unlocking Data Silos for Improved Decision-Making | 2